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Unverified Commit 52c2a8d4 authored by Woosuk Kwon's avatar Woosuk Kwon Committed by GitHub
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[V0 Deprecation] Remove LLMEngine (#25033) 


Signed-off-by: default avatarWoosuk Kwon <woosuk@thinkingmachines.ai>
Signed-off-by: default avatarWoosuk Kwon <woosuk.kwon@berkeley.edu>
parent 367a480b
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Showing with 17 additions and 1971 deletions
tests/plugins_tests/test_scheduler_plugins.py
View file @ 52c2a8d4
...@@ -3,47 +3,18 @@ ...@@ -3,47 +3,18 @@
import pytest import pytest
from vllm.core.scheduler import Scheduler
from vllm.engine.arg_utils import EngineArgs from vllm.engine.arg_utils import EngineArgs
from vllm.engine.llm_engine import LLMEngine
from vllm.sampling_params import SamplingParams from vllm.sampling_params import SamplingParams
from vllm.v1.core.sched.scheduler import Scheduler as V1Scheduler from vllm.v1.core.sched.scheduler import Scheduler
from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine from vllm.v1.engine.llm_engine import LLMEngine
class DummyV0Scheduler(Scheduler): class DummyV1Scheduler(Scheduler):
def schedule(self):
raise Exception("Exception raised by DummyV0Scheduler")
class DummyV1Scheduler(V1Scheduler):
def schedule(self): def schedule(self):
raise Exception("Exception raised by DummyV1Scheduler") raise Exception("Exception raised by DummyV1Scheduler")
def test_scheduler_plugins_v0(monkeypatch: pytest.MonkeyPatch):
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "0")
with pytest.raises(Exception) as exception_info:
engine_args = EngineArgs(
model="facebook/opt-125m",
enforce_eager=True, # reduce test time
scheduler_cls=DummyV0Scheduler,
)
engine = LLMEngine.from_engine_args(engine_args=engine_args)
sampling_params = SamplingParams(max_tokens=1)
engine.add_request("0", "foo", sampling_params)
engine.step()
assert str(
exception_info.value) == "Exception raised by DummyV0Scheduler"
def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch): def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch):
with monkeypatch.context() as m: with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1") m.setenv("VLLM_USE_V1", "1")
...@@ -59,7 +30,7 @@ def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch): ...@@ -59,7 +30,7 @@ def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch):
scheduler_cls=DummyV1Scheduler, scheduler_cls=DummyV1Scheduler,
) )
engine = V1LLMEngine.from_engine_args(engine_args=engine_args) engine = LLMEngine.from_engine_args(engine_args=engine_args)
sampling_params = SamplingParams(max_tokens=1) sampling_params = SamplingParams(max_tokens=1)
engine.add_request("0", "foo", sampling_params) engine.add_request("0", "foo", sampling_params)
......
tests/samplers/test_beam_search.py
View file @ 52c2a8d4
...@@ -10,13 +10,6 @@ from transformers import AutoModelForSeq2SeqLM ...@@ -10,13 +10,6 @@ from transformers import AutoModelForSeq2SeqLM
from vllm.assets.audio import AudioAsset from vllm.assets.audio import AudioAsset
@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
"""We can run both engines for this test."""
pass
# FIXME(zhuohan): The test can not pass if we: # FIXME(zhuohan): The test can not pass if we:
# 1. Increase max_tokens to 256. # 1. Increase max_tokens to 256.
# 2. Increase beam_width to 8. # 2. Increase beam_width to 8.
......
tests/samplers/test_ignore_eos.py
View file @ 52c2a8d4
...@@ -9,13 +9,6 @@ import pytest ...@@ -9,13 +9,6 @@ import pytest
from vllm import SamplingParams from vllm import SamplingParams
@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
"""We can run both engines for this test."""
pass
# We also test with llama because it has generation_config to specify EOS # We also test with llama because it has generation_config to specify EOS
# (past regression). # (past regression).
MODELS = ["distilbert/distilgpt2", "meta-llama/Llama-3.2-1B"] MODELS = ["distilbert/distilgpt2", "meta-llama/Llama-3.2-1B"]
......
tests/samplers/test_ranks.py
View file @ 52c2a8d4
...@@ -8,12 +8,6 @@ from vllm import SamplingParams ...@@ -8,12 +8,6 @@ from vllm import SamplingParams
MODELS = ["distilbert/distilgpt2"] MODELS = ["distilbert/distilgpt2"]
@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
"""We can run both engines for this test."""
pass
@pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("dtype", ["half"])
def test_ranks( def test_ranks(
......
tests/tokenization/test_detokenize.py
View file @ 52c2a8d4
...@@ -352,58 +352,3 @@ def test_decode_prompt_logprobs(complete_sequence: str, ...@@ -352,58 +352,3 @@ def test_decode_prompt_logprobs(complete_sequence: str,
logprobs[token_id + 1].decoded_token logprobs[token_id + 1].decoded_token
for token_id, logprobs in zip(token_ids[1:], decoded_prompt_logprobs) for token_id, logprobs in zip(token_ids[1:], decoded_prompt_logprobs)
]) ])
@pytest.mark.parametrize("model", ["facebook/opt-125m"])
@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 7, 16, -1])
def test_decode_prompt_logprobs_chunked_prefill(
vllm_runner,
model,
chunked_prefill_token_size: int,
example_prompts,
monkeypatch,
):
# VLLM V1 does not use incremental detokenization for
# prompt logprobs, so this test strategy is irrelevant.
monkeypatch.setenv("VLLM_USE_V1", "0")
max_num_seqs = 256
enable_chunked_prefill = False
max_num_batched_tokens = None
if chunked_prefill_token_size != -1:
enable_chunked_prefill = True
max_num_seqs = min(chunked_prefill_token_size, max_num_seqs)
max_num_batched_tokens = chunked_prefill_token_size
with vllm_runner(model,
dtype="half",
max_logprobs=5,
gpu_memory_utilization=0.5,
enable_chunked_prefill=enable_chunked_prefill,
max_num_batched_tokens=max_num_batched_tokens,
max_num_seqs=max_num_seqs) as vllm_model:
vllm_sampling_params = SamplingParams(max_tokens=10,
logprobs=5,
prompt_logprobs=5,
temperature=0.0)
vllm_results = vllm_model.llm.generate(
example_prompts, sampling_params=vllm_sampling_params)
for idx, result in enumerate(vllm_results):
assert result.prompt_logprobs is not None
assert result.prompt_logprobs[0] is None
# Compared detokenized prompts ids to original prompt.
generated_string = ""
for (prompt_token,
prompt_logprobs) in zip(result.prompt_token_ids[1:],
result.prompt_logprobs[1:]):
# prompt_logprobs is a dict of the token_id: logprob
# We select the token_id corresponding to the actual prompt
# Decoded token in the detokenized string corresponding to this
# prompt token.
generated_string += prompt_logprobs[prompt_token].decoded_token
assert generated_string == example_prompts[idx], (
"Detokenized prompt logprobs do not match original prompt")
vllm/engine/arg_utils.py
View file @ 52c2a8d4
...@@ -1508,14 +1508,6 @@ class EngineArgs: ...@@ -1508,14 +1508,6 @@ class EngineArgs:
recommend_to_remove=True) recommend_to_remove=True)
return False return False
if self.kv_cache_dtype != "auto":
supported = current_platform.is_kv_cache_dtype_supported(
self.kv_cache_dtype, model_config)
if not supported:
_raise_or_fallback(feature_name="--kv-cache-dtype",
recommend_to_remove=False)
return False
# No Mamba or Encoder-Decoder so far. # No Mamba or Encoder-Decoder so far.
if not model_config.is_v1_compatible: if not model_config.is_v1_compatible:
_raise_or_fallback(feature_name=model_config.architectures, _raise_or_fallback(feature_name=model_config.architectures,
......
