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Unverified Commit 71683ca6 authored by Woosuk Kwon's avatar Woosuk Kwon Committed by GitHub
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[V0 Deprecation] Remove multi-step scheduling (#22138) 


Signed-off-by: default avatarWoosuk Kwon <woosuk.kwon@berkeley.edu>
Signed-off-by: default avatarWoosuk Kwon <woosuk@thinkingmachines.ai>
parent e1885929
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Showing with 50 additions and 1882 deletions
vllm/config/__init__.py
View file @ 71683ca6
......@@ -3779,8 +3779,6 @@ class VllmConfig:
f"observability_config={self.observability_config!r}, "
f"seed={self.model_config.seed}, "
f"served_model_name={self.model_config.served_model_name}, "
f"num_scheduler_steps={self.scheduler_config.num_scheduler_steps}, "
f"multi_step_stream_outputs={self.scheduler_config.multi_step_stream_outputs}, " # noqa
f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, " # noqa
f"use_async_output_proc={self.model_config.use_async_output_proc}, "
......
vllm/core/scheduler.py
View file @ 71683ca6
......@@ -929,8 +929,7 @@ class Scheduler:
)
def _get_prompt_limit(self, seq_group: SequenceGroup) -> int:
if (self.scheduler_config.chunked_prefill_enabled
and not self.scheduler_config.is_multi_step):
if self.scheduler_config.chunked_prefill_enabled:
prompt_limit = self.scheduler_config.max_model_len
else:
prompt_limit = min(
......@@ -1114,9 +1113,6 @@ class Scheduler:
continue
num_lookahead_slots: int = 0
if self.scheduler_config.is_multi_step and enable_chunking:
num_lookahead_slots = self._get_num_lookahead_slots(
True, enable_chunking)
# If the sequence group cannot be allocated, stop.
can_allocate = self.block_manager.can_allocate(
......@@ -1195,24 +1191,6 @@ class Scheduler:
partial_prefill_metadata.maybe_increment_partial_prefills(
seq_group)
if enable_chunking and self.scheduler_config.is_multi_step:
blocks_to_copy: List[Tuple[int, int]] = []
# init_multi_step_from_lookahead_slots happens in append_slots
self._append_slots(seq_group, blocks_to_copy, enable_chunking)
# This assert will trip when a copy-on-write happens. This is
# not a concern as the very first sequence-group block
# allocation happens above. Still, we have the assert to
# catch any edge-cases.
assert not blocks_to_copy
else:
seq_group.init_multi_step_from_lookahead_slots(
num_lookahead_slots,
num_scheduler_steps=self.scheduler_config.
num_scheduler_steps,
is_multi_step=self.scheduler_config.is_multi_step,
enable_chunking=enable_chunking,
)
seq_groups.append(
ScheduledSequenceGroup(seq_group=seq_group,
token_chunk_size=num_new_tokens))
......@@ -1453,14 +1431,6 @@ class Scheduler:
num_prefill_groups = (len(prefills.seq_groups) +
len(swapped_in.prefill_seq_groups) +
len(running_scheduled.prefill_seq_groups))
# If all prompts, then we set num_lookahead_slots to 0
# this allows us to go through the `no_spec` path in
# `spec_decode_worker.py`
all_prefills = len(scheduled_seq_groups) == num_prefill_groups
num_lookahead_slots = (0 if
(all_prefills
and not self.scheduler_config.is_multi_step)
else running_scheduled.num_lookahead_slots)
return SchedulerOutputs(
scheduled_seq_groups=scheduled_seq_groups,
num_prefill_groups=num_prefill_groups,
......@@ -1472,7 +1442,7 @@ class Scheduler:
swapped_in.blocks_to_copy,
ignored_seq_groups=prefills.ignored_seq_groups +
swapped_in.infeasible_seq_groups,
num_lookahead_slots=num_lookahead_slots,
num_lookahead_slots=0,
running_queue_size=len(self.running),
preempted=(len(running_scheduled.preempted) +
len(running_scheduled.swapped_out)),
......@@ -1516,11 +1486,6 @@ class Scheduler:
num_lookahead_slots = self._get_num_lookahead_slots(
is_prefill, enable_chunking)
if is_prefill and num_lookahead_slots > 0:
# Appending prefill slots only happens multi-step and
# chunked-prefill are enabled together.
assert self.scheduler_config.is_multi_step and enable_chunking
return self.block_manager.can_append_slots(
seq_group=seq_group, num_lookahead_slots=num_lookahead_slots)
......@@ -1776,19 +1741,7 @@ class Scheduler:
num_lookahead_slots: int = self._get_num_lookahead_slots(
is_prefill, enable_chunking)
seq_group.init_multi_step_from_lookahead_slots(
num_lookahead_slots,
num_scheduler_steps=self.scheduler_config.num_scheduler_steps,
is_multi_step=self.scheduler_config.is_multi_step,
enable_chunking=enable_chunking,
)
seq_status: Optional[SequenceStatus] = SequenceStatus.RUNNING
if self.scheduler_config.is_multi_step and enable_chunking:
# In multi-step chunked-prefill any sequence type can have
# slots appended.
seq_status = None
for seq in seq_group.get_seqs(status=seq_status):
cows = self.block_manager.append_slots(seq, num_lookahead_slots)
if len(cows) > 0:
......@@ -1904,30 +1857,9 @@ class Scheduler:
"""The number of slots to allocate per sequence per step, beyond known
token ids. Speculative decoding uses these slots to store KV activations
of tokens which may or may not be accepted.
Speculative decoding does not yet support prefill, so we do not perform
lookahead allocation for prefill.
When chunking is enabled with multi-step, we allocate lookahead slots
for the prefills for when the prefills turn into decodes in the first
step.
