mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-09-27 21:02:24 +08:00
85 lines
3.2 KiB
Python
85 lines
3.2 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.ops.gpu import draft_model_postprocess
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def draft_model_postprocess_cpu(
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base_model_draft_tokens,
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base_model_seq_lens_encoder,
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base_model_stop_flags,
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):
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bsz = base_model_draft_tokens.shape[0]
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base_model_draft_token_len = base_model_draft_tokens.shape[1]
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base_model_seq_lens_this_time = paddle.ones((bsz), dtype=paddle.int32)
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for tid in range(bsz):
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if (not base_model_stop_flags[tid]) and (base_model_seq_lens_encoder[tid] == 0):
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base_model_draft_tokens_now = base_model_draft_tokens[tid]
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token_num = 0
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for i in range(base_model_draft_token_len):
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if base_model_draft_tokens_now[i] != -1:
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token_num += 1
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base_model_seq_lens_this_time[tid] = token_num
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elif base_model_stop_flags[tid]:
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base_model_seq_lens_this_time[tid] = 0
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return base_model_seq_lens_this_time
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class TestDraftModelPostProcess(unittest.TestCase):
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def _test_draft_model_postprocess(self, batch_size=1, base_model_draft_token_len=8192):
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paddle.seed(66)
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base_model_draft_tokens = paddle.randint(
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low=-1,
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high=1,
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shape=[batch_size, base_model_draft_token_len],
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dtype="int64",
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)
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base_model_seq_lens_encoder = paddle.randint(low=0, high=2, shape=[batch_size], dtype="int32")
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random_floats = paddle.rand(shape=[batch_size])
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base_model_stop_flags = random_floats >= 0.5
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base_model_seq_lens_this_time = draft_model_postprocess_cpu(
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base_model_draft_tokens,
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base_model_seq_lens_encoder,
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base_model_stop_flags,
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)
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base_model_seq_lens_this_time_gpu = paddle.ones((batch_size), dtype=paddle.int32)
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draft_model_postprocess(
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base_model_draft_tokens,
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base_model_seq_lens_this_time_gpu,
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base_model_seq_lens_encoder,
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base_model_stop_flags,
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)
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np.testing.assert_allclose(base_model_seq_lens_this_time.numpy(), base_model_seq_lens_this_time_gpu.numpy())
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def test_enough_cases(self):
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self._test_draft_model_postprocess(100, 1024)
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self._test_draft_model_postprocess(1, 11)
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self._test_draft_model_postprocess(1, 8192)
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self._test_draft_model_postprocess(2, 2048)
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self._test_draft_model_postprocess(3, 1023)
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self._test_draft_model_postprocess(4, 2047)
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self._test_draft_model_postprocess(5, 4095)
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self._test_draft_model_postprocess(10, 9191)
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if __name__ == "__main__":
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unittest.main()
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