chore: import upstream snapshot with attribution
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# Copyright (c) 2023 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 paddle
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from paddle.base import core
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from paddle.jit.dy2static import (
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pir_partial_program,
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program_translator,
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)
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from paddle.jit.dy2static.utils import Backend
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class TestPartiaProgramLayerHook(unittest.TestCase):
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def setUp(self):
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self._hook = pir_partial_program.PartialProgramLayerHook()
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def test_before_append_backward(self):
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self.assertEqual(
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self._hook.before_append_backward(None, None), (None, None)
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)
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def test_after_append_backward(self):
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self.assertEqual(
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self._hook.after_append_backward(None, None, None, None, 0, 0),
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(None, 0, None),
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)
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def test_after_infer(self):
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self.assertIsNone(self._hook.after_infer(None))
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class TestPrimHook(unittest.TestCase):
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def setUp(self):
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core._set_prim_all_enabled(True)
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def f():
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return paddle.nn.functional.dropout(paddle.rand((1,)))
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concrete_program, partial_program_layer = paddle.jit.to_static(
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f, full_graph=True
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).get_concrete_program()
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self._hook = program_translator.PirPrimHooker(
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concrete_program.main_program, Backend.PHI
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)
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self.partial_program_layer = partial_program_layer
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def tearDown(self):
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core._set_prim_all_enabled(False)
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def test_before_append_backward(self):
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program = self.partial_program_layer.program
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self._hook.before_append_backward(
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program.forward_program,
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program.out_values,
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)
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self.assertNotIn(
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'pd_op.dropout',
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tuple(
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op.name() for op in program.forward_program.global_block().ops
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),
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)
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def test_after_append_backward(self):
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program_ = self.partial_program_layer.train_program
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train_program = program_.program
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(
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program,
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forward_end_idx,
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targets,
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) = self._hook.after_append_backward(
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train_program, None, program_.out_values, None, 0, 0
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)
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self.assertNotIn(
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'pd_op.dropout_grad',
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tuple(op.name() for op in train_program.global_block().ops),
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)
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if __name__ == '__main__':
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unittest.main()
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