wehub-resource-sync
105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
# Copyright (c) 2020 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_test import get_places
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import paddle
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from paddle import base
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from paddle.tensor.manipulation import tensor_array_to_tensor
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paddle.enable_static()
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def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
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paddle.seed(1)
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np.random.seed(2)
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x = paddle.assign(
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np.random.rand(batch_size, beam_size, 2).astype("float32")
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)
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indices = paddle.static.data(
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shape=[None, beam_size], dtype="int64", name="indices"
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)
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step_idx = paddle.tensor.fill_constant(
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shape=[1], dtype="int64", value=0, force_cpu=True
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)
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max_len = paddle.tensor.fill_constant(
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shape=[1], dtype="int64", value=10, force_cpu=True
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)
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cond = paddle.less_than(x=step_idx, y=max_len)
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while_op = paddle.static.nn.control_flow.While(cond)
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scores = paddle.tensor.array_write(x, step_idx)
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with while_op.block():
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bs = paddle.cast(paddle.shape(x)[0], "int64")
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for _ in range(2):
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bs = paddle.cast(bs, 'int64')
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bs.stop_gradient = stop_gradient
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batch_pos = paddle.expand(
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paddle.unsqueeze(paddle.arange(0, bs, 1, dtype=bs.dtype), [1]),
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[-1, beam_size],
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)
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topk_coordinates = paddle.stack([batch_pos, indices], axis=2)
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topk_coordinates.stop_gradient = stop_gradient
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score = paddle.gather_nd(x, topk_coordinates)
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paddle.increment(x=step_idx, value=1.0)
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paddle.tensor.array_write(score, i=step_idx, array=scores)
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length_cond = paddle.less_than(x=step_idx, y=max_len)
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paddle.assign(length_cond, cond)
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scores.stop_gradient = True
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out = tensor_array_to_tensor(scores, axis=0, use_stack=True)[0]
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loss = paddle.mean(out)
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opt = paddle.optimizer.Adam(0.01)
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opt.minimize(loss)
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exe = base.Executor(place)
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data = np.random.random_integers(
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low=0, high=beam_size - 1, size=(batch_size, beam_size)
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).astype("int64")
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exe.run(paddle.static.default_startup_program())
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(loss_val,) = exe.run(feed={"indices": data}, fetch_list=[loss])
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return loss_val
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class TestDynRNNStopGradient(unittest.TestCase):
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def setUp(self):
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self.batch_size = 2
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self.beam_size = 2
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def run_main(self, place):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with (
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paddle.static.program_guard(main_program, startup_program),
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base.scope_guard(base.Scope()),
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):
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value1 = build_and_run_program(
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place, self.batch_size, self.beam_size, False
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)
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value2 = build_and_run_program(
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place, self.batch_size, self.beam_size, True
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)
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np.testing.assert_array_equal(value1, value2)
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def test_check_main(self):
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for p in get_places():
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self.run_main(p)
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if __name__ == '__main__':
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unittest.main()
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