vllm/engine/llm_engine.py
View file @ 52c2a8d4
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import time from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
from collections import Counter as collectionsCounter
from collections import deque
from contextlib import contextmanager
from dataclasses import dataclass
from functools import partial
from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,
Iterable, List, Literal, Mapping, NamedTuple, Optional)
from typing import Sequence as GenericSequence
from typing import Set, Type, Union, cast
import torch LLMEngine = V1LLMEngine # type: ignore
import torch.nn as nn
from typing_extensions import TypeVar
import vllm.envs as envs
from vllm.config import (LoRAConfig, ModelConfig, ObservabilityConfig,
ParallelConfig, SchedulerConfig, VllmConfig)
from vllm.core.scheduler import ScheduledSequenceGroup, SchedulerOutputs
from vllm.engine.arg_utils import EngineArgs
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.entrypoints.openai.logits_processors import (
get_logits_processors as get_openai_logits_processors)
from vllm.executor.executor_base import ExecutorBase
from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
from vllm.inputs.parse import split_enc_dec_inputs
from vllm.inputs.preprocess import InputPreprocessor
from vllm.logger import init_logger
from vllm.logits_process import get_bad_words_logits_processors
from vllm.lora.request import LoRARequest
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.multimodal.cache import processor_only_cache_from_config
from vllm.multimodal.processing import EncDecMultiModalProcessor
from vllm.outputs import (PoolingRequestOutput, RequestOutput,
RequestOutputFactory)
from vllm.reasoning import ReasoningParser, ReasoningParserManager
from vllm.sampling_params import RequestOutputKind, SamplingParams
from vllm.sequence import (ExecuteModelRequest, ParallelSampleSequenceGroup,
Sequence, SequenceGroup, SequenceGroupBase,
SequenceGroupMetadata, SequenceGroupOutput,
SequenceStatus)
from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
init_tracer)
from vllm.transformers_utils.detokenizer import Detokenizer
from vllm.transformers_utils.tokenizer import (AnyTokenizer,
init_tokenizer_from_configs)
from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
usage_message)
from vllm.utils import Counter, Device, resolve_obj_by_qualname, weak_bind
from vllm.version import __version__ as VLLM_VERSION
from vllm.worker.model_runner_base import InputProcessingError
from vllm.worker.worker_base import WorkerBase
logger = init_logger(__name__)
_LOCAL_LOGGING_INTERVAL_SEC = 5
_O = TypeVar("_O", RequestOutput, PoolingRequestOutput)
_R = TypeVar("_R", default=Any)
@dataclass
class SchedulerOutputState:
"""Caches the scheduler outputs for a virtual engine. Used for Multi-Step"""
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
scheduler_outputs: Optional[SchedulerOutputs] = None
allow_async_output_proc: bool = False
last_output: Optional[SamplerOutput] = None
class OutputData(NamedTuple):
outputs: List[SamplerOutput]
seq_group_metadata_list: List[SequenceGroupMetadata]
scheduler_outputs: SchedulerOutputs
is_async: bool
is_last_step: bool
# Indicates if this output is from the first step of the
# multi-step. When multi-step is disabled, this is always
# set to True.
# is_first_step_output is invalid when `outputs` has
# outputs from multiple steps.
is_first_step_output: Optional[bool]
skip: List[int]
class SchedulerContext:
def __init__(self) -> None:
self.output_queue: Deque[OutputData] = deque()
self.request_outputs: List[RequestOutput] = []
self.seq_group_metadata_list: Optional[
List[SequenceGroupMetadata]] = None
self.scheduler_outputs: Optional[SchedulerOutputs] = None
def append_output(self, outputs: List[SamplerOutput],
seq_group_metadata_list: List[SequenceGroupMetadata],
scheduler_outputs: SchedulerOutputs, is_async: bool,
is_last_step: bool,
is_first_step_output: Optional[bool]):
self.output_queue.append(
OutputData(outputs=outputs,
seq_group_metadata_list=seq_group_metadata_list,
scheduler_outputs=scheduler_outputs,
is_async=is_async,
is_last_step=is_last_step,
is_first_step_output=is_first_step_output,
skip=[]))
class LLMEngine:
"""An LLM engine that receives requests and generates texts.
This is the main class for the vLLM engine. It receives requests
from clients and generates texts from the LLM. It includes a tokenizer, a
language model (possibly distributed across multiple GPUs), and GPU memory
space allocated for intermediate states (aka KV cache). This class utilizes
iteration-level scheduling and efficient memory management to maximize the
serving throughput.
The [`LLM`][vllm.LLM] class wraps this class for offline batched inference
and the [`AsyncLLMEngine`][vllm.engine.async_llm_engine.AsyncLLMEngine]
class wraps this class for online serving.
The config arguments are derived from [`EngineArgs`][vllm.EngineArgs].
Args:
vllm_config: The configuration for initializing and running vLLM.
executor_class: The model executor class for managing distributed
execution.
log_stats: Whether to log statistics.
usage_context: Specified entry point, used for usage info collection.
"""
DO_VALIDATE_OUTPUT: ClassVar[bool] = False
"""A flag to toggle whether to validate the type of request output."""
@classmethod
@contextmanager
def enable_output_validation(cls):
cls.DO_VALIDATE_OUTPUT = True
yield
cls.DO_VALIDATE_OUTPUT = False
@classmethod
def validate_output(
cls,
output: object,
output_type: Type[_O],
) -> _O:
do_validate = cls.DO_VALIDATE_OUTPUT
if ((TYPE_CHECKING or do_validate)
and not isinstance(output, output_type)):
raise TypeError(f"Expected output of type {output_type}, "
f"but found type {type(output)}")
return cast(_O, output)
@classmethod
def validate_outputs(
cls,
outputs: GenericSequence[object],
output_type: Type[_O],
) -> List[_O]:
do_validate = cls.DO_VALIDATE_OUTPUT
outputs_: List[_O]
if TYPE_CHECKING or do_validate:
outputs_ = []
for output in outputs:
if not isinstance(output, output_type):
raise TypeError(f"Expected output of type {output_type}, "
f"but found type {type(output)}")
outputs_.append(output)
else:
outputs_ = outputs
return outputs_
tokenizer: Optional[AnyTokenizer]
def __init__(
self,
vllm_config: VllmConfig,
executor_class: Type[ExecutorBase],
log_stats: bool,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
use_cached_outputs: bool = False,
) -> None:
if envs.VLLM_USE_V1:
raise ValueError(
"Using V0 LLMEngine, but envs.VLLM_USE_V1=True. "
"This should not happen. As a workaround, try using "
"LLMEngine.from_vllm_config(...) or explicitly set "
"VLLM_USE_V1=0 or 1 and report this issue on Github.")
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.parallel_config = vllm_config.parallel_config
self.scheduler_config = vllm_config.scheduler_config
self.device_config = vllm_config.device_config
self.speculative_config = vllm_config.speculative_config # noqa
self.load_config = vllm_config.load_config
self.structured_outputs_config = vllm_config.structured_outputs_config
self.observability_config = vllm_config.observability_config or ObservabilityConfig( # noqa
)
logger.info(
"Initializing a V0 LLM engine (v%s) with config: %s, "
"use_cached_outputs=%s, ",
VLLM_VERSION,
vllm_config,
use_cached_outputs,
)
self.log_stats = log_stats
self.use_cached_outputs = use_cached_outputs
if self.model_config.skip_tokenizer_init:
self.tokenizer = None
self.detokenizer = None
else:
self.tokenizer = self._init_tokenizer()
self.detokenizer = Detokenizer(self.tokenizer)
self.seq_counter = Counter()
self.generation_config_fields = (
self.model_config.try_get_generation_config())
self.input_preprocessor = InputPreprocessor(
self.model_config,
self.tokenizer,
mm_registry,
mm_processor_cache=processor_only_cache_from_config(
self.model_config, mm_registry),
)
self.model_executor = executor_class(vllm_config=vllm_config)
self._initialize_kv_caches()
# If usage stat is enabled, collect relevant info.
if is_usage_stats_enabled():
from vllm.model_executor.model_loader import (
get_architecture_class_name)
usage_message.report_usage(
get_architecture_class_name(self.model_config),
usage_context,
extra_kvs={
# Common configuration
"dtype":
str(self.model_config.dtype),
"tensor_parallel_size":
self.parallel_config.tensor_parallel_size,
"block_size":
self.cache_config.block_size,
"gpu_memory_utilization":
self.cache_config.gpu_memory_utilization,
"kv_cache_memory_bytes":
self.cache_config.kv_cache_memory_bytes,
# Quantization
"quantization":
self.model_config.quantization,
"kv_cache_dtype":
str(self.cache_config.cache_dtype),
# Feature flags
"enable_lora":
bool(self.lora_config),
"enable_prefix_caching":
self.cache_config.enable_prefix_caching,
"enforce_eager":
self.model_config.enforce_eager,
"disable_custom_all_reduce":
self.parallel_config.disable_custom_all_reduce,
})
self.cached_scheduler_outputs = [
SchedulerOutputState()
for _ in range(self.parallel_config.pipeline_parallel_size)
]
self.scheduler_contexts = [
SchedulerContext()
for _ in range(self.parallel_config.pipeline_parallel_size)
]
if self.model_config.use_async_output_proc:
process_model_outputs = weak_bind(self._process_model_outputs)
self.async_callbacks = [
partial(process_model_outputs,
ctx=self.scheduler_contexts[v_id])
for v_id in range(self.parallel_config.pipeline_parallel_size)
]
else:
self.async_callbacks = []
