wehub-resource-sync
121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op import Operator
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from op_test import get_device_place, is_custom_device
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from paddle.base import core
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class TestBeamSearchDecodeOp(unittest.TestCase):
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"""unittest of beam_search_decode_op"""
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def setUp(self):
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self.scope = core.Scope()
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self.place = core.CPUPlace()
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def append_lod_tensor(self, tensor_array, lod, data):
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lod_tensor = core.DenseTensor()
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lod_tensor.set_lod(lod)
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lod_tensor.set(data, self.place)
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tensor_array.append(lod_tensor)
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def test_get_set(self):
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ids = self.scope.var("ids").get_dense_tensor_array()
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scores = self.scope.var("scores").get_dense_tensor_array()
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# Construct sample data with 5 steps and 2 source sentences
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# beam_size = 2, end_id = 1
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# start with start_id
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[
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self.append_lod_tensor(
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array, [[0, 1, 2], [0, 1, 2]], np.array([0, 0], dtype=dtype)
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)
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for array, dtype in ((ids, "int64"), (scores, "float32"))
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]
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[
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self.append_lod_tensor(
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array,
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[[0, 1, 2], [0, 2, 4]],
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np.array([2, 3, 4, 5], dtype=dtype),
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)
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for array, dtype in ((ids, "int64"), (scores, "float32"))
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]
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[
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self.append_lod_tensor(
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array,
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[[0, 2, 4], [0, 2, 2, 4, 4]],
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np.array([3, 1, 5, 4], dtype=dtype),
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)
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for array, dtype in ((ids, "int64"), (scores, "float32"))
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]
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[
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self.append_lod_tensor(
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array,
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[[0, 2, 4], [0, 1, 2, 3, 4]],
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np.array([1, 1, 3, 5], dtype=dtype),
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)
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for array, dtype in ((ids, "int64"), (scores, "float32"))
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]
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[
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self.append_lod_tensor(
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array,
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[[0, 2, 4], [0, 0, 0, 2, 2]],
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np.array([5, 1], dtype=dtype),
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)
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for array, dtype in ((ids, "int64"), (scores, "float32"))
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]
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sentence_ids = self.scope.var("sentence_ids").get_tensor()
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sentence_scores = self.scope.var("sentence_scores").get_tensor()
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beam_search_decode_op = Operator(
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"beam_search_decode",
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# inputs
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Ids="ids",
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Scores="scores",
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# outputs
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SentenceIds="sentence_ids",
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SentenceScores="sentence_scores",
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beam_size=2,
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end_id=1,
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)
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beam_search_decode_op.run(self.scope, self.place)
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expected_lod = [[0, 2, 4], [0, 4, 7, 12, 17]]
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self.assertEqual(sentence_ids.lod(), expected_lod)
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self.assertEqual(sentence_scores.lod(), expected_lod)
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expected_data = np.array(
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[0, 2, 3, 1, 0, 2, 1, 0, 4, 5, 3, 5, 0, 4, 5, 3, 1], "int64"
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)
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np.testing.assert_array_equal(np.array(sentence_ids), expected_data)
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np.testing.assert_array_equal(np.array(sentence_scores), expected_data)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestBeamSearchDecodeOpGPU(TestBeamSearchDecodeOp):
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def setUp(self):
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self.scope = core.Scope()
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self.place = get_device_place()
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if __name__ == '__main__':
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unittest.main()
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