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GoldPancake
123 lines
4.4 KiB
Python
123 lines
4.4 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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from abc import ABC, abstractmethod
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from copy import deepcopy
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from typing import Any
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import paddle.distributed as dist
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from fastdeploy import envs
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from fastdeploy.config import FDConfig
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from fastdeploy.utils import spec_logger
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class Proposer(ABC):
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"""
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Proposer Base Class.
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Used to provide an extensible interface for draft tokens within
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the speculative decoding framework
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"""
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def __init__(self, fd_config: FDConfig):
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"""
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Init Speculative proposer
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"""
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fd_config.parallel_config.tp_group = None
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fd_config.parallel_config.ep_group = None
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self.fd_config = deepcopy(fd_config)
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fd_config.parallel_config.tp_group = dist.get_group(
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fd_config.parallel_config.data_parallel_rank + envs.FD_TP_GROUP_GID_OFFSET
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)
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fd_config.parallel_config.ep_group = dist.get_group(
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fd_config.parallel_config.data_parallel_size + envs.FD_TP_GROUP_GID_OFFSET
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)
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self.fd_config.parallel_config.tp_group = dist.get_group(
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fd_config.parallel_config.data_parallel_rank + envs.FD_TP_GROUP_GID_OFFSET
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)
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self.fd_config.parallel_config.ep_group = dist.get_group(
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fd_config.parallel_config.data_parallel_size + envs.FD_TP_GROUP_GID_OFFSET
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)
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self.parallel_config = self.fd_config.parallel_config
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self.model_config = self.fd_config.model_config
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self.speculative_config = self.fd_config.speculative_config
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self.cache_config = self.fd_config.cache_config
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self.quant_config = self.fd_config.quant_config
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self.graph_opt_config = self.fd_config.graph_opt_config
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self.scheduler_config = self.fd_config.scheduler_config
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self.max_num_seqs = self.scheduler_config.max_num_seqs
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self.max_model_len = self.model_config.max_model_len
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self.speculative_method = self.speculative_config.method
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self.max_draft_token_num = self.speculative_config.num_speculative_tokens
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self.num_model_steps = self.speculative_config.num_model_steps
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self.max_ngram_size = self.speculative_config.max_ngram_size
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self.min_ngram_size = self.speculative_config.min_ngram_size
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self.enable_mm = self.model_config.enable_mm
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spec_logger.info(f"Speculate config: {self.speculative_config}")
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def run(self, *args, **kwargs) -> Any:
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"""
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Unified entry point for all proposer types.
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Dispatches to subclass-specific logic via `_run_impl`.
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"""
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return self._run_impl(*args, **kwargs)
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@abstractmethod
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def _run_impl(self, *args, **kwargs) -> Any:
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"""
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Implementation for different method
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"""
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raise NotImplementedError
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def is_chunk_prefill_enabled(self) -> bool:
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"""
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Check whether chunk-based prefill is enabled.
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Default is False.
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Returns:
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bool: True if chunk prefill is enabled; False otherwise.
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"""
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return False
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def prepare_dummy_speculative_drafts(
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self,
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share_inputs,
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batch_size: int,
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) -> None:
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"""
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Construct a set of dummy draft tokens for CUDAGraph capture scenarios,
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used only to stabilize shape/step count, with no requirement for semantic correctness.
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Args:
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share_inputs: share_inputs dict maintained by GPUModelRunner
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batch_size: current batch_size for dummy_run
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expected_decode_len: expected number of decode steps (must match what's passed to _dummy_run)
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"""
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max_fake_drafts = self.max_draft_token_num
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stop = share_inputs["stop_flags"][0].item()
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if not stop:
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share_inputs["draft_tokens"][:batch_size, :max_fake_drafts] = 5
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share_inputs["seq_lens_this_time"][:batch_size] = max_fake_drafts + 1
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else:
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share_inputs["seq_lens_this_time"][:batch_size] = 0
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