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* support mtp distribution equivalence verification * fix bugs * add unit test
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
# Copyright (c) 2025 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.layers.sample.sampler import padding_sampling_params
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class TestPaddingSamplingParams(unittest.TestCase):
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def setUp(self):
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self.batch_size = 32
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def test_all_decode(self):
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top_p = paddle.to_tensor([0.8, 0.9, 0.95], dtype="float32")
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top_k = paddle.to_tensor([10, 20, 30], dtype="int64")
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seq_lens_this_time = paddle.to_tensor([2, 3, 1], dtype="int64")
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seq_lens_encoder = paddle.to_tensor([0, 0, 0], dtype="int64")
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top_p_padding, top_k_padding = padding_sampling_params(top_p, top_k, seq_lens_this_time, seq_lens_encoder)
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expected_len = sum(seq_lens_this_time.numpy())
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self.assertEqual(top_p_padding.shape[0], expected_len)
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self.assertEqual(top_k_padding.shape[0], expected_len)
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expected_top_p = np.repeat([0.8, 0.9, 0.95], [2, 3, 1]).reshape(-1, 1)
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np.testing.assert_allclose(top_p_padding.numpy(), expected_top_p, rtol=1e-6)
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def test_partial_decode(self):
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top_p = paddle.to_tensor([0.7, 0.6, 0.5], dtype="float32")
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top_k = paddle.to_tensor([15, 25, 35], dtype="int64")
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seq_lens_this_time = paddle.to_tensor([3, 2, 4], dtype="int64")
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seq_lens_encoder = paddle.to_tensor([0, 1, 0], dtype="int64")
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top_p_padding, top_k_padding = padding_sampling_params(top_p, top_k, seq_lens_this_time, seq_lens_encoder)
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expected_repeats = [3, 1, 4]
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expected_top_p = np.repeat([0.7, 0.6, 0.5], expected_repeats).reshape(-1, 1)
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expected_top_k = np.repeat([15, 25, 35], expected_repeats).reshape(-1, 1)
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np.testing.assert_allclose(top_p_padding.numpy(), expected_top_p, rtol=1e-6)
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np.testing.assert_array_equal(top_k_padding.numpy(), expected_top_k)
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def test_all_prefill(self):
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top_p = paddle.to_tensor([0.5, 0.6], dtype="float32")
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top_k = paddle.to_tensor([5, 6], dtype="int64")
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seq_lens_this_time = paddle.to_tensor([4, 3], dtype="int64")
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seq_lens_encoder = paddle.to_tensor([1, 2], dtype="int64")
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top_p_padding, top_k_padding = padding_sampling_params(top_p, top_k, seq_lens_this_time, seq_lens_encoder)
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expected_top_p = np.array([[0.5], [0.6]])
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expected_top_k = np.array([[5], [6]])
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np.testing.assert_allclose(top_p_padding.numpy(), expected_top_p)
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np.testing.assert_array_equal(top_k_padding.numpy(), expected_top_k)
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if __name__ == "__main__":
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unittest.main()
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