392 lines
15 KiB
Python
392 lines
15 KiB
Python
from __future__ import annotations
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import dataclasses
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import logging
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import threading
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
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import torch
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import sglang.srt.sampling.penaltylib as penaltylib
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from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
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from sglang.srt.sampling.penaltylib.penalizers.repetition_penalty import (
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apply_scaling_penalties,
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)
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logger = logging.getLogger(__name__)
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if TYPE_CHECKING:
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from sglang.srt.managers.schedule_batch import ScheduleBatch
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@dataclasses.dataclass
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class SamplingBatchInfo:
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# Batched sampling params
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temperatures: torch.Tensor
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top_ps: torch.Tensor
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top_ks: torch.Tensor
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min_ps: torch.Tensor
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# All requests use greedy sampling
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is_all_greedy: bool
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# Dispatch in CUDA graph
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need_min_p_sampling: bool
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# Whether any request has custom logit processor
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has_custom_logit_processor: bool
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# Bias Tensors
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vocab_size: int
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grammars: Optional[List] = None
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sampling_info_done: Optional[threading.Event] = None
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logit_bias: torch.Tensor = None
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vocab_mask: Optional[torch.Tensor] = None
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apply_mask: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None
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# Penalizer
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penalizer_orchestrator: Optional[penaltylib.BatchedPenalizerOrchestrator] = None
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linear_penalties: Optional[torch.Tensor] = None
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scaling_penalties: Optional[torch.Tensor] = None
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# Device
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device: str = "cuda"
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# Custom Parameters
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custom_params: Optional[List[Optional[Dict[str, Any]]]] = None
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# Custom Logit Processor
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custom_logit_processor: Optional[
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Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]
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] = None
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@classmethod
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def from_schedule_batch(
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cls, batch: ScheduleBatch, vocab_size: int, enable_overlap_schedule: bool
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):
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reqs = batch.reqs
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device = batch.device
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temperatures = (
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torch.tensor(
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[r.sampling_params.temperature for r in reqs],
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dtype=torch.float,
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)
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.view(-1, 1)
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.to(device, non_blocking=True)
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)
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top_ps = torch.tensor(
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[r.sampling_params.top_p for r in reqs], dtype=torch.float
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).to(device, non_blocking=True)
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top_ks = torch.tensor(
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[r.sampling_params.top_k for r in reqs], dtype=torch.int32
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).to(device, non_blocking=True)
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min_ps = torch.tensor(
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[r.sampling_params.min_p for r in reqs], dtype=torch.float
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).to(device, non_blocking=True)
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# Check if any request has custom logit processor
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has_custom_logit_processor = (
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batch.enable_custom_logit_processor # check the flag first.
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and any(r.custom_logit_processor for r in reqs) # then check the requests.
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)
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if has_custom_logit_processor:
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# Merge the same type of custom logit processors together
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processor_dict = {}
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for i, r in enumerate(reqs):
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if r.custom_logit_processor is None:
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continue
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processor_str = r.custom_logit_processor
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if processor_str not in processor_dict:
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processor_dict[processor_str] = []
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processor_dict[processor_str].append(i)
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merged_custom_logit_processor = {
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hash(processor_str): (
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# The deserialized custom logit processor object
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CustomLogitProcessor.from_str(processor_str),
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# The mask tensor for the requests that use this custom logit processor
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torch.zeros(len(reqs), dtype=torch.bool)
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.scatter_(0, torch.tensor(true_indices), True)
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.to(device, non_blocking=True),
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)
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for processor_str, true_indices in processor_dict.items()
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}
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custom_params = [r.sampling_params.custom_params for r in reqs]
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else:
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merged_custom_logit_processor = None
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custom_params = None
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ret = cls(
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temperatures=temperatures,
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top_ps=top_ps,
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top_ks=top_ks,
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min_ps=min_ps,
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need_min_p_sampling=any(r.sampling_params.min_p > 0 for r in reqs),
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is_all_greedy=all(r.sampling_params.top_k <= 1 for r in reqs),
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has_custom_logit_processor=has_custom_logit_processor,
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vocab_size=vocab_size,
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device=device,
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custom_params=custom_params,
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custom_logit_processor=merged_custom_logit_processor,
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)
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# TODO (lianmin): `need_min_p_sampling` needs to be updated in filter and merge.
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if enable_overlap_schedule:
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# TODO (lianmin): Some penalizers such as frequency and presence depend on model outputs,
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# so it is kind of tricky to make it work with overlap scheduler.