"""
if is_prefill:
if self.scheduler_config.is_multi_step and enable_chunking:
# num_lookahead_slots was introduced in the context of decodes,
# in Speculative Decoding.
# When the num_scheduler_steps is 8, say, then the
# num_lookahead_slots is 7. Meaning, we are doing a 1-step of
# decode anyways and we wish to do 7 more.
#
# "lookaheads" for prefills, is introduced in support for
# Chunked-Prefill in Multi-Step.
return self.scheduler_config.num_lookahead_slots + 1
else:
return 0
return self.scheduler_config.num_lookahead_slots
def _get_num_new_uncached_and_cached_tokens(
self,
seq_group: SequenceGroup,
......@@ -2068,24 +2000,6 @@ class Scheduler:
The number of new tokens to schedule after chunking.
"""
remaining_token_budget = budget.remaining_token_budget()
if scheduler_config.is_multi_step:
# The current multi-step + chunked prefill capability does
# not actually support chunking prompts.
#
# Therefore, `num_new_tokens` is computed in the same fashion
# for both multi-step+chunked-prefill &
# multi-step+chunked-prefill+APC
#
# Prompts with more tokens than the current remaining budget
# are postponed to future scheduler steps
if num_new_tokens > prompt_limit:
# If the seq_group is in prompt-stage, pass the
# num_new_tokens as-is so the caller can ignore
# the sequence.
return num_new_tokens
return 0 if num_new_tokens > \
remaining_token_budget else num_new_tokens
# Get the number of tokens to allocate to this prefill slot
prefill_slot_budget = (
......
vllm/engine/arg_utils.py
View file @ 71683ca6
......@@ -362,8 +362,6 @@ class EngineArgs:
lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
num_gpu_blocks_override: Optional[
int] = CacheConfig.num_gpu_blocks_override
......@@ -799,11 +797,8 @@ class EngineArgs:
**scheduler_kwargs["delay_factor"])
scheduler_group.add_argument("--preemption-mode",
**scheduler_kwargs["preemption_mode"])
scheduler_group.add_argument("--num-scheduler-steps",
**scheduler_kwargs["num_scheduler_steps"])
scheduler_group.add_argument(
"--multi-step-stream-outputs",
**scheduler_kwargs["multi_step_stream_outputs"])
# multi-step scheduling has been removed; corresponding arguments
# are no longer supported.
scheduler_group.add_argument("--scheduling-policy",
**scheduler_kwargs["policy"])
scheduler_group.add_argument(
......@@ -1257,28 +1252,11 @@ class EngineArgs:
disable_log_stats=self.disable_log_stats,
)
# Reminder: Please update docs/features/compatibility_matrix.md
# If the feature combo become valid
if self.num_scheduler_steps > 1:
# make sure num_lookahead_slots is set appropriately depending on
# whether speculative decoding is enabled
num_lookahead_slots = self.num_lookahead_slots
if speculative_config is not None:
raise ValueError("Speculative decoding is not supported with "
"multi-step (--num-scheduler-steps > 1)")
if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
raise ValueError("Multi-Step Chunked-Prefill is not supported "
"for pipeline-parallel-size > 1")
if current_platform.is_cpu():
logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
"currently not supported for CPUs and has been "
"disabled.")
self.num_scheduler_steps = 1
# make sure num_lookahead_slots is set the higher value depending on
# if we are using speculative decoding or multi-step
num_lookahead_slots = max(self.num_lookahead_slots,
self.num_scheduler_steps - 1)
num_lookahead_slots = num_lookahead_slots \
if speculative_config is None \
else speculative_config.num_lookahead_slots
num_lookahead_slots = speculative_config.num_lookahead_slots
scheduler_config = SchedulerConfig(
runner_type=model_config.runner_type,
......@@ -1292,8 +1270,6 @@ class EngineArgs:
disable_chunked_mm_input=self.disable_chunked_mm_input,
is_multimodal_model=model_config.is_multimodal_model,
preemption_mode=self.preemption_mode,
num_scheduler_steps=self.num_scheduler_steps,
multi_step_stream_outputs=self.multi_step_stream_outputs,
send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
and parallel_config.use_ray),
policy=self.scheduling_policy,
......@@ -1392,11 +1368,6 @@ class EngineArgs:
recommend_to_remove=True)
return False
if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
_raise_or_fallback(feature_name="--num-scheduler-steps",
recommend_to_remove=True)
return False
if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
_raise_or_fallback(feature_name="--scheduler-delay-factor",
recommend_to_remove=True)
......
vllm/engine/async_llm_engine.py
View file @ 71683ca6
......@@ -15,7 +15,7 @@ from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
from vllm.core.scheduler import SchedulerOutputs
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_timeout import asyncio_timeout
from vllm.engine.llm_engine import LLMEngine, SchedulerOutputState
from vllm.engine.llm_engine import LLMEngine
from vllm.engine.metrics_types import StatLoggerBase
from vllm.engine.protocol import EngineClient
from vllm.executor.executor_base import ExecutorBase
......@@ -308,13 +308,6 @@ class _AsyncLLMEngine(LLMEngine):
if not allow_async_output_proc and len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
if (self.scheduler_config.is_multi_step
and scheduler_outputs.num_lookahead_slots > 0):
# cache the scheduler outputs for the next iteration if we have
# lookahead slots
self._cache_scheduler_outputs_for_multi_step(
virtual_engine, seq_group_metadata_list, scheduler_outputs,
allow_async_output_proc)
else:
finished_requests_ids = list()
......@@ -351,29 +344,14 @@ class _AsyncLLMEngine(LLMEngine):
outputs = await self.model_executor.execute_model_async(
execute_model_req)
# we need to do this here so that last step's sampled_token_ids can
# be passed to the next iteration for PP.
if self.scheduler_config.is_multi_step:
self._update_cached_scheduler_output(virtual_engine, outputs)
else:
if len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
outputs = []
# Finish the current step for all the sequence groups.
if self.scheduler_config.is_multi_step:
for seq_group in seq_group_metadata_list:
seq_group.finish_step()
if not self._has_remaining_steps(seq_group_metadata_list):
# Clear the cache if we have finished all the steps
if self.scheduler_config.is_multi_step:
self.cached_scheduler_outputs[
virtual_engine] = SchedulerOutputState()
# is_first_step_output is True only when the num_steps of all
# the sequences are 1. When the num_steps > 1,
# multi_step_model_runner does the first-step output append.
# the sequences are 1.
is_first_step_output: bool = False if not seq_group_metadata_list \
else seq_group_metadata_list[0].state.num_steps == 1
......