# Currently used by AsyncLLMEngine to ensure quick append
# of request outputs to asyncio queues
self.process_request_outputs_callback: Optional[Callable] = None
# Create the scheduler.
# NOTE: the cache_config here have been updated with the numbers of
# GPU and CPU blocks, which are profiled in the distributed executor.
if isinstance(self.vllm_config.scheduler_config.scheduler_cls, str):
Scheduler = resolve_obj_by_qualname(
self.vllm_config.scheduler_config.scheduler_cls)
else:
Scheduler = self.vllm_config.scheduler_config.scheduler_cls
self.scheduler = [
Scheduler(
self.scheduler_config, self.cache_config, self.lora_config,
self.parallel_config.pipeline_parallel_size,
self.async_callbacks[v_id]
if self.model_config.use_async_output_proc else None)
for v_id in range(self.parallel_config.pipeline_parallel_size)
]
# Metric Logging.
if self.log_stats:
if stat_loggers is not None:
self.stat_loggers = stat_loggers
else:
# Lazy import for prometheus multiprocessing.
# We need to set PROMETHEUS_MULTIPROC_DIR environment variable
# before prometheus_client is imported.
# See https://prometheus.github.io/client_python/multiprocess/
from vllm.engine.metrics import (LoggingStatLogger,
PrometheusStatLogger)
self.stat_loggers = {
"logging":
LoggingStatLogger(
local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
vllm_config=vllm_config),
"prometheus":
PrometheusStatLogger(
local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
labels=dict(
model_name=self.model_config.served_model_name),
vllm_config=vllm_config),
}
self.stat_loggers["prometheus"].info("cache_config",
self.cache_config)
self.tracer = None
if self.observability_config.otlp_traces_endpoint:
self.tracer = init_tracer(
"vllm.llm_engine",
self.observability_config.otlp_traces_endpoint)
# Initialize reasoning parser if reasoning backend is set.
if self.structured_outputs_config.reasoning_parser and self.tokenizer:
reasoner_class = ReasoningParserManager.get_reasoning_parser(
self.structured_outputs_config.reasoning_parser)
self.reasoner: ReasoningParser = reasoner_class(
self.tokenizer.get_lora_tokenizer())
# Create sequence output processor, e.g. for beam search or
# speculative decoding.
self.output_processor = (
SequenceGroupOutputProcessor.create_output_processor(
self.scheduler_config,
self.detokenizer,
self.scheduler,
self.seq_counter,
stop_checker=StopChecker(
self.scheduler_config.max_model_len,
self.reasoner
if self.structured_outputs_config.reasoning_parser
and self.tokenizer else None,
),
))
self.seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {}
# Flag to set when an input fails to process and the engine should run
# the next step without re-scheduling.
self._skip_scheduling_next_step = False
# Don't keep the dummy data in memory
self.reset_mm_cache()
def _initialize_kv_caches(self) -> None:
"""Initialize the KV cache in the worker(s).
The workers will determine the number of blocks in both the GPU cache
and the swap CPU cache.
"""
start = time.time()
num_gpu_blocks, num_cpu_blocks = (
self.model_executor.determine_num_available_blocks())
if self.cache_config.num_gpu_blocks_override is not None:
num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override
logger.info(
"Overriding num_gpu_blocks=%d with "
"num_gpu_blocks_override=%d", num_gpu_blocks,
num_gpu_blocks_override)
num_gpu_blocks = num_gpu_blocks_override
self.cache_config.num_gpu_blocks = num_gpu_blocks
self.cache_config.num_cpu_blocks = num_cpu_blocks
self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
elapsed = time.time() - start
logger.info(("init engine (profile, create kv cache, "
"warmup model) took %.2f seconds"), elapsed)
@classmethod
def _get_executor_cls(cls,
engine_config: VllmConfig) -> Type[ExecutorBase]:
# distributed_executor_backend must be set in VllmConfig.__post_init__
distributed_executor_backend = (
engine_config.parallel_config.distributed_executor_backend)
# Initialize the cluster and specify the executor class.
if isinstance(distributed_executor_backend, type):
if not issubclass(distributed_executor_backend, ExecutorBase):
raise TypeError(
"distributed_executor_backend must be a subclass of "
f"ExecutorBase. Got {distributed_executor_backend}.")
executor_class = distributed_executor_backend
elif distributed_executor_backend == "ray":
from vllm.executor.ray_distributed_executor import (
RayDistributedExecutor)
executor_class = RayDistributedExecutor
elif distributed_executor_backend == "mp":
from vllm.executor.mp_distributed_executor import (
MultiprocessingDistributedExecutor)
assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
"multiprocessing distributed executor backend does not "
"support VLLM_USE_RAY_SPMD_WORKER=1")
executor_class = MultiprocessingDistributedExecutor
elif distributed_executor_backend == "uni":
# JAX-style, single-process, multi-device executor.
from vllm.executor.uniproc_executor import UniProcExecutor
executor_class = UniProcExecutor
elif distributed_executor_backend == "external_launcher":
# executor with external launcher
from vllm.executor.uniproc_executor import ( # noqa
ExecutorWithExternalLauncher)
executor_class = ExecutorWithExternalLauncher
else:
raise ValueError("unrecognized distributed_executor_backend: "
f"{distributed_executor_backend}")
return executor_class
@classmethod
def from_vllm_config(
cls,
vllm_config: VllmConfig,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
disable_log_stats: bool = False,
) -> "LLMEngine":
return cls(
vllm_config=vllm_config,
executor_class=cls._get_executor_cls(vllm_config),
log_stats=(not disable_log_stats),
usage_context=usage_context,
stat_loggers=stat_loggers,
)
@classmethod
def from_engine_args(
cls,
engine_args: EngineArgs,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
) -> "LLMEngine":
"""Creates an LLM engine from the engine arguments."""
# Create the engine configs.
vllm_config = engine_args.create_engine_config(usage_context)
engine_cls = cls
if envs.VLLM_USE_V1:
from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
engine_cls = V1LLMEngine
return engine_cls.from_vllm_config(
vllm_config=vllm_config,
usage_context=usage_context,
stat_loggers=stat_loggers,
disable_log_stats=engine_args.disable_log_stats,
)
def __reduce__(self):
# This is to ensure that the LLMEngine is not referenced in
# the closure used to initialize Ray worker actors
raise RuntimeError("LLMEngine should not be pickled!")
def __del__(self):
# Shutdown model executor when engine is garbage collected
# Use getattr since __init__ can fail before the field is set
if model_executor := getattr(self, "model_executor", None):
model_executor.shutdown()
def get_tokenizer(self) -> AnyTokenizer:
if self.tokenizer is None:
raise ValueError("Unable to get tokenizer because "
"skip_tokenizer_init is True")
return self.tokenizer
def _init_tokenizer(self) -> AnyTokenizer:
return init_tokenizer_from_configs(model_config=self.model_config)
def _verify_args(self) -> None:
self.model_config.verify_with_parallel_config(self.parallel_config)
self.cache_config.verify_with_parallel_config(self.parallel_config)
if self.lora_config:
self.lora_config.verify_with_model_config(self.model_config)
self.lora_config.verify_with_scheduler_config(
self.scheduler_config)
def _add_processed_request(
self,
request_id: str,
processed_inputs: ProcessorInputs,
params: SamplingParams,
arrival_time: float,
lora_request: Optional[LoRARequest],
trace_headers: Optional[Mapping[str, str]] = None,
priority: int = 0,
) -> Optional[SequenceGroup]:
"""Add a processed request to the engine's request pool.
return the created sequence group.
"""
if isinstance(params, SamplingParams) and params.n > 1:
ParallelSampleSequenceGroup.add_request(
request_id,
self,
params,
processed_inputs=processed_inputs,
arrival_time=arrival_time,
lora_request=lora_request,
trace_headers=trace_headers,
priority=priority,
)
return None
self._validate_model_inputs(processed_inputs)
# Create the sequences.
block_size = self.cache_config.block_size
seq_id = next(self.seq_counter)
eos_token_id = self.input_preprocessor.get_eos_token_id()
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
seq = Sequence(seq_id, decoder_inputs, block_size, eos_token_id,
lora_request)
encoder_seq = (None if encoder_inputs is None else Sequence(
seq_id, encoder_inputs, block_size, eos_token_id, lora_request))
# Create a SequenceGroup based on SamplingParams
if isinstance(params, SamplingParams):
seq_group = self._create_sequence_group_with_sampling(
request_id,
seq,
params,
arrival_time=arrival_time,
lora_request=lora_request,
trace_headers=trace_headers,
encoder_seq=encoder_seq,
priority=priority)
else:
raise ValueError("SamplingParams must be provided.")
# Add the sequence group to the scheduler with least unfinished seqs.
costs = [
scheduler.get_num_unfinished_seq_groups()
for scheduler in self.scheduler
]
min_cost_scheduler = self.scheduler[costs.index(min(costs))]
min_cost_scheduler.add_seq_group(seq_group)
return seq_group
def stop_remote_worker_execution_loop(self) -> None:
self.model_executor.stop_remote_worker_execution_loop()
def add_request(
self,
request_id: str,
prompt: PromptType,
params: SamplingParams,
arrival_time: Optional[float] = None,
lora_request: Optional[LoRARequest] = None,
tokenization_kwargs: Optional[dict[str, Any]] = None,
trace_headers: Optional[Mapping[str, str]] = None,
priority: int = 0,
) -> None:
"""Add a request to the engine's request pool.