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# It requires correcly updating the penalty logits before the sampling and syncing the events.
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# We will support them later.
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penalizers = {
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penaltylib.BatchedMinNewTokensPenalizer,
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}
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if (
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any(req.sampling_params.frequency_penalty != 0.0 for req in reqs)
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or any(req.sampling_params.presence_penalty != 0.0 for req in reqs)
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or any(req.sampling_params.repetition_penalty != 1.0 for req in reqs)
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):
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logger.warning(
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"frequency_penalty, presence_penalty, and repetition_penalty are not supported "
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"when using the default overlap scheduler. They will be ignored. "
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"Please add `--disable-overlap` when launching the server if you need these features. "
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"The speed will be slower in that case."
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)
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else:
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penalizers = {
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penaltylib.BatchedFrequencyPenalizer,
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penaltylib.BatchedMinNewTokensPenalizer,
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penaltylib.BatchedPresencePenalizer,
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penaltylib.BatchedRepetitionPenalizer,
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}
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# Each penalizers will do nothing if they evaluate themselves as not required by looking at
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# the sampling_params of the requests (See {_is_required()} of each penalizers). So this
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# should not add hefty computation overhead other than simple checks.
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#
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# While we choose not to even create the class instances if they are not required, this
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# could add additional complexity to the {ScheduleBatch} class, especially we need to
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# handle {filter_batch()} and {merge_batch()} cases as well.
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ret.penalizer_orchestrator = penaltylib.BatchedPenalizerOrchestrator(
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vocab_size=vocab_size,
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batch=batch,
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device=batch.device,
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Penalizers=penalizers,
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)
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# Handle logit bias but only allocate when needed
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ret.logit_bias = None
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return ret
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def __len__(self):
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return len(self.temperatures)
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def update_penalties(self):
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self.scaling_penalties = None
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self.linear_penalties = None
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for penalizer in self.penalizer_orchestrator.penalizers.values():
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if not penalizer.is_prepared():
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continue
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if isinstance(penalizer, penaltylib.BatchedRepetitionPenalizer):
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self.scaling_penalties = penalizer.cumulated_repetition_penalties
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else:
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if self.linear_penalties is None:
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bs = self.penalizer_orchestrator.batch.batch_size()
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self.linear_penalties = torch.zeros(
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(bs, self.vocab_size),
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dtype=torch.float32,
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device=self.device,
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)
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self.linear_penalties = penalizer.apply(self.linear_penalties)
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def update_regex_vocab_mask(self):
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if not self.grammars:
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self.vocab_mask = None
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self.apply_mask = None
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return
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# find a grammar from the list
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first_grammar = next(grammar for grammar in self.grammars if grammar)
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# maybe we can reuse the existing mask?
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self.vocab_mask = first_grammar.allocate_vocab_mask(
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vocab_size=self.vocab_size,
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batch_size=len(self.temperatures),
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device=self.device,
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)
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self.apply_mask = first_grammar.apply_vocab_mask # force to use static method
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# Apply the mask
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for i, grammar in enumerate(self.grammars):
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if grammar and not grammar.finished:
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grammar.fill_vocab_mask(self.vocab_mask, i)
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# Move the mask to the device if needed
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self.vocab_mask = first_grammar.move_vocab_mask(self.vocab_mask, self.device)
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def filter_batch(self, unfinished_indices: List[int], new_indices: torch.Tensor):
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self.penalizer_orchestrator.filter(unfinished_indices, new_indices)
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if self.has_custom_logit_processor:
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self._filter_batch_custom_logit_processor(unfinished_indices, new_indices)
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for item in [
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"temperatures",
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"top_ps",
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"top_ks",
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"min_ps",
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"logit_bias",
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]:
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value = getattr(self, item, None)
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if value is not None: # logit_bias can be None
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setattr(self, item, value[new_indices])
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def _filter_batch_custom_logit_processor(
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self, unfinished_indices: List[int], new_indices: torch.Tensor
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):
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"""Filter the custom logit processor and custom params"""
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self.custom_logit_processor = {
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k: (p, mask[new_indices])
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for k, (p, mask) in self.custom_logit_processor.items()
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if any(
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mask[new_indices]
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) # ignore the custom logit processor whose mask is all False
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}
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self.custom_params = [self.custom_params[i] for i in unfinished_indices]
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# If the custom logit processor is an empty dict, set the flag to False,
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# and set the custom logit processor and custom params to None.
