mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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70 lines
2.3 KiB
Python
70 lines
2.3 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.config import FDConfig
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from fastdeploy.model_executor.ops.gpu import ngram_match
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from .base import Proposer
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class NgramProposer(Proposer):
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"""
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Proposer for Ngram match method.
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Matching corresponding tokens in input and output as draft tokens.
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"""
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def __init__(self, cfg: FDConfig):
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super().__init__(cfg)
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self.max_ngram_size = self.speculative_config.max_ngram_size
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self.input_ids_len = paddle.zeros(shape=[self.max_num_seqs, 1],
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dtype="int64").cpu()
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def update(self, bid: int, seq_len: int):
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"""
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update
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"""
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self.input_ids_len[bid] = seq_len
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def _run_impl(self, share_inputs):
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"""
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run
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"""
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draft_tokens = share_inputs["draft_tokens"].cpu()
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seq_lens_this_time = share_inputs["seq_lens_this_time"].cpu()
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seq_lens_encoder = share_inputs["seq_lens_encoder"].cpu()
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seq_lens_decoder = share_inputs["seq_lens_decoder"].cpu()
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ngram_match(
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share_inputs["input_ids_cpu"],
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self.input_ids_len.cpu(),
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share_inputs["pre_ids"].cpu(),
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share_inputs["step_idx"].cpu(),
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share_inputs["actual_draft_token_num"].cpu(),
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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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share_inputs["max_dec_len"].cpu(),
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self.max_ngram_size,
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self.max_draft_token_num,
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)
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share_inputs["draft_tokens"][:] = draft_tokens.cuda()
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share_inputs["seq_lens_encoder"][:] = seq_lens_encoder.cuda()
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share_inputs["seq_lens_this_time"][:] = seq_lens_this_time.cuda()
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