ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
from abc import ABC, abstractmethod
|
|
from typing import Callable, List
|
|
|
|
from vllm.config import SchedulerConfig
|
|
from vllm.core.scheduler import Scheduler
|
|
from vllm.engine.output_processor.stop_checker import StopChecker
|
|
from vllm.sequence import Sequence, SequenceGroup, SequenceGroupOutput
|
|
from vllm.transformers_utils.detokenizer import Detokenizer
|
|
from vllm.transformers_utils.tokenizer import AnyTokenizer
|
|
from vllm.utils import Counter
|
|
|
|
|
|
class SequenceGroupOutputProcessor(ABC):
|
|
"""Interface for logic that processes new token ids in sequence groups,
|
|
managing detokenization, stop checking, and freeing/forking sequences with
|
|
the scheduler.
|
|
|
|
This is highly coupled with the LLMEngine and should be seen as an extension
|
|
of it. The logic is separated to simplify the LLMEngine class and allow
|
|
separate implementations for single-step decoding (which supports beam
|
|
search sequence forking) and multi-step decoding (which does not support
|
|
beam search, but does support speculative decoding).
|
|
"""
|
|
|
|
@staticmethod
|
|
def create_output_processor(
|
|
scheduler_config: SchedulerConfig,
|
|
detokenizer: Detokenizer,
|
|
scheduler: List[Scheduler],
|
|
seq_counter: Counter,
|
|
get_tokenizer_for_seq: Callable[[Sequence], AnyTokenizer],
|
|
stop_checker: "StopChecker",
|
|
):
|
|
"""Create an output processor.
|
|
|
|
This returns a single-step output processor if num_lookahead_slots is
|
|
zero, else returns a multi-step output processor.
|
|
"""
|
|
if scheduler_config.num_lookahead_slots == 0:
|
|
# Importing here to avoid cycle.
|
|
from vllm.engine.output_processor.single_step import (
|
|
SingleStepOutputProcessor)
|
|
return SingleStepOutputProcessor(scheduler_config, detokenizer,
|
|
scheduler, seq_counter,
|
|
stop_checker)
|
|
else:
|
|
# Importing here to avoid cycle.
|
|
from vllm.engine.output_processor.multi_step import (
|
|
MultiStepOutputProcessor)
|
|
return MultiStepOutputProcessor(
|
|
detokenizer,
|
|
scheduler,
|
|
seq_counter,
|
|
get_tokenizer_for_seq,
|
|
stop_checker,
|
|
)
|
|
|
|
@abstractmethod
|
|
def process_outputs(self, sequence_group: SequenceGroup,
|
|
outputs: List[SequenceGroupOutput],
|
|
is_async: bool) -> None:
|
|
"""Process new token ids for the sequence group. Handles logic such as
|
|
detokenization, stop checking, and freeing/forking sequences in the
|
|
scheduler.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def process_prompt_logprob(self, seq_group: SequenceGroup,
|
|
outputs: List[SequenceGroupOutput]) -> None:
|
|
"""Update prompt logprobs received from outputs to seq_group."""
|
|
pass
|