The request is added to the request pool and will be processed by the
scheduler as `engine.step()` is called. The exact scheduling policy is
determined by the scheduler.
Args:
request_id: The unique ID of the request.
prompt: The prompt to the LLM. See
[PromptType][vllm.inputs.PromptType]
for more details about the format of each input.
params: Parameters for sampling.
[SamplingParams][vllm.SamplingParams] for text generation.
arrival_time: The arrival time of the request. If None, we use
the current monotonic time.
lora_request: The LoRA request to add.
trace_headers: OpenTelemetry trace headers.
priority: The priority of the request.
Only applicable with priority scheduling.
Details:
- Set arrival_time to the current time if it is None.
- Set prompt_token_ids to the encoded prompt if it is None.
- Create `n` number of [Sequence][vllm.sequence.Sequence] objects.
- Create a [SequenceGroup][vllm.sequence.SequenceGroup] object
from the list of [Sequence][vllm.sequence.Sequence].
- Add the [SequenceGroup][vllm.sequence.SequenceGroup] object to the
scheduler.
Example:
>>> # initialize engine
>>> engine = LLMEngine.from_engine_args(engine_args)
>>> # set request arguments
>>> example_prompt = "Who is the president of the United States?"
>>> sampling_params = SamplingParams(temperature=0.0)
>>> request_id = 0
>>>
>>> # add the request to the engine
>>> engine.add_request(
>>> str(request_id),
>>> example_prompt,
>>> SamplingParams(temperature=0.0))
>>> # continue the request processing
>>> ...
"""
if not isinstance(request_id, str):
raise TypeError(
f"request_id must be a string, got {type(request_id)}")
if lora_request is not None and not self.lora_config:
raise ValueError(f"Got lora_request {lora_request} but LoRA is "
"not enabled!")
if priority != 0 and not self.scheduler_config.policy == "priority":
raise ValueError(f"Got priority {priority} but "
"Priority scheduling is not enabled.")
if isinstance(params, SamplingParams) \
and params.logits_processors:
raise ValueError(
"Logits processors are not supported in multi-step decoding")
if arrival_time is None:
arrival_time = time.time()
if (isinstance(prompt, dict)
and prompt.get("prompt_embeds", None) is not None):
if not prompt.get("prompt_token_ids", None):
seq_len = prompt["prompt_embeds"].shape[0]
prompt["prompt_token_ids"] = [0] * seq_len
if params.prompt_logprobs is not None:
raise ValueError(
"prompt_logprobs is not compatible with prompt embeds.")
processed_inputs = self.input_preprocessor.preprocess(
prompt,
tokenization_kwargs=tokenization_kwargs,
)
self._add_processed_request(
request_id=request_id,
processed_inputs=processed_inputs,
params=params,
arrival_time=arrival_time,
lora_request=lora_request,
trace_headers=trace_headers,
priority=priority,
)
def _create_sequence_group_with_sampling(
self,
request_id: str,
seq: Sequence,
sampling_params: SamplingParams,
arrival_time: float,
lora_request: Optional[LoRARequest],
trace_headers: Optional[Mapping[str, str]] = None,
encoder_seq: Optional[Sequence] = None,
priority: int = 0,
) -> SequenceGroup:
"""Creates a SequenceGroup with SamplingParams."""
max_logprobs = self.get_model_config().max_logprobs
if (sampling_params.logprobs
and sampling_params.logprobs > max_logprobs) or (
sampling_params.prompt_logprobs
and sampling_params.prompt_logprobs > max_logprobs):
raise ValueError(f"Cannot request more than "
f"{max_logprobs} logprobs.")
sampling_params = self._build_logits_processors(
sampling_params, lora_request)
# Defensive copy of SamplingParams, which are used by the sampler,
# this doesn't deep-copy LogitsProcessor objects
sampling_params = sampling_params.clone()
sampling_params.update_from_generation_config(
self.generation_config_fields, seq.eos_token_id)
# Create the sequence group.
draft_size = 1
if self.vllm_config.speculative_config is not None:
draft_size = \
self.vllm_config.speculative_config.num_speculative_tokens + 1
seq_group = SequenceGroup(request_id=request_id,
seqs=[seq],
arrival_time=arrival_time,
sampling_params=sampling_params,
lora_request=lora_request,
trace_headers=trace_headers,
encoder_seq=encoder_seq,
priority=priority,
draft_size=draft_size)
return seq_group
def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
"""Aborts a request(s) with the given ID.
Args:
request_id: The ID(s) of the request to abort.
Details:
- Refer to [vllm.core.scheduler.Scheduler.abort_seq_group][].
Example:
>>> # initialize engine and add a request with request_id
>>> request_id = str(0)
>>> # abort the request
>>> engine.abort_request(request_id)
"""
for scheduler in self.scheduler:
scheduler.abort_seq_group(
request_id, seq_id_to_seq_group=self.seq_id_to_seq_group)
def get_vllm_config(self) -> VllmConfig:
"""Gets the vllm configuration."""
return self.vllm_config
def get_model_config(self) -> ModelConfig:
"""Gets the model configuration."""
return self.model_config
def get_parallel_config(self) -> ParallelConfig:
"""Gets the parallel configuration."""
return self.parallel_config
def get_scheduler_config(self) -> SchedulerConfig:
"""Gets the scheduler configuration."""
return self.scheduler_config
def get_lora_config(self) -> LoRAConfig:
"""Gets the LoRA configuration."""
return self.lora_config
def get_num_unfinished_requests(self) -> int:
"""Gets the number of unfinished requests."""
return sum(scheduler.get_num_unfinished_seq_groups()
for scheduler in self.scheduler)
def has_unfinished_requests(self) -> bool:
"""Returns True if there are unfinished requests."""
return any(scheduler.has_unfinished_seqs()
for scheduler in self.scheduler)
def has_unfinished_requests_for_virtual_engine(
self, virtual_engine: int) -> bool:
"""
Returns True if there are unfinished requests for the virtual engine.
"""
return self.scheduler[virtual_engine].has_unfinished_seqs()
def reset_mm_cache(self) -> bool:
"""Reset the multi-modal cache."""
self.input_preprocessor.clear_cache()
return True
def reset_prefix_cache(self, device: Optional[Device] = None) -> bool:
"""Reset prefix cache for all devices."""
success = True
for scheduler in self.scheduler:
success = success and scheduler.reset_prefix_cache(device)
return success
def _process_model_outputs(self,
ctx: SchedulerContext,
request_id: Optional[str] = None) -> None:
"""Apply the model output to the sequences in the scheduled seq groups
and return responses.
