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ming1753
100 lines
3.3 KiB
Python
100 lines
3.3 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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"""UT for GPU operator stop_generation_multi_ends"""
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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 set_stop_value_multi_ends
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def test_set_stop_value_multi_ends_with_stop_seq():
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sampled_token_ids = paddle.to_tensor([[61502], [2]], dtype="int64")
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stop_flags = paddle.to_tensor([[False], [True]], dtype="bool")
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seq_lens_this_time = paddle.to_tensor([[1], [0]], dtype="int32")
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eos_token_id = paddle.to_tensor([2], dtype="int64")
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next_tokens = paddle.to_tensor([[61502], [2]], dtype="int64")
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token_ids_all = paddle.full([2, 32768], -1, dtype="int64")
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token_ids_all[0, :10] = np.array([21, 22, 23, 24, 25, 26, 27, 28, 8038, 61502])
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prompt_lens = paddle.zeros([2, 1], dtype="int64")
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step_idx = paddle.to_tensor([[10], [0]], dtype="int64")
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stop_token_ids = paddle.full([2, 5, 8], -1, dtype="int64")
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stop_token_ids[0, 0, :2] = np.array([8038, 61502])
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stop_seqs_len = paddle.full([2, 5], 10, dtype="int32")
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stop_seqs_len[0, 0] = 2
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min_tokens = paddle.to_tensor([[0], [0]], dtype="int64")
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set_stop_value_multi_ends(
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sampled_token_ids,
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stop_flags,
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seq_lens_this_time,
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eos_token_id,
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next_tokens,
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token_ids_all,
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prompt_lens,
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step_idx,
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stop_token_ids,
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stop_seqs_len,
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min_tokens,
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False,
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)
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assert bool(stop_flags[0, 0]) is True
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assert sampled_token_ids[0, 0] == 2 # eos token id
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def test_min_tokens():
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"""Test min_tokens functionality"""
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sampled_token_ids = paddle.to_tensor([[2], [100], [200]], dtype="int64")
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stop_flags = paddle.to_tensor([[False], [False], [False]], dtype="bool")
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seq_lens_this_time = paddle.to_tensor([[1], [1], [1]], dtype="int32")
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eos_token_id = paddle.to_tensor([2], dtype="int64")
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next_tokens = paddle.to_tensor([[2], [100], [200]], dtype="int64")
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token_ids_all = paddle.full([3, 100], -1, dtype="int64")
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prompt_lens = paddle.zeros([3, 1], dtype="int64")
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step_idx = paddle.to_tensor([[5], [50], [10]], dtype="int64")
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stop_seqs = paddle.full([3, 5, 8], -1, dtype="int64")
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stop_seqs_len = paddle.zeros([3, 5], dtype="int32")
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min_tokens = paddle.to_tensor([[10], [0], [5]], dtype="int64")
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set_stop_value_multi_ends(
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sampled_token_ids,
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stop_flags,
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seq_lens_this_time,
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eos_token_id,
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next_tokens,
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token_ids_all,
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prompt_lens,
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step_idx,
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stop_seqs,
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stop_seqs_len,
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min_tokens,
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False,
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)
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# Sample 0: step < min_tokens, should not stop even with EOS
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assert bool(stop_flags[0, 0]) is False
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if __name__ == "__main__":
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test_set_stop_value_multi_ends_with_stop_seq()
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test_min_tokens()
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