vllm/engine/llm_engine.py
View file @ 71683ca6
......@@ -25,7 +25,6 @@ from vllm.engine.metrics_types import StatLoggerBase, Stats
from vllm.engine.output_processor.interfaces import (
SequenceGroupOutputProcessor)
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.engine.output_processor.util import create_output_by_sequence_group
from vllm.entrypoints.openai.logits_processors import (
get_logits_processors as get_openai_logits_processors)
from vllm.executor.executor_base import ExecutorBase
......@@ -91,7 +90,7 @@ class OutputData(NamedTuple):
class SchedulerContext:
def __init__(self, multi_step_stream_outputs: bool = False):
def __init__(self) -> None:
self.output_queue: Deque[OutputData] = deque()
self.request_outputs: List[Union[RequestOutput,
PoolingRequestOutput]] = []
......@@ -99,8 +98,6 @@ class SchedulerContext:
List[SequenceGroupMetadata]] = None
self.scheduler_outputs: Optional[SchedulerOutputs] = None
self.multi_step_stream_outputs: bool = multi_step_stream_outputs
def append_output(self, outputs: List[SamplerOutput],
seq_group_metadata_list: List[SequenceGroupMetadata],
scheduler_outputs: SchedulerOutputs, is_async: bool,
......@@ -303,8 +300,7 @@ class LLMEngine:
]
self.scheduler_contexts = [
SchedulerContext(multi_step_stream_outputs=self.scheduler_config.
multi_step_stream_outputs)
SchedulerContext()
for _ in range(self.parallel_config.pipeline_parallel_size)
]
......@@ -683,8 +679,7 @@ class LLMEngine:
"Priority scheduling is not enabled.")
if isinstance(params, SamplingParams) \
and params.logits_processors \
and self.scheduler_config.num_scheduler_steps > 1:
and params.logits_processors:
raise ValueError(
"Logits processors are not supported in multi-step decoding")
......@@ -868,45 +863,6 @@ class LLMEngine:
return
def _update_num_computed_tokens_for_multi_step_prefill(
self, seq_group: SequenceGroup,
seq_group_meta: SequenceGroupMetadata,
is_first_step_output: Optional[bool]):
"""
This function updates num_computed_tokens for prompt sequences
when Multi-Step is enabled.
seq_group: SequenceGroup to update the num_computed_tokens for.
seq_group_meta: Metadata of the given SequenceGroup.
is_first_step_output: Optional[bool] -
When available, is_first_step_output indicates if the appended
output token is the output of the first-step in multi-step.
A value of None indicates that outputs from all steps in
in multi-step are submitted in a single burst.
"""
assert self.scheduler_config.is_multi_step
if not seq_group_meta.is_prompt:
# num_computed_token updates for multi-step decodes happen after
# the tokens are appended to the sequence.
return
do_update: bool = False
if self.scheduler_config.chunked_prefill_enabled:
# In multi-step + chunked-prefill case, the prompt sequences
# that are scheduled are fully processed in the first step.
do_update = is_first_step_output is None or is_first_step_output
else:
# Normal multi-step decoding case. In this case prompt-sequences
# are actually single-stepped. Always update in this case.
assert seq_group.state.num_steps == 1
do_update = True
if do_update:
seq_group.update_num_computed_tokens(
seq_group_meta.token_chunk_size)
def _process_model_outputs(self,
ctx: SchedulerContext,
request_id: Optional[str] = None) -> None:
......@@ -939,32 +895,7 @@ class LLMEngine:
has_multiple_outputs: bool = len(outputs) > 1
outputs_by_sequence_group: List[List[SequenceGroupOutput]]
if has_multiple_outputs:
assert self.scheduler_config.is_multi_step or \
self.speculative_config
# Organize outputs by [step][sequence group] instead of
# [sequence group][step].
if self.scheduler_config.is_multi_step:
outputs_by_sequence_group = create_output_by_sequence_group(
outputs, len(seq_group_metadata_list))
elif self.speculative_config:
# Decodes are multi-steps while prefills are not, outputting at
# most 1 token. Separate them so that we can trigger chunk
# processing without having to pad or copy over prompts K times
# to match decodes structure (costly with prompt_logprobs).
num_prefills = sum(sg.is_prompt
for sg in seq_group_metadata_list)
prefills, decodes = outputs[:num_prefills], outputs[
num_prefills:]
outputs_by_sequence_group = create_output_by_sequence_group(
decodes,
num_seq_groups=len(seq_group_metadata_list) - num_prefills)
