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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| from __future__ import annotations | ||
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| from abc import ABC, abstractmethod | ||
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| import paddle | ||
| from paddlenlp_ops import ngram_match | ||
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| class Proposer(ABC): | ||
| """ | ||
| Abstract base class for all proposers that can be used in the speculative decoding framework. | ||
| The subclasses of this class must implement the run method to get the draft tokens that are | ||
| generated by the proposer. | ||
| """ | ||
|
|
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| def __init__(self, **kwargs): | ||
| pass | ||
|
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| @abstractmethod | ||
| def run(self, model_inputs: dict[str, paddle.Tensor], **kargs): | ||
| """ | ||
| Get the draft tokens that are generated by the proposer. | ||
| """ | ||
| raise NotImplementedError() | ||
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| class InferenceWithReferenceProposer(Proposer): | ||
| """ | ||
| InferenceWithReference(https://arxiv.org/pdf/2304.04487) is one of the speculative decoding method. | ||
| It match tokens in the input and output as draft tokens. | ||
| """ | ||
|
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| def __init__(self, max_draft_token_num: int, max_ngram_size: int, max_batch_size: int): | ||
| """ | ||
| Args: | ||
| max_draft_token_num (int): | ||
| Maximum number of tokens a proposer can generate at one time. | ||
| The hyperparameter of k in the paper. | ||
| max_ngram_size (int): | ||
| The maximum size of the window used to match inputs and outputs. | ||
| The hyperparameter of n in the paper. | ||
| max_batch_size (int): | ||
| The maximum batch size. | ||
| """ | ||
| super().__init__() | ||
| self.max_ngram_size = max_ngram_size | ||
| self.input_ids_len = paddle.zeros(shape=[max_batch_size, 1], dtype="int64").cpu() | ||
| self.max_batch_size = max_batch_size | ||
| self.max_draft_token_num = max_draft_token_num | ||
| # self.input_ids_cpu = paddle.full(shape=[max_batch_size, max_seq_len], fill_value=1, dtype="int64").cpu() | ||
|
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| def update(self, bid: int, seq_len: int): | ||
| """ | ||
| Used when inserting a new query to update the length of the input_ids. | ||
| """ | ||
| self.input_ids_len[bid] = seq_len | ||
|
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| def run(self, share_inputs: dict[str, paddle.Tensor], **kargs): | ||
| """ | ||
| Use ngram_match to get draft tokens from the input and output. | ||
| """ | ||
| draft_tokens = share_inputs["draft_tokens"].cpu() | ||
| seq_lens_this_time = kargs["seq_lens_this_time"].cpu() | ||
| seq_lens_encoder = share_inputs["seq_lens_encoder"].cpu() | ||
| seq_lens_decoder = share_inputs["seq_lens_decoder"].cpu() | ||
| ngram_match( | ||
| share_inputs["input_ids_cpu"], | ||
| self.input_ids_len.cpu(), | ||
| share_inputs["pre_ids"].cpu(), | ||
| share_inputs["step_idx"].cpu(), | ||
| share_inputs["actual_draft_token_num"].cpu(), | ||
| draft_tokens, | ||
| seq_lens_this_time, | ||
| seq_lens_encoder, | ||
| seq_lens_decoder, | ||
| kargs["real_batch_size"], | ||
| self.max_ngram_size, | ||
| self.max_draft_token_num, | ||
| ) | ||
| share_inputs["draft_tokens"][:] = draft_tokens.cuda() | ||
| share_inputs["seq_lens_encoder"][:] = seq_lens_encoder.cuda() | ||
| kargs["seq_lens_this_time"][:] = seq_lens_this_time.cuda() |
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