ctx: The virtual engine context to work on
request_id: If provided, then only this request is going to be processed
"""
now = time.time()
if len(ctx.output_queue) == 0:
return None
# Get pending async postprocessor
if request_id:
# When we process only one request, no pop is required
# (since later we will process all of the rest)
(outputs, seq_group_metadata_list, scheduler_outputs, is_async,
is_last_step, is_first_step_output, skip) = ctx.output_queue[0]
else:
(outputs, seq_group_metadata_list, scheduler_outputs, is_async,
is_last_step, is_first_step_output,
skip) = ctx.output_queue.popleft()
# Sanity check
assert len(seq_group_metadata_list) == len(
scheduler_outputs.scheduled_seq_groups)
has_multiple_outputs: bool = len(outputs) > 1
outputs_by_sequence_group: List[List[SequenceGroupOutput]]
assert not has_multiple_outputs
outputs_by_sequence_group = outputs
# Determine the requests we need to operate on
if request_id:
indices = []
for i, seq_group_meta in enumerate(seq_group_metadata_list):
if seq_group_meta.request_id == request_id:
assert i not in skip # Cannot be called twice
indices.append(i)
break
# If the request_id was not found, then it means that
# this is a new request that has no pending async
# postprocessor
if not indices:
return
else:
indices = range(len(seq_group_metadata_list)) # type: ignore
finished_before: List[int] = []
finished_now: List[int] = []
for i in indices:
if i in skip:
continue
seq_group_meta = seq_group_metadata_list[i]
scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]
seq_group: SequenceGroup = scheduled_seq_group.seq_group
if seq_group.is_finished():
finished_before.append(i)
continue
output: List[SequenceGroupOutput]
if has_multiple_outputs:
output = outputs_by_sequence_group[i]
else:
output = [outputs_by_sequence_group[0][i]]
if not is_async:
seq_group.update_num_computed_tokens(
seq_group_meta.token_chunk_size or 0)
if outputs:
for o in outputs:
if (isinstance(o, SamplerOutput)
and seq_group.metrics is not None):
if seq_group.metrics.model_forward_time is not None:
seq_group.metrics.model_forward_time += (
o.model_forward_time or 0)
else:
seq_group.metrics.model_forward_time = (
o.model_forward_time)
if seq_group.metrics.model_execute_time is not None:
seq_group.metrics.model_execute_time += (
o.model_execute_time or 0)
else:
seq_group.metrics.model_execute_time = (
o.model_execute_time)
self.output_processor.process_prompt_logprob(seq_group, output)
if seq_group_meta.do_sample:
self.output_processor.process_outputs(seq_group, output,
is_async)
if seq_group.is_finished():
finished_now.append(i)
# Generate outputs for the requests that finished this iteration
for i in finished_now:
scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]
seq_group = scheduled_seq_group.seq_group
seq_group.maybe_set_first_token_time(now)
if not seq_group.is_prefill():
seq_group.set_last_token_time(now)
request_output = RequestOutputFactory.create(
seq_group,
self.seq_id_to_seq_group,
use_cache=self.use_cached_outputs)
if request_output:
ctx.request_outputs.append(request_output)
# When we process a single request, we skip it for the next time,
# and invoke the request output callback (if there was final output)
if request_id:
assert len(indices) == 1
skip.append(indices[0])
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
# Free currently finished requests
if finished_now:
for scheduler in self.scheduler:
scheduler.free_finished_seq_groups()
# Create the outputs
for i in indices:
if i in skip or i in finished_before or i in finished_now:
continue # Avoids double processing
scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]
seq_group = scheduled_seq_group.seq_group
seq_group.maybe_set_first_token_time(now)
if not seq_group.is_prefill():
seq_group.set_last_token_time(now)
request_output = RequestOutputFactory.create(
seq_group,
self.seq_id_to_seq_group,
use_cache=self.use_cached_outputs)
if request_output:
ctx.request_outputs.append(request_output)
# Create outputs only after processing the scheduler's results
for seq_group in scheduler_outputs.ignored_seq_groups:
params = seq_group.sampling_params
if params is not None and params.output_kind == (
RequestOutputKind.DELTA) and not seq_group.is_finished():
continue
request_output = RequestOutputFactory.create(
seq_group,
self.seq_id_to_seq_group,
use_cache=self.use_cached_outputs,
)
if request_output:
ctx.request_outputs.append(request_output)
# Immediately process request outputs here (if callback is given)
if (ctx.request_outputs
and self.process_request_outputs_callback is not None):
self.process_request_outputs_callback(ctx.request_outputs)
ctx.request_outputs.clear()
# For async case, we need to record the stats here.
# For non-async case, the stats are done in the
# LLMEngine/AsyncLLMEngine directly
if is_async:
# Log stats.
self.do_log_stats(scheduler_outputs, outputs, finished_before,
skip)
# Tracing
self.do_tracing(scheduler_outputs, finished_before)
return None
def _advance_to_next_step(
self, output: SamplerOutput,
seq_group_metadata_list: List[SequenceGroupMetadata],
scheduled_seq_groups: List[ScheduledSequenceGroup]) -> None:
"""Given model output from a single run, append the tokens to the
sequences. This is normally done inside output processor, but it is
required if the worker is to perform async forward pass to next step.
"""
for seq_group_metadata, sequence_group_outputs, scheduled_seq_group in \
zip(seq_group_metadata_list, output, scheduled_seq_groups):
seq_group = scheduled_seq_group.seq_group
if seq_group.is_finished():
continue
token_chunk_size = (seq_group_metadata.token_chunk_size
if seq_group_metadata.token_chunk_size
is not None else 0)
seq_group.update_num_computed_tokens(token_chunk_size)
if seq_group_metadata.do_sample:
assert len(sequence_group_outputs.samples) == 1, (
"Async output processor expects a single sample"
" (i.e sampling_params.n == 1)")
sample = sequence_group_outputs.samples[0]
assert len(seq_group.seqs) == 1
seq = seq_group.seqs[0]
seq.append_token_id(sample.output_token, sample.logprobs,
sample.output_embed)
def step(self) -> List[RequestOutput]:
"""Performs one decoding iteration and returns newly generated results.
<figure markdown="span">
![Overview of the step function](https://i.imgur.com/sv2HssD.png)
<figcaption>Overview of the step function</figcaption>
</figure>
Details:
- Step 1: Schedules the sequences to be executed in the next
iteration and the token blocks to be swapped in/out/copy.
- Depending on the scheduling policy,
sequences may be `preempted/reordered`.
- A Sequence Group (SG) refer to a group of sequences
that are generated from the same prompt.
- Step 2: Calls the distributed executor to execute the model.
- Step 3: Processes the model output. This mainly includes:
- Decodes the relevant outputs.
- Updates the scheduled sequence groups with model outputs
based on its `sampling parameters` (`use_beam_search` or not).
- Frees the finished sequence groups.
- Finally, it creates and returns the newly generated results.
Example:
```
# Please see the example/ folder for more detailed examples.
# initialize engine and request arguments
engine = LLMEngine.from_engine_args(engine_args)
example_inputs = [(0, "What is LLM?",
SamplingParams(temperature=0.0))]
# Start the engine with an event loop
while True:
if example_inputs:
req_id, prompt, sampling_params = example_inputs.pop(0)
engine.add_request(str(req_id),prompt,sampling_params)
# continue the request processing
request_outputs = engine.step()
for request_output in request_outputs:
if request_output.finished:
# return or show the request output
if not (engine.has_unfinished_requests() or example_inputs):
break
```
"""
if self.parallel_config.pipeline_parallel_size > 1:
raise NotImplementedError(
"Pipeline parallelism is only supported through AsyncLLMEngine "
"as performance will be severely degraded otherwise.")
# For llm_engine, there is no pipeline parallel support, so the engine
# used is always 0.
virtual_engine = 0
# These are cached outputs from previous iterations. None if on first
# iteration
cached_outputs = self.cached_scheduler_outputs[virtual_engine]
seq_group_metadata_list = cached_outputs.seq_group_metadata_list
scheduler_outputs = cached_outputs.scheduler_outputs
allow_async_output_proc = cached_outputs.allow_async_output_proc
ctx = self.scheduler_contexts[virtual_engine]
# Clear outputs for each new scheduler iteration
ctx.request_outputs.clear()
# Skip the scheduler if there are any remaining steps in the seq groups.
# This ensures that the scheduler is only called again when the current
# batch has completed.
# The scheduler is also skipped if a single request caused the last
# engine step to fail, and the previous schedule needs to be rerun.
if not self._has_remaining_steps(
seq_group_metadata_list
) and not self._skip_scheduling_next_step:
# Schedule iteration
(seq_group_metadata_list, scheduler_outputs,
allow_async_output_proc
) = self.scheduler[virtual_engine].schedule()
ctx.seq_group_metadata_list = seq_group_metadata_list
ctx.scheduler_outputs = scheduler_outputs
finished_requests_ids = self.scheduler[
virtual_engine].get_and_reset_finished_requests_ids()
# When n>1, elements in self.seq_id_to_seq_group should be deleted
# here, otherwise memory leaks.
for finished_request_id in finished_requests_ids:
if finished_request_id in self.seq_id_to_seq_group:
del self.seq_id_to_seq_group[finished_request_id]
# Maybe switch from async mode to sync mode
if not allow_async_output_proc and len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
else:
finished_requests_ids = list()
assert seq_group_metadata_list is not None
assert scheduler_outputs is not None
if not scheduler_outputs.is_empty():