outputs_by_sequence_group = [p.outputs for p in prefills
] + outputs_by_sequence_group
# We have outputs for multiple steps submitted in a single burst,
# so invalidate is_first_step_output.
is_first_step_output = None
else:
assert not has_multiple_outputs
outputs_by_sequence_group = outputs
# Determine the requests we need to operate on
......@@ -1006,11 +937,6 @@ class LLMEngine:
output = [outputs_by_sequence_group[0][i]]
if not is_async:
if self.scheduler_config.is_multi_step:
# Updates happen only if the sequence is prefill
self._update_num_computed_tokens_for_multi_step_prefill(
seq_group, seq_group_meta, is_first_step_output)
else:
seq_group.update_num_computed_tokens(
seq_group_meta.token_chunk_size or 0)
......@@ -1074,15 +1000,6 @@ class LLMEngine:
for scheduler in self.scheduler:
scheduler.free_finished_seq_groups()
# For multi-step without streaming, don't create outputs each iteration
if not is_last_step and not ctx.multi_step_stream_outputs:
# Immediately process request outputs here (if callback is given)
if (finished_now
and self.process_request_outputs_callback is not None):
self.process_request_outputs_callback(ctx.request_outputs)
ctx.request_outputs.clear()
return
# Create the outputs
for i in indices:
if i in skip or i in finished_before or i in finished_now:
......@@ -1101,13 +1018,7 @@ class LLMEngine:
if request_output:
ctx.request_outputs.append(request_output)
# For multi-step with streaming, create outputs each iteration
if not is_last_step and ctx.multi_step_stream_outputs:
# Immediately process request outputs here (if callback is given)
if self.process_request_outputs_callback is not None:
self.process_request_outputs_callback(ctx.request_outputs)
ctx.request_outputs.clear()
return
# Create outputs only after processing the scheduler's results
for seq_group in scheduler_outputs.ignored_seq_groups:
params = seq_group.sampling_params
......@@ -1157,12 +1068,6 @@ class LLMEngine:
if seq_group.is_finished():
continue
if self.scheduler_config.is_multi_step:
# Updates happen only if the sequence is prefill
self._update_num_computed_tokens_for_multi_step_prefill(
seq_group, seq_group_metadata,
seq_group.state.num_steps == 1)
else:
token_chunk_size = (seq_group_metadata.token_chunk_size
if seq_group_metadata.token_chunk_size
is not None else 0)
......@@ -1177,14 +1082,6 @@ class LLMEngine:
assert len(seq_group.seqs) == 1
seq = seq_group.seqs[0]
if self.scheduler_config.is_multi_step:
is_prefill_append = seq.data.get_num_uncomputed_tokens(
) == 0
seq.append_token_id(sample.output_token, sample.logprobs,
sample.output_embed)
if not is_prefill_append:
seq_group.update_num_computed_tokens(1)
else:
seq.append_token_id(sample.output_token, sample.logprobs,
sample.output_embed)
......@@ -1289,13 +1186,6 @@ class LLMEngine:
if not allow_async_output_proc and len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
if (self.scheduler_config.is_multi_step
and scheduler_outputs.num_lookahead_slots > 0):
# cache the scheduler outputs for the next iteration if we have
# lookahead slots
self._cache_scheduler_outputs_for_multi_step(
virtual_engine, seq_group_metadata_list, scheduler_outputs,
allow_async_output_proc)
else:
finished_requests_ids = list()
......@@ -1345,10 +1235,6 @@ class LLMEngine:
# Raise so the caller is notified that this request failed
raise
# We need to do this here so that last step's sampled_token_ids can
# be passed to the next iteration for PP.
if self.scheduler_config.is_multi_step:
self._update_cached_scheduler_output(virtual_engine, outputs)
else:
# Nothing scheduled => If there is pending async postprocessor,
# then finish it here.
......@@ -1357,19 +1243,9 @@ class LLMEngine:
# No outputs in this case
outputs = []
# Finish the current step for all the sequence groups.
if self.scheduler_config.is_multi_step:
for seq_group in seq_group_metadata_list:
seq_group.finish_step()
if not self._has_remaining_steps(seq_group_metadata_list):
# clear the cache if we have finished all the steps.
if self.scheduler_config.is_multi_step:
self.cached_scheduler_outputs[0] = SchedulerOutputState()
# is_first_step_output is True only when the num_steps of all
# the sequences are 1. When the num_steps > 1,
# multi_step_model_runner does the first-step output append.
# the sequences are 1.
is_first_step_output: bool = False if not seq_group_metadata_list \
else seq_group_metadata_list[0].state.num_steps == 1
......@@ -1453,23 +1329,8 @@ class LLMEngine:
def _has_remaining_steps(
self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
) -> bool:
if (not self.scheduler_config.is_multi_step
or not seq_group_metadata_list):
return False
# TODO(will) this is a sanity check for nowto make sure that all the
# seqs are on the same steps. Eventually we will want to do some sort of
# dynamic scheduling when doing multi-step decoding.
ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps
if any([
seq_group.state.remaining_steps != ref_remaining_steps
for seq_group in seq_group_metadata_list[1:]
]):
raise AssertionError("All running sequence groups should "
"have the same remaining steps.")
return ref_remaining_steps > 0
def _cache_scheduler_outputs_for_multi_step(
self, virtual_engine: int,
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
......@@ -1497,13 +1358,6 @@ class LLMEngine:
def _get_last_sampled_token_ids(
self, virtual_engine: int) -> Optional[torch.Tensor]:
cached_last_output = self.cached_scheduler_outputs[
virtual_engine].last_output
if (self.scheduler_config.is_multi_step
and self.parallel_config.pipeline_parallel_size > 1
and cached_last_output is not None
and cached_last_output.sampled_token_ids_cpu is not None):
return cached_last_output.sampled_token_ids_cpu
return None
def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
......
vllm/engine/output_processor/interfaces.py
View file @ 71683ca6
......@@ -36,27 +36,13 @@ class SequenceGroupOutputProcessor(ABC):
):
"""Create an output processor.
This returns a single-step output processor if num_lookahead_slots is
zero, else returns a multi-step output processor.
Multi-step scheduling is no longer supported. Always return a
single-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,
)
scheduler, seq_counter, stop_checker)
@abstractmethod
def process_outputs(self, sequence_group: SequenceGroup,
......
vllm/engine/output_processor/multi_step.py deleted 100644 → 0
View file @ e1885929
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import functools
from typing import Callable, List, cast
from vllm.core.scheduler import Scheduler
from vllm.engine.output_processor.interfaces import (
SequenceGroupOutputProcessor)
from vllm.engine.output_processor.single_step import (
single_step_process_prompt_logprob)
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.logger import init_logger
from vllm.sampling_params import SamplingParams
from vllm.sequence import (VLLM_INVALID_TOKEN_ID,
CompletionSequenceGroupOutput, Sequence,
SequenceGroup, SequenceGroupOutput, SequenceOutput,
SequenceStatus)
from vllm.transformers_utils.detokenizer import Detokenizer
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import Counter
logger = init_logger(__name__)
class MultiStepOutputProcessor(SequenceGroupOutputProcessor):
"""SequenceGroupOutputProcessor which handles logic related to
detokenization and stopping conditions. It specializes to "multi-step
decoding", where vLLM's worker may generate multiple tokens per invocation.
This is currently mutually exclusive with advanced sampling techniques like
beam search, which motivates the separation of this logic from the single
step output processor.
This class is responsible for things such as correctly appending all new
token ids to their sequence, detokenizing new token ids, truncating new
output tokens after an eos token, and correctly handling the case where the
number of new output tokens per sequence differs in a single batch.
