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* [feat] provide an interface for logits processors and a builtin LogitBiasLogitsProcessor * [chore] fix code style * [fix] add unit test & fix existing bugs * [feat] add engine/worker arg --logits-processors * [fix] redefine user args as logits_processors_args and fix some bugs * [fix] fix test_sampler * Update fastdeploy/model_executor/logits_processor/builtin.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update fastdeploy/model_executor/logits_processor/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/model_executor/test_logits_processor.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * [fix] fix typo * Update fastdeploy/engine/sampling_params.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * [fix] fix bracelet * [chore] redefine logits processor interface: pass the entire share_inputs into LP, do not copy share_inputs and logits * [doc] add docs * [fix] fix logit bias processor not applied when decoding is too fast & add docs and tests * [fix] fix redundant code * [feat] skip apply() if no bias is specified --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
69 lines
2.6 KiB
Python
69 lines
2.6 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.logits_processor.base import LogitsProcessor
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class LogitBiasLogitsProcessor(LogitsProcessor):
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"""
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Maintains per-request logit biases and applies them to logits.
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"""
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def __init__(self, fd_config: FDConfig):
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self.device = paddle.device.get_device()
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self.dtype = fd_config.model_config.dtype
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self.batch_ids: list[int] = []
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self.token_ids: list[int] = []
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self.biases: list[float] = []
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def update_state(self, share_inputs: dict):
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"""Build per-step logit-bias state from request slots and move it to device."""
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# Retrive inference states from share_inputs
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stop_flags = share_inputs["stop_flags"]
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logits_processors_args = share_inputs["logits_processors_args"]
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logits_processors_args = [a for a, f in zip(logits_processors_args, stop_flags) if not f]
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# Get bias states for each request
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self.batch_ids = []
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self.token_ids: list[int] = []
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self.biases: list[float] = []
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for batch_id, logit_proc_args in enumerate(logits_processors_args):
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tok_id_bias_map = logit_proc_args.get("logit_bias") or {}
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self.batch_ids.extend([batch_id] * len(tok_id_bias_map))
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self.token_ids.extend(tok_id_bias_map.keys())
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self.biases.extend(tok_id_bias_map.values())
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return
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def apply(self, logits: paddle.Tensor) -> paddle.Tensor:
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"""Apply logit bias to logits: [batch_size, vocab_size]"""
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# Skip if no bias is applied
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if len(self.biases) == 0:
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return logits
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# Make bias indices and bias tensor
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bias_indices = (
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paddle.tensor(self.batch_ids, dtype="int32").to(self.device),
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paddle.tensor(self.token_ids, dtype="int32").to(self.device),
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)
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bias_tensor = paddle.tensor(self.biases, device=self.device, dtype=self.dtype)
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logits[bias_indices] += bias_tensor
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return logits
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