# Check if we have a cached last_output from the previous iteration.
# For supporting PP this is probably the best way to pass the
# sampled_token_ids, as a separate broadcast over all the PP stages
# will cause one virtual engine's microbatch to block the pipeline.
last_sampled_token_ids = \
self._get_last_sampled_token_ids(virtual_engine)
execute_model_req = ExecuteModelRequest(
seq_group_metadata_list=seq_group_metadata_list,
blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
blocks_to_copy=scheduler_outputs.blocks_to_copy,
num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
running_queue_size=scheduler_outputs.running_queue_size,
finished_requests_ids=finished_requests_ids,
# We use ExecuteModelRequest to pass the last sampled_token_ids
# to each of the non-last PP stages for in-place prepare_input.
last_sampled_token_ids=last_sampled_token_ids)
if allow_async_output_proc:
execute_model_req.async_callback = self.async_callbacks[
virtual_engine]
try:
outputs = self.model_executor.execute_model(
execute_model_req=execute_model_req)
self._skip_scheduling_next_step = False
except InputProcessingError as e:
# The input for this request cannot be processed, so we must
# abort it. If there are remaining requests in the batch that
# have been scheduled, they will be retried on the next step.
invalid_request_id = e.request_id
self._abort_and_cache_schedule(
request_id=invalid_request_id,
virtual_engine=virtual_engine,
seq_group_metadata_list=seq_group_metadata_list,
scheduler_outputs=scheduler_outputs,
allow_async_output_proc=allow_async_output_proc)
# Raise so the caller is notified that this request failed
raise
else:
# Nothing scheduled => If there is pending async postprocessor,
# then finish it here.
if len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
# No outputs in this case
outputs = []
if not self._has_remaining_steps(seq_group_metadata_list):
# is_first_step_output is True only when the num_steps of all
# 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
# Add results to the output_queue
ctx.append_output(outputs=outputs,
seq_group_metadata_list=seq_group_metadata_list,
scheduler_outputs=scheduler_outputs,
is_async=allow_async_output_proc,
is_last_step=True,
is_first_step_output=is_first_step_output)
if outputs and allow_async_output_proc:
assert len(outputs) == 1, (
"Async postprocessor expects only a single output set")
self._advance_to_next_step(
outputs[0], seq_group_metadata_list,
scheduler_outputs.scheduled_seq_groups)
# Check if need to run the usual non-async path
if not allow_async_output_proc:
self._process_model_outputs(ctx=ctx)
# Log stats.
self.do_log_stats(scheduler_outputs, outputs)
# Tracing
self.do_tracing(scheduler_outputs)
else:
# Multi-step case
return ctx.request_outputs
if not self.has_unfinished_requests():
# Drain async postprocessor (if exists)
if len(ctx.output_queue) > 0:
self._process_model_outputs(ctx=ctx)
assert len(ctx.output_queue) == 0
# Stop the execute model loop in parallel workers until there are
# more requests to process. This avoids waiting indefinitely in
# torch.distributed ops which may otherwise time out, and unblocks
# the RPC thread in the workers so that they can process any other
# queued control plane messages, such as add/remove lora adapters.
logger.debug("Stopping remote worker execution loop.")
self.model_executor.stop_remote_worker_execution_loop()
return ctx.request_outputs
def _abort_and_cache_schedule(
self, request_id: str, virtual_engine: int,
seq_group_metadata_list: List[SequenceGroupMetadata],
scheduler_outputs: SchedulerOutputs,
allow_async_output_proc: bool) -> None:
"""Aborts a single request, and caches the scheduler outputs minus that
request. This allows the next step to continue processing the remaining
requests without having to re-run the scheduler."""
# Abort the request and remove its sequence group from the current
# schedule
self.abort_request(request_id)
for i, metadata in enumerate(seq_group_metadata_list):
if metadata.request_id == request_id:
del seq_group_metadata_list[i]
break
for i, group in enumerate(scheduler_outputs.scheduled_seq_groups):
if group.seq_group.request_id == request_id:
del scheduler_outputs.scheduled_seq_groups[i]
break
# If there are still other sequence groups left in the schedule, cache
# them and flag the engine to reuse the schedule.
if len(seq_group_metadata_list) > 0:
self._skip_scheduling_next_step = True
# Reuse multi-step caching logic
self._cache_scheduler_outputs_for_multi_step(
virtual_engine=virtual_engine,
scheduler_outputs=scheduler_outputs,
seq_group_metadata_list=seq_group_metadata_list,
allow_async_output_proc=allow_async_output_proc)
def _has_remaining_steps(
self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
) -> bool:
return False
def _cache_scheduler_outputs_for_multi_step(
self, virtual_engine: int,
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
scheduler_outputs: SchedulerOutputs,
allow_async_output_proc: bool) -> None:
co = self.cached_scheduler_outputs[virtual_engine]
co.seq_group_metadata_list = seq_group_metadata_list
co.scheduler_outputs = scheduler_outputs
co.allow_async_output_proc = allow_async_output_proc
co.last_output = None
def _update_cached_scheduler_output(
self, virtual_engine: int,
output: List[Optional[SamplerOutput]]) -> None:
if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0
and output[0] is not None):
last_output = output[-1]
assert last_output is not None
assert last_output.sampled_token_ids_cpu is not None
assert last_output.sampled_token_ids is None
assert last_output.sampled_token_probs is None
self.cached_scheduler_outputs[
virtual_engine].last_output = last_output
def _get_last_sampled_token_ids(
self, virtual_engine: int) -> Optional[torch.Tensor]:
return None
def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
if not self.log_stats:
raise RuntimeError(
"Stat logging is disabled. Set `disable_log_stats=False` "
"argument to enable.")
if logger_name in self.stat_loggers:
raise KeyError(f"Logger with name {logger_name} already exists.")
self.stat_loggers[logger_name] = logger
def remove_logger(self, logger_name: str) -> None:
if not self.log_stats:
raise RuntimeError(
"Stat logging is disabled. Set `disable_log_stats=False` "
"argument to enable.")
if logger_name not in self.stat_loggers:
raise KeyError(f"Logger with name {logger_name} does not exist.")
del self.stat_loggers[logger_name]
def do_log_stats(self,
scheduler_outputs: Optional[SchedulerOutputs] = None,
model_output: Optional[List[SamplerOutput]] = None,
finished_before: Optional[List[int]] = None,
skip: Optional[List[int]] = None) -> None:
"""Forced log when no requests active."""
if self.log_stats:
stats = self._get_stats(scheduler_outputs, model_output,
finished_before, skip)
for logger in self.stat_loggers.values():
logger.log(stats)
def _get_stats(self,
scheduler_outputs: Optional[SchedulerOutputs],
model_output: Optional[List[SamplerOutput]] = None,
finished_before: Optional[List[int]] = None,
skip: Optional[List[int]] = None) -> Stats:
"""Get Stats to be Logged to Prometheus.
Args:
scheduler_outputs: Optional, used to populate metrics related to
the scheduled batch,
model_output: Optional, used to emit speculative decoding metrics
which are created by the workers.
finished_before: Optional, indices of sequences that were finished
before. These sequences will be ignored.
skip: Optional, indices of sequences that were preempted. These
sequences will be ignored.
"""
now = time.time()
# System State
# Scheduler State
num_running_sys = sum(
len(scheduler.running) for scheduler in self.scheduler)
num_swapped_sys = sum(
len(scheduler.swapped) for scheduler in self.scheduler)
num_waiting_sys = sum(
len(scheduler.waiting) for scheduler in self.scheduler)
# KV Cache Usage in %
num_total_gpu = self.cache_config.num_gpu_blocks
gpu_cache_usage_sys = 0.
if num_total_gpu: # Guard against both None and 0
num_free_gpu = sum(
scheduler.block_manager.get_num_free_gpu_blocks()
for scheduler in self.scheduler)
gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)
num_total_cpu = self.cache_config.num_cpu_blocks
cpu_cache_usage_sys = 0.
if num_total_cpu: # Guard against both None and 0
num_free_cpu = sum(
scheduler.block_manager.get_num_free_cpu_blocks()
for scheduler in self.scheduler)
cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu)
# Prefix Cache Hit Rate. Note that we always use
# the cache hit rate of the first virtual engine.
cpu_prefix_cache_hit_rate = self.scheduler[
0].get_prefix_cache_hit_rate(Device.CPU)
gpu_prefix_cache_hit_rate = self.scheduler[
0].get_prefix_cache_hit_rate(Device.GPU)
# Exchange the uasge and cache hit stats between gpu and cpu when
# running on cpu because the cpu_worker.py intentionally reports the
# number of cpu blocks as gpu blocks in favor of cache management.
if self.device_config.device_type == "cpu":
num_total_gpu, num_total_cpu = num_total_cpu, num_total_gpu
gpu_cache_usage_sys, cpu_cache_usage_sys = (
cpu_cache_usage_sys,
gpu_cache_usage_sys,
)
gpu_prefix_cache_hit_rate, cpu_prefix_cache_hit_rate = (
cpu_prefix_cache_hit_rate,
gpu_prefix_cache_hit_rate,
)
# Iteration stats
num_prompt_tokens_iter = 0
num_generation_tokens_iter = 0
num_tokens_iter = 0
time_to_first_tokens_iter: List[float] = []
inter_token_latencies_iter: List[float] = []
num_preemption_iter = (0 if scheduler_outputs is None else
scheduler_outputs.preempted)
# Request stats
# Latency
time_e2e_requests: List[float] = []
time_queue_requests: List[float] = []
time_inference_requests: List[float] = []
time_prefill_requests: List[float] = []
time_decode_requests: List[float] = []
# Metadata
num_prompt_tokens_requests: List[int] = []
num_generation_tokens_requests: List[int] = []
n_requests: List[int] = []
max_num_generation_tokens_requests: List[int] = []
max_tokens_requests: List[int] = []
finished_reason_requests: List[str] = []
# LoRA requests
running_lora_adapters = dict(
collectionsCounter([
running_request.lora_request.lora_name
for scheduler in self.scheduler
for running_request in scheduler.running
if running_request.lora_request
]))
waiting_lora_adapters = dict(
collectionsCounter([
waiting_request.lora_request.lora_name
for scheduler in self.scheduler
for waiting_request in scheduler.waiting
if waiting_request.lora_request
]))
max_lora_stat = "0"
if self.lora_config:
max_lora_stat = str(self.lora_config.max_loras)