"""
def __init__(
self,
detokenizer: Detokenizer,
scheduler: List[Scheduler],
seq_counter: Counter,
get_tokenizer_for_seq: Callable[[Sequence], AnyTokenizer],
stop_checker: StopChecker,
):
self.detokenizer = detokenizer
self.scheduler = scheduler
self.seq_counter = seq_counter
self.get_tokenizer_for_seq = get_tokenizer_for_seq
self.stop_checker = stop_checker
def process_prompt_logprob(self, seq_group: SequenceGroup,
outputs: List[SequenceGroupOutput]) -> None:
"""Process prompt logprobs associated with each step of a multi-step-
scheduled computation.
Args:
seq_group: the outputs are associated with this
[`SequenceGroup`][vllm.sequence.SequenceGroup]
outputs: the
[`SequenceGroupOutput`][vllm.sequence.SequenceGroupOutput]s
for all scheduler steps
"""
for output in outputs:
# Concatenate single-step prompt logprob processing results.
assert isinstance(output, CompletionSequenceGroupOutput)
single_step_process_prompt_logprob(self, seq_group, output)
@staticmethod
@functools.lru_cache
def _log_prompt_logprob_unsupported_warning_once():
# Reminder: Please update docs/features/compatibility_matrix.md
# If the feature combo become valid
logger.warning(
"Prompt logprob is not supported by multi step workers. "
"(e.g., speculative decode uses multi step workers).")
def process_outputs(self,
sequence_group: SequenceGroup,
outputs: List[SequenceGroupOutput],
is_async: bool = False) -> None:
"""Append new tokens in the outputs to sequences in the sequence group.
This only supports sequence groups of size 1. It supports greater than
one new token per sequence.
This applies logic like stop condition checking and detokenization.
It also handles cases where there are tokens emitted after
the EOS token.
is_async - Indicates whether this postprocessor runs in
parallel with the GPU forward pass and is processing
tokens from the previous step. If this is true, then
no tokens need to be appended since it is already done
externally (before the next schedule() call)
"""
# Sequences can be in RUNNING or FINISHED_ABORTED state
# once scheduled, as a sequence is moved to FINISHED_ABORTED
# if a client disconnects from the api server.
seqs = sequence_group.get_seqs(status=SequenceStatus.RUNNING)
if seqs is None:
seqs = sequence_group.get_seqs(
status=SequenceStatus.FINISHED_ABORTED)
assert seqs, "Expected RUNNING or FINISHED_ABORTED sequences"
assert len(seqs) == 1, (
"Beam search not supported in multi-step decoding.")
seq = seqs[0]
seq_id = seq.seq_id
# This method is defined in the more generic
# SequenceGroupOutputProcessor, but here we assume that the outputs are
# of a more specific type.
assert all([
isinstance(output, CompletionSequenceGroupOutput)
for output in outputs
])
compl_outputs = cast(List[CompletionSequenceGroupOutput], outputs)
assert all([
seq_id == output.samples[0].parent_seq_id
for output in compl_outputs
])
if is_async:
# Async case: We process tokens one by one. Here, we know the token
# was already appended, so we only need to do the rest of the
# postprocessor: Detokenization + stopping logic
self._process_decode_and_stop(seq, sequence_group.sampling_params)
else:
# Standard multi-step case
# Since there's only one sequence per sequence group,
# we can take the first sample.
samples = [output.samples[0] for output in compl_outputs]
# entries in sample tokens may be invalid (eg. due to spec decode
# rejecting tokens).
valid_samples = [
sample for sample in samples
if sample.output_token != VLLM_INVALID_TOKEN_ID
]
# When both spec-decode and pre-fill chunking are enabled, we
# don't have guaranteed samples here (e.g. all -1s).
if valid_samples:
self._process_seq_outputs(seq, valid_samples,
sequence_group.sampling_params)
def _process_decode_and_stop(self, seq: Sequence,
sampling_params: SamplingParams) -> None:
new_char_count = 0
if sampling_params.detokenize and self.detokenizer:
new_char_count = self.detokenizer.decode_sequence_inplace(
seq, sampling_params)
# TODO(sang): Support lora.
self.stop_checker.maybe_stop_sequence(
seq,
new_char_count=new_char_count,
sampling_params=sampling_params,
)
def _process_seq_outputs(self, seq: Sequence,
valid_samples: List[SequenceOutput],
sampling_params: SamplingParams) -> None:
output_token_ids = [sample.output_token for sample in valid_samples]
output_logprobs = [sample.logprobs for sample in valid_samples]
output_embeds = [sample.output_embed for sample in valid_samples]
# Truncate to max_tokens if necessary.
remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() +
len(output_token_ids))
if remaining_tokens < 0:
output_token_ids = output_token_ids[:remaining_tokens]
# Truncate any tokens after EOS. This is required as spec decode
# generates a fixed number of tokens without evaluating stopping
# conditions within the block. This can cause an eos token to be
# unintentionally ignored.
if not sampling_params.ignore_eos and self.detokenizer:
eos_token_id = self.get_tokenizer_for_seq(seq).eos_token_id
# Avoiding .index calls as exception throwing in the happy path
# is expensive.
for i in range(len(output_token_ids)):
if output_token_ids[i] == eos_token_id:
output_token_ids = output_token_ids[:i + 1]
break
is_prefill_sampled_token = seq.data.get_num_uncomputed_tokens() == 0
# Incrementally append tokens to the sequence, as if we had only one new
# token.
for output_token_id, output_logprob, output_embed in zip(
output_token_ids, output_logprobs, output_embeds):
seq.append_token_id(
token_id=output_token_id,
logprobs=output_logprob,
token_embed=output_embed,
)
if is_prefill_sampled_token:
is_prefill_sampled_token = False
else:
# Update num_computed_tokens iff the sampled token is not from
# a prefill step.
seq.data.update_num_computed_tokens(1)
self._process_decode_and_stop(seq, sampling_params)
if seq.is_finished():
break
vllm/platforms/cuda.py
View file @ 71683ca6
......@@ -118,20 +118,10 @@ class CudaPlatformBase(Platform):
@classmethod
def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
parallel_config = vllm_config.parallel_config
scheduler_config = vllm_config.scheduler_config
model_config = vllm_config.model_config
if parallel_config.worker_cls == "auto":
if scheduler_config.is_multi_step:
if envs.VLLM_USE_V1:
raise NotImplementedError(
"Multi-step scheduling is not supported (and not "
"needed) on vLLM V1. Please launch without "
"--num-scheduler-steps.")
else:
parallel_config.worker_cls = \
"vllm.worker.multi_step_worker.MultiStepWorker"
elif vllm_config.speculative_config:
if vllm_config.speculative_config:
if not envs.VLLM_USE_V1:
raise NotImplementedError(
"Speculative decoding is not supported on vLLM V0.")
