mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import paddle
|
|
|
|
from fastdeploy.model_executor.ops.gpu import rejection_top_p_sampling
|
|
|
|
|
|
class TestRejectionTopPSampling(unittest.TestCase):
|
|
def setUp(self):
|
|
"""Initialize common test data"""
|
|
self.batch_size = 10
|
|
self.vocab_size = 103424
|
|
paddle.seed(2023)
|
|
|
|
# Generate test data once for all tests
|
|
self.pre_norm_prob_np = np.random.rand(self.batch_size, self.vocab_size).astype(np.float32)
|
|
self.paddle_pre_norm_prob = paddle.to_tensor(self.pre_norm_prob_np)
|
|
self.paddle_norm_prob = self.paddle_pre_norm_prob / self.paddle_pre_norm_prob.sum(axis=-1, keepdim=True)
|
|
|
|
def test_top_p_sampling_reject_case1(self):
|
|
"""Test with fixed top_p=0.8 and different random seeds"""
|
|
top_p_paddle = paddle.full((self.batch_size,), 0.8)
|
|
|
|
# Test with different seeds
|
|
for seed in [1024, 2033, 2033]:
|
|
samples = rejection_top_p_sampling(self.paddle_norm_prob, top_p_paddle, seed)
|
|
self._validate_samples(samples)
|
|
|
|
# Basic validation
|
|
self.assertTrue(paddle.all(samples >= 0))
|
|
self.assertTrue(paddle.all(samples < self.vocab_size))
|
|
|
|
def test_top_p_sampling_reject_case2(self):
|
|
"""Test with varying top_p values across batch"""
|
|
top_p_paddle = paddle.uniform(shape=[self.batch_size], min=0.1, max=1.0)
|
|
samples = rejection_top_p_sampling(self.paddle_norm_prob, top_p_paddle, -1)
|
|
|
|
self._validate_samples(samples)
|
|
|
|
# Additional check that we're getting different results for different top_p
|
|
unique_samples = len(paddle.unique(samples))
|
|
print(f"Unique samples: {unique_samples}")
|
|
self.assertGreater(unique_samples, 1) # Should have some diversity
|
|
|
|
def _validate_samples(self, samples):
|
|
"""Common validation for all test cases"""
|
|
self.assertTrue(paddle.all(samples >= 0))
|
|
self.assertTrue(paddle.all(samples < self.vocab_size))
|
|
|
|
# Check dtype
|
|
self.assertEqual(samples.dtype, paddle.int64)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|