mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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ming1753
121 lines
4.1 KiB
Python
121 lines
4.1 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 paddle
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from fastdeploy.model_executor.ops.gpu import ngram_match
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class TestNgramMatchOp(unittest.TestCase):
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def setUp(self):
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paddle.set_device("cpu")
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def test_basic_match(self):
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"""
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Case 1: input_ids overlaps with token_ids_all, and can extract draft tokens.
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"""
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batch_size = 1
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seq_len = 6
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# Input IDs
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input_ids = paddle.to_tensor([[10, 20, 30, 40, 50, 60]], dtype="int64")
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# Length of input IDs
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input_ids_len = paddle.to_tensor([6], dtype="int64")
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# Previous IDs
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token_ids_all = paddle.to_tensor([[10, 20, 30, 40, 0, 0]], dtype="int64")
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prompt_lens = paddle.zeros([4, 1], dtype="int64")
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# Current step index
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step_idx = paddle.to_tensor([3], dtype="int64")
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# Number of draft tokens
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draft_token_num = paddle.to_tensor([3], dtype="int32")
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# Placeholder for draft tokens
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draft_tokens = paddle.zeros([batch_size, seq_len], dtype="int64")
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# Sequence lengths for this time step
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seq_lens_this_time = paddle.zeros([batch_size], dtype="int32")
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# Sequence lengths for encoder
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seq_lens_encoder = paddle.zeros([batch_size], dtype="int32")
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# Sequence lengths for decoder
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seq_lens_decoder = paddle.ones([batch_size], dtype="int32")
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# Maximum decoding length
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max_dec_len = paddle.to_tensor([10], dtype="int64")
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ngram_match(
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input_ids,
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input_ids_len,
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token_ids_all,
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prompt_lens,
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step_idx,
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draft_token_num,
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draft_tokens,
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seq_lens_this_time,
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seq_lens_encoder,
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seq_lens_decoder,
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max_dec_len,
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3,
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4,
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)
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# Extract non-zero tokens and assert the results.
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nonzero_tokens = draft_tokens.numpy()[0][draft_tokens.numpy()[0] != 0]
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expected_tokens = [50, 60]
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self.assertTrue((nonzero_tokens == expected_tokens).all())
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# Check length
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self.assertEqual(seq_lens_this_time.numpy()[0], 3)
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def test_no_match(self):
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"""
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Case 2: token_ids_all does not match input_ids, should only keep the current token.
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"""
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batch_size = 1
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input_ids = paddle.to_tensor([[100, 200, 300, 400]], dtype="int64")
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input_ids_len = paddle.to_tensor([4], dtype="int64")
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token_ids_all = paddle.to_tensor([[1, 2, 3, 4]], dtype="int64")
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prompt_lens = paddle.zeros([4, 1], dtype="int64")
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step_idx = paddle.to_tensor([3], dtype="int64")
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draft_token_num = paddle.to_tensor([2], dtype="int32")
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draft_tokens = paddle.zeros([batch_size, 4], dtype="int64")
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seq_lens_this_time = paddle.zeros([batch_size], dtype="int32")
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seq_lens_encoder = paddle.zeros([batch_size], dtype="int32")
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seq_lens_decoder = paddle.ones([batch_size], dtype="int32")
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max_dec_len = paddle.to_tensor([6], dtype="int64")
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ngram_match(
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input_ids,
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input_ids_len,
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token_ids_all,
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prompt_lens,
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step_idx,
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draft_token_num,
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draft_tokens,
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seq_lens_this_time,
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seq_lens_encoder,
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seq_lens_decoder,
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max_dec_len,
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3,
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3,
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)
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# No match → should only keep 1 token
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self.assertEqual(seq_lens_this_time.numpy()[0], 1)
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if __name__ == "__main__":
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unittest.main()
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