......
vllm/platforms/rocm.py
View file @ 71683ca6
......@@ -327,18 +327,8 @@ class RocmPlatform(Platform):
cache_config.block_size = 16
parallel_config = vllm_config.parallel_config
scheduler_config = vllm_config.scheduler_config
if parallel_config.worker_cls == "auto":
if scheduler_config.is_multi_step:
if envs.VLLM_USE_V1:
raise NotImplementedError(
"Multi-step scheduling is not supported (and not "
"needed) on vLLM V1. Please launch without "
"--num-scheduler-steps.")
else:
parallel_config.worker_cls = \
"vllm.worker.multi_step_worker.MultiStepWorker"
elif vllm_config.speculative_config:
if vllm_config.speculative_config:
if not envs.VLLM_USE_V1:
raise NotImplementedError(
"Speculative decoding is not supported on vLLM V0.")
......
vllm/platforms/tpu.py
View file @ 71683ca6
......@@ -133,11 +133,6 @@ class TpuPlatform(Platform):
parallel_config = vllm_config.parallel_config
scheduler_config = vllm_config.scheduler_config
if parallel_config.worker_cls == "auto":
if scheduler_config.is_multi_step:
raise NotImplementedError(
"Multi-step scheduling is not supported (and not "
"needed) on vLLM V1. Please launch without "
"--num-scheduler-steps.")
parallel_config.worker_cls = "vllm.v1.worker.tpu_worker.TPUWorker"
assert not vllm_config.speculative_config, (
......
vllm/sequence.py
View file @ 71683ca6
......@@ -794,35 +794,6 @@ class SequenceGroup:
def lora_int_id(self) -> int:
return self.lora_request.lora_int_id if self.lora_request else 0
def init_multi_step(self, num_steps: int) -> None:
self.state.num_steps = num_steps
self.state.current_step = 0
def init_multi_step_from_lookahead_slots(self, num_lookahead_slots: int,
num_scheduler_steps: int,
is_multi_step: bool,
enable_chunking: bool) -> None:
if not is_multi_step:
self.init_multi_step(num_steps=num_scheduler_steps)
return
# Multi-Step case
is_prefill = self.is_prefill()
# The asserts below reflect the expectations of the current system.
if is_prefill and enable_chunking:
assert num_lookahead_slots == num_scheduler_steps
self.init_multi_step(num_steps=num_lookahead_slots)
else:
is_decode: bool = not is_prefill
# If it is a prefill, num_lookahead_slots must be 0
assert num_lookahead_slots == 0 or is_decode
# If it is a decode, num_lookahead_slots + 1 must match
# the scheduler steps.
assert num_lookahead_slots + 1 == num_scheduler_steps or is_prefill
self.init_multi_step(num_steps=num_lookahead_slots + 1)
def set_last_token_time(self, now: float) -> None:
"""Sets the last token time for Request level timings."""
# If still in prefill phase, assertion fails.
......@@ -1367,15 +1338,6 @@ class ExecuteModelRequest(
# Async callback
async_callback: Optional[Callable] = None
@property
def is_first_multi_step(self) -> bool:
# TODO(will) make this be able to handle batches with variable number of
# steps
assert len(self.seq_group_metadata_list) > 0
first_seq_group = self.seq_group_metadata_list[0]
assert first_seq_group.state is not None
return first_seq_group.state.current_step == 0
@property
def is_last_step(self) -> bool:
# TODO(will) make this be able to handle batches with variable number of
......
vllm/worker/model_runner.py
View file @ 71683ca6
......@@ -508,8 +508,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
if inter_data.is_prompt:
context_len = seq_data.get_num_computed_tokens()
seq_len = min(seq_len, context_len + token_chunk_size)
elif self.runner.scheduler_config.is_multi_step or \
self.runner.model_config.is_encoder_decoder:
elif self.runner.model_config.is_encoder_decoder:
context_len = seq_len - 1
else:
context_len = seq_data.get_num_computed_tokens()
......@@ -778,9 +777,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
int: Returns the determined number of padding sequences. If
CUDA graphs is not viable, returns -1.
"""
is_mscp: bool = self.runner.scheduler_config.is_multi_step and \
self.runner.scheduler_config.chunked_prefill_enabled
decode_only = self.decode_only or is_mscp
decode_only = self.decode_only
if not decode_only:
# Early exit so we can treat num_seqs as the batch_size below.
return -1
......
vllm/worker/multi_step_model_runner.py deleted 100644 → 0
View file @ e1885929
This diff is collapsed.
