mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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101 lines
3.8 KiB
Python
101 lines
3.8 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.layers.sample.ops.speculate_logprob_utils import (
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speculate_insert_first_token,
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)
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class TestSpeculateInsertFirstToken(unittest.TestCase):
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def test_all_decode(self):
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token_num = 5
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accept_tokens = paddle.to_tensor([[1001, 1002], [1003, 1004], [1005, 1006]], dtype="int64")
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next_tokens = paddle.to_tensor([[2001], [2002], [2003], [2004], [2005]], dtype="int64")
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cu_next_token_offset = paddle.to_tensor([0, 2, 3, 5], dtype="int32")
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cu_batch_token_offset = paddle.to_tensor([0, 2, 3, 5], dtype="int32")
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seq_lens_this_time = paddle.to_tensor([[2], [1], [2]], dtype="int32")
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seq_lens_encoder = paddle.to_tensor([[0], [0], [0]], dtype="int32")
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token_id = paddle.empty(token_num, dtype="int64")
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speculate_insert_first_token(
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token_id,
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accept_tokens,
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next_tokens,
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cu_next_token_offset,
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cu_batch_token_offset,
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seq_lens_this_time,
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seq_lens_encoder,
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)
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gold_token_id = paddle.to_tensor([2001, 2002, 2003, 2004, 2005], dtype="int64")
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assert paddle.equal_all(token_id, gold_token_id)
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def test_partial_decode(self):
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token_num = 6
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accept_tokens = paddle.to_tensor([[1001, 1002], [1003, 1004], [1005, 1006]], dtype="int64")
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next_tokens = paddle.to_tensor([[2001], [2002], [2003], [2004], [2005]], dtype="int64")
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cu_next_token_offset = paddle.to_tensor([0, 2, 3, 5], dtype="int32")
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cu_batch_token_offset = paddle.to_tensor([0, 2, 4, 6], dtype="int32")
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seq_lens_this_time = paddle.to_tensor([[2], [10], [2]], dtype="int32")
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seq_lens_encoder = paddle.to_tensor([[0], [10], [0]], dtype="int32")
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token_id = paddle.empty(token_num, dtype="int64")
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speculate_insert_first_token(
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token_id,
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accept_tokens,
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next_tokens,
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cu_next_token_offset,
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cu_batch_token_offset,
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seq_lens_this_time,
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seq_lens_encoder,
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)
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gold_token_id = paddle.to_tensor([2001, 2002, 1003, 2003, 2004, 2005], dtype="int64")
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assert paddle.equal_all(token_id, gold_token_id)
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def test_all_prefill(self):
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token_num = 6
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accept_tokens = paddle.to_tensor([[1001, 1002], [1003, 1004], [1005, 1006]], dtype="int64")
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next_tokens = paddle.to_tensor([[2001], [2002], [2003]], dtype="int64")
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cu_next_token_offset = paddle.to_tensor([0, 1, 2, 3], dtype="int32")
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cu_batch_token_offset = paddle.to_tensor([0, 2, 4, 6], dtype="int32")
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seq_lens_this_time = paddle.to_tensor([[10], [10], [10]], dtype="int32")
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seq_lens_encoder = paddle.to_tensor([[10], [10], [10]], dtype="int32")
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token_id = paddle.empty(token_num, dtype="int64")
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speculate_insert_first_token(
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token_id,
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accept_tokens,
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next_tokens,
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cu_next_token_offset,
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cu_batch_token_offset,
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seq_lens_this_time,
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seq_lens_encoder,
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)
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gold_token_id = paddle.to_tensor([1001, 2001, 1003, 2002, 1005, 2003], dtype="int64")
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assert paddle.equal_all(token_id, gold_token_id)
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if __name__ == "__main__":
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unittest.main()
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