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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# Copyright (c) 2024 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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import paddle
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from fastdeploy.model_executor.ops.gpu import rejection_top_p_sampling
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class TestRejectionTopPSampling(unittest.TestCase):
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def setUp(self):
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"""Initialize common test data"""
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self.batch_size = 10
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self.vocab_size = 103424
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paddle.seed(2023)
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# Generate test data once for all tests
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self.pre_norm_prob_np = np.random.rand(self.batch_size, self.vocab_size).astype(np.float32)
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self.paddle_pre_norm_prob = paddle.to_tensor(self.pre_norm_prob_np)
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self.paddle_norm_prob = self.paddle_pre_norm_prob / self.paddle_pre_norm_prob.sum(axis=-1, keepdim=True)
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def test_top_p_sampling_reject_case1(self):
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"""Test with fixed top_p=0.8 and different random seeds"""
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top_p_paddle = paddle.full((self.batch_size,), 0.8)
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top_k_paddle = paddle.full((self.batch_size,), 20).cast("int64")
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# Test with different seeds
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for seed in [1024, 2033, 2033]:
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samples = rejection_top_p_sampling(self.paddle_norm_prob, top_p_paddle, top_k_paddle, seed)
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self._validate_samples(samples)
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# Basic validation
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self.assertTrue(paddle.all(samples >= 0))
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self.assertTrue(paddle.all(samples < self.vocab_size))
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def test_top_p_sampling_reject_case2(self):
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"""Test with varying top_p values across batch"""
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top_p_paddle = paddle.uniform(shape=[self.batch_size], min=0.1, max=1.0)
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top_k_paddle = paddle.full((self.batch_size,), 20).cast("int64")
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samples = rejection_top_p_sampling(self.paddle_norm_prob, top_p_paddle, top_k_paddle, -1)
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self._validate_samples(samples)
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# Additional check that we're getting different results for different top_p
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unique_samples = len(paddle.unique(samples))
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self.assertGreater(unique_samples, 1) # Should have some diversity
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def _validate_samples(self, samples):
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"""Common validation for all test cases"""
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self.assertTrue(paddle.all(samples >= 0))
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self.assertTrue(paddle.all(samples < self.vocab_size))
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# Check dtype
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self.assertEqual(samples.dtype, paddle.int64)
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if __name__ == "__main__":
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unittest.main()
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