# NOTE: This loop assumes prefill seq_groups are before
# decode seq_groups in scheduled_seq_groups.
if scheduler_outputs is not None:
# For async postprocessor, already finished sequences need to be
# not counted (to avoid double counting)
actual_num_batched_tokens = scheduler_outputs.num_batched_tokens # type: ignore
num_generation_tokens_from_prefill_groups = 0
# NOTE: if scheduler_outputs.num_prefill_groups > 0 and
# the len of scheduler_outputs.scheduled_seq_groups is !=
# scheduler_outputs.num_prefill_groups, this means that
# chunked prefills have been detected.
for idx, scheduled_seq_group in enumerate(
scheduler_outputs.scheduled_seq_groups):
# Skip double logging when using async output proc
if finished_before and idx in finished_before:
actual_num_batched_tokens -= 1
continue
# Currently, skip == preempted sequences, so we need to skip
# their log stats
if skip and idx in skip:
continue
group_was_prefill = idx < scheduler_outputs.num_prefill_groups
seq_group = scheduled_seq_group.seq_group
# NOTE: a seq_group that completed all of its prefill tokens
# in the last iteration will have seq_group.is_prefill() = False
# with group_was_prefill = True
if group_was_prefill:
# Number of prompt tokens.
num_prompt_tokens_iter += (
scheduled_seq_group.token_chunk_size)
# If the seq_group just finished the prefill state
# get TTFT.
if not seq_group.is_prefill():
latency = seq_group.get_last_token_latency()
time_to_first_tokens_iter.append(latency)
# One generation token per finished prefill.
num_generation_tokens_from_prefill_groups += (
seq_group.num_seqs())
else:
# ITLs
latency = seq_group.get_last_token_latency()
inter_token_latencies_iter.append(latency)
if seq_group.state.current_step == 0:
# For async_output_proc, the do_log_stats()
# is called following init_multi_step(), which
# sets the current_step to zero.
actual_num_batched_tokens +=\
seq_group.state.num_steps - 1
else:
actual_num_batched_tokens +=\
seq_group.state.current_step - 1
# Because of chunked prefill, we can have a single sequence
# group that does multiple prompt_runs. To prevent logging
# the same metadata more than once per request, we standardize
# on logging request level information for finished requests,
# which can only happen once.
if seq_group.is_finished():
# Latency timings
time_e2e_requests.append(now -
seq_group.metrics.arrival_time)
if (seq_group.metrics.first_scheduled_time is not None and
seq_group.metrics.first_token_time is not None):
time_queue_requests.append(
seq_group.metrics.first_scheduled_time -
seq_group.metrics.arrival_time)
time_prefill_requests.append(
seq_group.metrics.first_token_time -
seq_group.metrics.first_scheduled_time)
time_decode_requests.append(
now - seq_group.metrics.first_token_time)
time_inference_requests.append(
now - seq_group.metrics.first_scheduled_time)
# Metadata
num_prompt_tokens_requests.append(
len(seq_group.prompt_token_ids))
num_generation_tokens_requests.extend([
seq.get_output_len()
for seq in seq_group.get_finished_seqs()
])
max_num_generation_tokens_requests.append(
max(seq.get_output_len()
for seq in seq_group.get_seqs()))
if seq_group.sampling_params is not None:
n_requests.append(seq_group.sampling_params.n)
max_tokens_requests.append(
seq_group.sampling_params.max_tokens)
finished_reason_requests.extend([
SequenceStatus.get_finished_reason(seq.status)
for seq in seq_group.get_finished_seqs()
])
# Number of generation tokens.
# num_batched_tokens equals the number of prompt_tokens plus the
# number of decode_tokens in a single iteration. So,
# num_generation_tokens = num_batched_tokens - num_prompt_tokens
# + num_generation_tokens_from_prefill_groups (since we generate
# one token on prefills on iters where the prefill finishes).
num_generation_tokens_iter = (
actual_num_batched_tokens - num_prompt_tokens_iter +
num_generation_tokens_from_prefill_groups)
num_tokens_iter = (num_generation_tokens_iter +
num_prompt_tokens_iter)
return Stats(
now=now,
# System stats
# Scheduler State
num_running_sys=num_running_sys,
num_swapped_sys=num_swapped_sys,
num_waiting_sys=num_waiting_sys,
# KV Cache Usage in %
gpu_cache_usage_sys=gpu_cache_usage_sys,
cpu_cache_usage_sys=cpu_cache_usage_sys,
# Prefix Cache Hit Rate
cpu_prefix_cache_hit_rate=cpu_prefix_cache_hit_rate,
gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate,
# Iteration stats
num_prompt_tokens_iter=num_prompt_tokens_iter,
num_generation_tokens_iter=num_generation_tokens_iter,
num_tokens_iter=num_tokens_iter,
time_to_first_tokens_iter=time_to_first_tokens_iter,
inter_token_latencies_iter=inter_token_latencies_iter,
num_preemption_iter=num_preemption_iter,
# Request stats
# Latency
time_e2e_requests=time_e2e_requests,
time_queue_requests=time_queue_requests,
time_inference_requests=time_inference_requests,
time_prefill_requests=time_prefill_requests,
time_decode_requests=time_decode_requests,
# Metadata
num_prompt_tokens_requests=num_prompt_tokens_requests,
num_generation_tokens_requests=num_generation_tokens_requests,
max_num_generation_tokens_requests=
max_num_generation_tokens_requests,
n_requests=n_requests,
max_tokens_requests=max_tokens_requests,
finished_reason_requests=finished_reason_requests,
max_lora=str(max_lora_stat),
waiting_lora_adapters=list(waiting_lora_adapters.keys()),
running_lora_adapters=list(running_lora_adapters.keys()))
def add_lora(self, lora_request: LoRARequest) -> bool:
return self.model_executor.add_lora(lora_request)
def remove_lora(self, lora_id: int) -> bool:
return self.model_executor.remove_lora(lora_id)
def list_loras(self) -> Set[int]:
return self.model_executor.list_loras()
def pin_lora(self, lora_id: int) -> bool:
return self.model_executor.pin_lora(lora_id)
def start_profile(self) -> None:
self.model_executor.start_profile()
def stop_profile(self) -> None:
self.model_executor.stop_profile()
def sleep(self, level: int = 1) -> None:
assert self.vllm_config.model_config.enable_sleep_mode, (
"Sleep mode is not enabled in the model config")
self.model_executor.sleep(level=level)
def wake_up(self, tags: Optional[list[str]] = None) -> None:
assert self.vllm_config.model_config.enable_sleep_mode, (
"Sleep mode is not enabled in the model config")
self.model_executor.wake_up(tags)
def is_sleeping(self) -> bool:
return self.model_executor.is_sleeping
def check_health(self) -> None:
self.model_executor.check_health()
def is_tracing_enabled(self) -> bool:
return self.tracer is not None
def do_tracing(self,
scheduler_outputs: SchedulerOutputs,
finished_before: Optional[List[int]] = None) -> None:
if self.tracer is None:
return
for idx, scheduled_seq_group in enumerate(
scheduler_outputs.scheduled_seq_groups):
# Skip double tracing when using async output proc
if finished_before and idx in finished_before:
continue
seq_group = scheduled_seq_group.seq_group
if seq_group.is_finished():
self.create_trace_span(seq_group)
def create_trace_span(self, seq_group: SequenceGroup) -> None:
if self.tracer is None or seq_group.sampling_params is None:
return
arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9)
trace_context = extract_trace_context(seq_group.trace_headers)
with self.tracer.start_as_current_span(
"llm_request",
kind=SpanKind.SERVER,
context=trace_context,
start_time=arrival_time_nano_seconds) as seq_span:
metrics = seq_group.metrics
# Handle potential None values for cancelled/aborted requests
ttft = (metrics.first_token_time - metrics.arrival_time
if metrics.first_token_time is not None else None)
e2e_time = (metrics.finished_time - metrics.arrival_time
if metrics.finished_time is not None else None)
seq_span.set_attribute(SpanAttributes.GEN_AI_RESPONSE_MODEL,
self.model_config.model)
seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID,
seq_group.request_id)
seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE,
seq_group.sampling_params.temperature)
seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P,
seq_group.sampling_params.top_p)
seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS,
seq_group.sampling_params.max_tokens)
seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N,
seq_group.sampling_params.n)
seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_NUM_SEQUENCES,
seq_group.num_seqs())
seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS,
len(seq_group.prompt_token_ids))
seq_span.set_attribute(
SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS,
sum([
seq.get_output_len()
for seq in seq_group.get_finished_seqs()
]))
# Only set timing attributes if the values are available
if metrics.time_in_queue is not None:
seq_span.set_attribute(
SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE,
metrics.time_in_queue)
if ttft is not None:
seq_span.set_attribute(
SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
if e2e_time is not None:
seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E,
e2e_time)
if metrics.scheduler_time is not None:
seq_span.set_attribute(
SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER,
metrics.scheduler_time)
if metrics.model_forward_time is not None:
seq_span.set_attribute(
SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD,
metrics.model_forward_time / 1000.0)
if metrics.model_execute_time is not None:
seq_span.set_attribute(
SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE,
metrics.model_execute_time)
def _validate_model_inputs(self, inputs: ProcessorInputs):
encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
if encoder_inputs is not None:
self._validate_model_input(encoder_inputs, prompt_type="encoder")
self._validate_model_input(decoder_inputs, prompt_type="decoder")
def _validate_model_input(
self,
prompt_inputs: SingletonInputs,
*,
prompt_type: Literal["encoder", "decoder"],
):
model_config = self.model_config
tokenizer = self.tokenizer
prompt_ids = prompt_inputs.get("prompt_token_ids", [])
if not prompt_ids:
if prompt_type == "encoder" and model_config.is_multimodal_model:
pass # Mllama may have empty encoder inputs for text-only data
elif prompt_inputs["type"] == "embeds":
pass
else:
raise ValueError(f"The {prompt_type} prompt cannot be empty")
if tokenizer is not None:
max_input_id = max(prompt_ids, default=0)
if max_input_id > tokenizer.max_token_id:
raise ValueError(
f"Token id {max_input_id} is out of vocabulary")
max_prompt_len = self.model_config.max_model_len
if len(prompt_ids) > max_prompt_len:
if prompt_type == "encoder" and model_config.is_multimodal_model:
mm_registry = self.input_preprocessor.mm_registry
mm_processor = mm_registry.create_processor(
model_config,
tokenizer=tokenizer or object(), # Dummy if no tokenizer
)
assert isinstance(mm_processor, EncDecMultiModalProcessor)
if mm_processor.pad_dummy_encoder_prompt:
return # Skip encoder length check for Whisper
if model_config.is_multimodal_model:
suggestion = (
"Make sure that `max_model_len` is no smaller than the "
"number of text tokens plus multimodal tokens. For image "
"inputs, the number of image tokens depends on the number "
"of images, and possibly their aspect ratios as well.")