vllm/worker/multi_step_neuron_model_runner.py deleted 100644 → 0
View file @ e1885929
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from importlib.util import find_spec
from typing import List, Optional
import torch
from vllm.config import VllmConfig
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.multimodal import MultiModalKwargs
from vllm.sequence import IntermediateTensors
from vllm.worker.neuron_model_runner import (ModelInputForNeuron,
NeuronModelRunner)
class MultiStepNeuronModelRunner(NeuronModelRunner):
"""A model runner for multi step decoding using the transformers_neuronx
framework"""
def __init__(
self,
vllm_config: VllmConfig,
):
super().__init__(vllm_config)
self.speculation_config = self.speculative_config
from transformers_neuronx.config import GenerationConfig
self.speculation_config.draft_model_config.neuron_sampling_params = (
GenerationConfig(
max_length=self.scheduler_config.max_model_len,
do_sample=True,
per_batch_line=True,
top_k=[self._MAX_NEURON_SAMPLING_TOP_K] \
* self.scheduler_config.max_num_seqs,
top_p=[1.0] * self.scheduler_config.max_num_seqs,
temperature=[1.0] * self.scheduler_config.max_num_seqs,
dynamic=True,
global_top_k=self._MAX_NEURON_SAMPLING_TOP_K
))
def load_model(self) -> None:
if find_spec("transformers_neuronx") is not None:
from vllm.model_executor.model_loader.neuron import (
get_neuron_eagle_speculation_model,
get_neuron_speculation_model)
if self.speculation_config.speculative_token_tree is not None:
self.model = get_neuron_eagle_speculation_model(
self.model_config,
parallel_config=self.parallel_config,
scheduler_config=self.scheduler_config,
speculation_config=self.speculation_config)
else:
self.model = get_neuron_speculation_model(
self.model_config,
parallel_config=self.parallel_config,
scheduler_config=self.scheduler_config,
speculation_config=self.speculation_config)
else:
raise NotImplementedError(
"Supports only Transformer-NeuronX based models.")
@torch.inference_mode()
def execute_model(
self,
model_input: ModelInputForNeuron,
kv_caches: Optional[List[torch.Tensor]] = None,
intermediate_tensors: Optional[IntermediateTensors] = None,
num_steps: int = 1,
) -> Optional[List[SamplerOutput]]:
logits = self.model(
input_ids=model_input.input_tokens,
positions=model_input.input_positions,
input_block_ids=model_input.input_block_ids,
**MultiModalKwargs.as_kwargs(
model_input.multi_modal_kwargs or {},
device=self.device,
),
)
output = self.model.sample(
logits=logits,
sampling_metadata=model_input.sampling_metadata,
)
return output
vllm/worker/multi_step_neuronx_distributed_model_runner.py deleted 100644 → 0
View file @ e1885929
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import List, Optional
import torch
from vllm.config import VllmConfig
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.multimodal import MultiModalKwargs
from vllm.sequence import IntermediateTensors
from vllm.worker.neuronx_distributed_model_runner import (
NeuronxDistributedModelRunner)
class MultiStepNeuronxDistributedModelRunner(NeuronxDistributedModelRunner):
"""A model runner for multi-step decoding using the
neuronx-distributed-inference framework"""
def __init__(
self,
vllm_config: VllmConfig,
):
super().__init__(vllm_config)
def load_model(self) -> None:
from vllm.model_executor.model_loader.neuronx_distributed import (
get_neuron_speculation_model)
self.model = get_neuron_speculation_model(
self.model_config,
parallel_config=self.parallel_config,
scheduler_config=self.scheduler_config,
speculation_config=self.speculative_config)
@torch.inference_mode()
def execute_model(
self,
model_input,
kv_caches: Optional[List[torch.Tensor]] = None,
intermediate_tensors: Optional[IntermediateTensors] = None,
num_steps: int = 1,
) -> Optional[List[SamplerOutput]]:
sampling_params = torch.tensor([[
seq_group.sampling_params.top_k,
seq_group.sampling_params.top_p,
seq_group.sampling_params.temperature,
] for seq_group in model_input.sampling_metadata.seq_groups])
logits = self.model(
input_ids=model_input.input_tokens,
positions=model_input.input_positions,
input_block_ids=model_input.input_block_ids,
sampling_params=sampling_params,
**MultiModalKwargs.as_kwargs(
model_input.multi_modal_kwargs or {},
device=self.device,
),
)
output = self.model.sample(
logits=logits,
sampling_metadata=model_input.sampling_metadata,
)
return output
vllm/worker/multi_step_worker.py deleted 100644 → 0
View file @ e1885929
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import dataclasses
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from vllm.distributed import broadcast_tensor_dict, get_pp_group
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest
from vllm.worker.model_runner_base import BroadcastableModelInput
from vllm.worker.multi_step_model_runner import (MultiStepModelRunner,
StatefulModelInput)
from vllm.worker.worker import Worker, WorkerInput
@dataclass
class MultiStepState:
worker_input: WorkerInput
model_input: StatefulModelInput
class MultiStepWorker(Worker):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
base_model_runner = self.model_runner
# for multi-step model, wrap the model runner with MultiStepModelRunner
self.model_runner = MultiStepModelRunner(
base_model_runner,
vllm_config=base_model_runner.vllm_config,
kv_cache_dtype=self.cache_config.cache_dtype,
is_driver_worker=base_model_runner.is_driver_worker,
)
pipeline_parallel_size = self.parallel_config.pipeline_parallel_size
self.multi_step_states: List[
Optional[MultiStepState]] = [None] * pipeline_parallel_size
self.temp_output = None
def _get_driver_input_and_broadcast(
self, execute_model_req: ExecuteModelRequest
) -> Tuple[BroadcastableModelInput, WorkerInput, Dict[str, torch.Tensor]]:
"""
Get the driver input and broadcast it to other workers.
"""
assert self.is_driver_worker
virtual_engine = execute_model_req.virtual_engine
is_first_multi_step = execute_model_req.is_first_multi_step
if is_first_multi_step:
# on first step we prepare the worker input and model input normally
worker_input: WorkerInput = self.prepare_worker_input(
execute_model_req=execute_model_req)
model_input: StatefulModelInput = (
self.model_runner.prepare_model_input(
execute_model_req.seq_group_metadata_list,
execute_model_req.virtual_engine,
execute_model_req.finished_requests_ids))
if execute_model_req.async_callback:
model_input.frozen_model_input = dataclasses.replace( # type: ignore
model_input.frozen_model_input,
async_callback=execute_model_req.async_callback)
else:
# on subsequent steps we reuse the worker input and model input
multi_step_state = self.multi_step_states[virtual_engine]
