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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
"""
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# 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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"""
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import paddle
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from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
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from fastdeploy.model_executor.layers.sample.sampler import Sampler
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def _create_fake_logits(batch_size: int, vocab_size: int) -> paddle.Tensor:
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fake_logits = paddle.full(shape=[batch_size, vocab_size], fill_value=1e-2, dtype="float32")
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return fake_logits
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def _create_penalty_tensor(batch_size: int, penalty_value: float) -> paddle.Tensor:
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return paddle.full(shape=[batch_size, 1], fill_value=penalty_value, dtype="float32")
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def _create_tokens_tensor(
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batch_size: int,
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max_seq_len: int,
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) -> paddle.Tensor:
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pre_token_ids = paddle.full(shape=[batch_size, max_seq_len], fill_value=-1, dtype="int64")
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return pre_token_ids
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def _create_default_sampling_metadata(
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batch_size: int,
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min_seq_len: int,
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max_seq_len: int,
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) -> SamplingMetadata:
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fake_sampling_metadata = SamplingMetadata(
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temperature=paddle.full(shape=[batch_size, 1], fill_value=0.9, dtype="float32"),
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top_p=paddle.full(shape=[batch_size, 1], fill_value=0.7, dtype="float32"),
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prompt_ids=paddle.full(shape=[batch_size, max_seq_len], fill_value=0, dtype="int64"),
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prompt_lens=paddle.full(shape=[batch_size, 1], fill_value=5, dtype="int64"),
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step_idx=paddle.full(shape=[batch_size, 1], fill_value=0, dtype="int64"),
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pre_token_ids=_create_tokens_tensor(batch_size, max_seq_len),
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frequency_penalties=_create_penalty_tensor(batch_size, 0.0),
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presence_penalties=_create_penalty_tensor(batch_size, 0.0),
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repetition_penalties=_create_penalty_tensor(batch_size, 1.0),
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min_dec_lens=paddle.full(shape=[batch_size, 1], fill_value=min_seq_len, dtype="int64"),
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bad_words_token_ids=paddle.full(shape=[batch_size], fill_value=-1, dtype="int64"),
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eos_token_ids=paddle.full(shape=[batch_size], fill_value=-2, dtype="int64"),
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min_p=paddle.randn([batch_size]),
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seed=paddle.to_tensor([[2025]]),
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)
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return fake_sampling_metadata
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def test_sampler():
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batch_size = 32
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vocab_size = 1024
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min_seq_len = 1
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max_seq_len = 1024
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sampler = Sampler()
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logits = _create_fake_logits(batch_size, vocab_size)
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sampling_metadata = _create_default_sampling_metadata(batch_size, min_seq_len, max_seq_len)
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next_tokens = sampler(logits, sampling_metadata)
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print(next_tokens)
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if __name__ == "__main__":
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test_sampler()
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