else:
suggestion = (
"Make sure that `max_model_len` is no smaller than the "
"number of text tokens.")
raise ValueError(
f"The {prompt_type} prompt (length {len(prompt_ids)}) is "
f"longer than the maximum model length of {max_prompt_len}. "
f"{suggestion}")
# TODO: Find out how many placeholder tokens are there so we can
# check that chunked prefill does not truncate them
# max_batch_len = self.scheduler_config.max_num_batched_tokens
def _build_logits_processors(
self, sampling_params: SamplingParams,
lora_request: Optional[LoRARequest]) -> SamplingParams:
"""Constructs logits processors based on the logits_bias, and
allowed_token_ids fields in sampling_params. Deletes those fields and
adds the constructed logits processors to the logits_processors field.
Returns the modified sampling params."""
logits_processors = []
if (sampling_params.logit_bias or sampling_params.allowed_token_ids):
tokenizer = self.get_tokenizer()
processors = get_openai_logits_processors(
logit_bias=sampling_params.logit_bias,
allowed_token_ids=sampling_params.allowed_token_ids,
tokenizer=tokenizer)
logits_processors.extend(processors)
# Unset so these don't get passed down to the model
sampling_params.logit_bias = None
sampling_params.allowed_token_ids = None
if len(sampling_params.bad_words) > 0:
tokenizer = self.get_tokenizer()
processors = get_bad_words_logits_processors(
bad_words=sampling_params.bad_words, tokenizer=tokenizer)
logits_processors.extend(processors)
if logits_processors:
if sampling_params.logits_processors is None:
sampling_params.logits_processors = logits_processors
else:
sampling_params.logits_processors.extend(logits_processors)
return sampling_params
def collective_rpc(self,
method: Union[str, Callable[[WorkerBase], _R]],
timeout: Optional[float] = None,
args: tuple = (),
kwargs: Optional[dict[str, Any]] = None) -> list[_R]:
return self.model_executor.collective_rpc(method, timeout, args,
kwargs)
def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:
return self.collective_rpc("apply_model", args=(func, ))
if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
LLMEngine = V1LLMEngine # type: ignore
vllm/entrypoints/llm.py
View file @ 52c2a8d4
...@@ -11,7 +11,6 @@ from pydantic import ValidationError ...@@ -11,7 +11,6 @@ from pydantic import ValidationError
from tqdm.auto import tqdm from tqdm.auto import tqdm
from typing_extensions import TypeVar from typing_extensions import TypeVar
import vllm.envs as envs
from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput, from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
BeamSearchSequence, BeamSearchSequence,
create_sort_beams_key_function) create_sort_beams_key_function)
...@@ -19,7 +18,6 @@ from vllm.config import (CompilationConfig, ModelDType, ...@@ -19,7 +18,6 @@ from vllm.config import (CompilationConfig, ModelDType,
StructuredOutputsConfig, TokenizerMode, is_init_field) StructuredOutputsConfig, TokenizerMode, is_init_field)
from vllm.engine.arg_utils import (ConvertOption, EngineArgs, HfOverrides, from vllm.engine.arg_utils import (ConvertOption, EngineArgs, HfOverrides,
PoolerConfig, RunnerOption) PoolerConfig, RunnerOption)
from vllm.engine.llm_engine import LLMEngine
from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam, from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
ChatTemplateContentFormatOption, ChatTemplateContentFormatOption,
apply_hf_chat_template, apply_hf_chat_template,
...@@ -54,6 +52,7 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer, ...@@ -54,6 +52,7 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
get_cached_tokenizer) get_cached_tokenizer)
from vllm.usage.usage_lib import UsageContext from vllm.usage.usage_lib import UsageContext
from vllm.utils import Counter, Device, as_iter, is_list_of from vllm.utils import Counter, Device, as_iter, is_list_of
from vllm.v1.engine.llm_engine import LLMEngine
from vllm.v1.sample.logits_processor import LogitsProcessor from vllm.v1.sample.logits_processor import LogitsProcessor
if TYPE_CHECKING: if TYPE_CHECKING:
...@@ -309,11 +308,7 @@ class LLM: ...@@ -309,11 +308,7 @@ class LLM:
self.request_counter = Counter() self.request_counter = Counter()
self.default_sampling_params: Union[dict[str, Any], None] = None self.default_sampling_params: Union[dict[str, Any], None] = None
if envs.VLLM_USE_V1: supported_tasks = self.llm_engine.get_supported_tasks() # type: ignore
supported_tasks = self.llm_engine \
.get_supported_tasks() # type: ignore
else:
supported_tasks = self.llm_engine.model_config.supported_tasks
logger.info("Supported_tasks: %s", supported_tasks) logger.info("Supported_tasks: %s", supported_tasks)
...@@ -1473,8 +1468,6 @@ class LLM: ...@@ -1473,8 +1468,6 @@ class LLM:
Note: Note:
This method is only available with the V1 LLM engine. This method is only available with the V1 LLM engine.
""" """
from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
assert isinstance(self.llm_engine, V1LLMEngine)
return self.llm_engine.get_metrics() return self.llm_engine.get_metrics()
def _validate_and_add_requests( def _validate_and_add_requests(
......
vllm/model_executor/model_loader/tensorizer.py
View file @ 52c2a8d4
...@@ -672,21 +672,15 @@ def tensorize_vllm_model(engine_args: "EngineArgs", ...@@ -672,21 +672,15 @@ def tensorize_vllm_model(engine_args: "EngineArgs",
) as stream: ) as stream:
stream.write(encryption_params.key) stream.write(encryption_params.key)
from vllm import LLMEngine assert envs.VLLM_USE_V1
from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
from vllm.v1.engine.llm_engine import LLMEngine
if not envs.VLLM_USE_V1:
engine = LLMEngine.from_engine_args(engine_args) engine = LLMEngine.from_vllm_config(engine_config)
engine.model_executor.collective_rpc( engine.collective_rpc(
"save_tensorized_model", "save_tensorized_model",
kwargs={"tensorizer_config": tensorizer_config.to_serializable()}, kwargs={"tensorizer_config": tensorizer_config.to_serializable()},
) )
else:
engine = V1LLMEngine.from_vllm_config(engine_config)
engine.collective_rpc(
"save_tensorized_model",
kwargs={"tensorizer_config": tensorizer_config.to_serializable()},
)
def tensorize_lora_adapter(lora_path: str, def tensorize_lora_adapter(lora_path: str,
......
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