worker_input = multi_step_state.worker_input
model_input = multi_step_state.model_input
frozen_model_input = model_input.frozen_model_input
assert frozen_model_input is not None
assert frozen_model_input.attn_metadata is not None
# clear the cached metadata so that it can be recomputed on
# the workers.
frozen_model_input.attn_metadata._cached_prefill_metadata = None
frozen_model_input.attn_metadata._cached_decode_metadata = None
model_input.is_first_multi_step = is_first_multi_step
model_input.is_last_step = execute_model_req.is_last_step
if not is_first_multi_step:
# we broadcast the last sampled token ids to all TP workers so they
# can update their model input metadata in-place.
self._prepare_last_sampled_token_ids_for_tp_workers(
execute_model_req=execute_model_req, model_input=model_input)
if self.do_metadata_broadcast:
broadcast_data = worker_input.as_broadcastable_tensor_dict()
broadcast_data.update(model_input.as_broadcastable_tensor_dict())
broadcast_tensor_dict(broadcast_data, src=0)
# Retuning empty dict here to keep this compatible with
# `LocalOrDistributedWorkerBase._get_driver_input_and_broadcast`
return model_input, worker_input, {}
def _prepare_last_sampled_token_ids_for_tp_workers(
self,
execute_model_req: ExecuteModelRequest,
model_input: StatefulModelInput,
) -> None:
"""
Prepare the last sampled token ids for TP workers. If it's the last
PP rank, then the last sampled token ids are already in the model_input.
If it is NOT the last PP rank, then we need to get the last sampled
token that is cached in the execute_model_req.
"""
if get_pp_group().is_last_rank:
assert model_input.cached_outputs[
-1].sampler_output.sampled_token_ids is None
assert model_input.cached_outputs[-1].sampled_token_ids is not None
model_input.last_sampled_token_ids = model_input.cached_outputs[
-1].sampled_token_ids
# free sampled token ids from the previous step if it has been
# pythonized. Cannot free the last sampled token ids because
# we need it for GPU advance_step.
for output in model_input.cached_outputs[:-1]:
if output.pythonized:
output.sampled_token_ids = None
else:
# otherwise we need to get the cached sampled token ids from the
# execute_model_req
assert execute_model_req.last_sampled_token_ids is not None
model_input.last_sampled_token_ids = (
execute_model_req.last_sampled_token_ids.cuda())
model_input.add_sampler_output(
SamplerOutput(outputs=[], sampled_token_ids=None),
model_input.last_sampled_token_ids)
# free sampled token ids from the previous step.
# TODO(will) we could reuse the sampled token ids tensor from
# the previous step instead.
for output in model_input.cached_outputs[:-1]:
output.sampled_token_ids = None
assert model_input.cached_outputs[-1].sampled_token_ids is not None
def prepare_input(
self,
execute_model_req: Optional[ExecuteModelRequest] = None,
) -> Optional[Tuple[StatefulModelInput, WorkerInput, Dict[str,
torch.Tensor]]]:
"""
Depending on the current state of the request and multi step worker,
this method may skip the normal _prepare_model_input and
_prepare_worker_input methods and instead used cached values.
"""
if self.is_driver_worker:
if execute_model_req is None:
if self.do_metadata_broadcast:
# This signals that there's no more requests to process for
# now. All workers are running infinite loop with
# broadcast_tensor_dict, and it stops the loop when the
# driver broadcasts an empty input. Send an empty input to
# notify all other workers to stop their execution loop.
broadcast_tensor_dict({}, src=0)
return None
virtual_engine = execute_model_req.virtual_engine
(model_input, worker_input,
kwargs) = self._get_driver_input_and_broadcast(execute_model_req)
assert isinstance(model_input, StatefulModelInput)
if execute_model_req.is_first_multi_step:
# cache the worker input and model input for the next steps
self.multi_step_states[virtual_engine] = MultiStepState(
worker_input=worker_input, model_input=model_input)
# if TP workers
else:
broadcast_data = self._get_worker_input_from_broadcast()
# if the driver has sent an empty input, we should stop the worker
# loop
if broadcast_data is None:
return None
model_input, worker_input, kwargs = broadcast_data
assert isinstance(model_input, StatefulModelInput)
virtual_engine = worker_input.virtual_engine
if model_input.is_first_multi_step:
pass
# TODO(will) Can cache the worker input and model input for the
# next steps. See below for details
else:
# TODO(will) possible to also cache and reuse the cached worker
# input and model input. The idea is essentially the delta
# optimization for model_inputs. Where the TP workers can cache
# the model input states and we only broadcast the delta need
# for the next step (sampled_token_ids from the previous step)
assert isinstance(model_input, StatefulModelInput)
# we need to update the last sampled token ids in the model
# input for the workers so that they can run inplace
# advance_step
model_input.add_sampler_output(
SamplerOutput(outputs=[], sampled_token_ids=None),
model_input.last_sampled_token_ids)
assert model_input is not None
assert worker_input is not None
return model_input, worker_input, kwargs
vllm/worker/neuron_worker.py
View file @ 71683ca6
......@@ -64,24 +64,20 @@ class NeuronWorker(LocalOrDistributedWorkerBase):
assert (self.lora_config
is None), ("LoRA is not supported for TransformersNeuronX "
"framework.")
from vllm.worker.multi_step_neuron_model_runner import (
MultiStepNeuronModelRunner)
if self.speculative_config is not None:
return MultiStepNeuronModelRunner(vllm_config=vllm_config)
else:
raise NotImplementedError(
"Speculative decoding is not supported for TransformersNeuronX"
)
return NeuronModelRunner(vllm_config=vllm_config)
def get_neuronx_distributed_model_runner(self, vllm_config):
from vllm.worker.multi_step_neuronx_distributed_model_runner import (
MultiStepNeuronxDistributedModelRunner)
from vllm.worker.neuronx_distributed_model_runner import (
NeuronxDistributedModelRunner)
if self.speculative_config is not None:
assert (self.lora_config
is None), "LoRA is not supported for Speculative Decoding"
return MultiStepNeuronxDistributedModelRunner(
vllm_config=vllm_config)
else:
assert (self.lora_config is None), (
"LoRA is not supported for Speculative Decoding")
raise NotImplementedError(
"Speculative decoding is not supported for NeuronxDistributed")
return NeuronxDistributedModelRunner(vllm_config=vllm_config)
def init_device(self) -> None:
......
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