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if len(self.custom_logit_processor) == 0:
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self.custom_logit_processor = None
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self.custom_params = None
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self.has_custom_logit_processor = False
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@staticmethod
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def merge_bias_tensor(
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lhs: torch.Tensor,
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rhs: torch.Tensor,
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bs1: int,
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bs2: int,
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device: str,
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default: int = 0,
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):
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# bias tensor can be None
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if lhs is not None or rhs is not None:
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shape, dtype = None, None
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if lhs is not None:
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shape, dtype = lhs.shape[1:], lhs.dtype
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else:
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shape, dtype = rhs.shape[1:], rhs.dtype
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with torch.dtype(dtype):
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if lhs is None:
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lhs = torch.empty((bs1, *shape), device=device).fill_(default)
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if rhs is None:
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rhs = torch.empty((bs2, *shape), device=device).fill_(default)
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return torch.cat([lhs, rhs])
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return None
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@staticmethod
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def merge_custom_logit_processor(
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lhs: Optional[Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]],
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rhs: Optional[Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]],
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bs1: int,
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bs2: int,
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device: str,
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):
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if lhs is None and rhs is None:
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return None
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lhs, rhs = lhs or {}, rhs or {}
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keys = set(lhs.keys()).union(set(rhs.keys()))
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merged_dict = {}
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for k in keys:
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# Get the logit processor object
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processor = lhs[k][0] if k in lhs else rhs[k][0]
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# Get and merge the mask tensors from the two dicts
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left_mask = (
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lhs[k][1]
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if k in lhs
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else torch.zeros(bs1, dtype=torch.bool, device=device)
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)
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right_mask = (
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rhs[k][1]
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if k in rhs
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else torch.zeros(bs2, dtype=torch.bool, device=device)
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)
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merged_dict[k] = (processor, torch.cat([left_mask, right_mask]))
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assert merged_dict[k][1].shape[0] == bs1 + bs2, (
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f"The batch size of merged mask ({merged_dict[k][1].shape[0]}) does not match "
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f"the sum of the batch sizes of the two masks ({bs1 + bs2})"
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f"\n{left_mask=}\n{right_mask=}\n{bs1=}\n{bs2=}"
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f"\n{lhs=}\n{rhs=}"
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)
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return merged_dict
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def merge_batch(self, other: "SamplingBatchInfo"):
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self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
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# Merge the logit bias tensor
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self.logit_bias = SamplingBatchInfo.merge_bias_tensor(
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self.logit_bias, other.logit_bias, len(self), len(other), self.device
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)
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# Merge the custom logit processors and custom params lists
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if self.has_custom_logit_processor or other.has_custom_logit_processor:
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# Merge the custom logit processors
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self.custom_logit_processor = (
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SamplingBatchInfo.merge_custom_logit_processor(
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self.custom_logit_processor,
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other.custom_logit_processor,
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len(self),
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len(other),
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self.device,
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)
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)
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# Merge the custom params lists
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self.custom_params = self.custom_params or [None] * len(self)
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other.custom_params = other.custom_params or [None] * len(other)
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self.custom_params.extend(other.custom_params)
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# Set the flag to True if any of the two has custom logit processor
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self.has_custom_logit_processor = True
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# Note: becasue the __len()__ operator is defined on the temperatures tensor,
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# please make sure any merge operation with len(self) or len(other) is done before
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# the merge operation of the temperatures tensor below.
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for item in [
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"temperatures",
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"top_ps",
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"top_ks",
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"min_ps",
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]:
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self_val = getattr(self, item, None)
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other_val = getattr(other, item, None)
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setattr(self, item, torch.concat([self_val, other_val]))
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self.is_all_greedy = self.is_all_greedy and other.is_all_greedy
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self.need_min_p_sampling = self.need_min_p_sampling or other.need_min_p_sampling
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def apply_logits_bias(self, logits: torch.Tensor):
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# Apply logit_bias
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if self.logit_bias is not None:
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logits.add_(self.logit_bias)
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# min-token, presence, frequency
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if self.linear_penalties is not None:
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logits.add_(self.linear_penalties)
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# repetition
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if self.scaling_penalties is not None:
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apply_scaling_penalties(logits, self.scaling_penalties)
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# Apply regex vocab_mask
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if self.vocab_mask is not None:
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self.apply_mask(logits=logits, vocab_mask=self.vocab_mask)
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