LlamaGen / serve /llm_engine.py
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# Modified from:
# vLLM: https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py
import time
from typing import Iterable, List, Optional, Type, Union
import argparse
from transformers import GenerationConfig, PreTrainedTokenizer
import vllm
from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
LoRAConfig, ModelConfig, ParallelConfig,
SchedulerConfig, SpeculativeConfig,
VisionLanguageConfig)
from vllm.core.scheduler import Scheduler, SchedulerOutputs
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.metrics import StatLogger, 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.engine.ray_utils import initialize_ray_cluster
from vllm.executor.executor_base import ExecutorBase
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
from vllm.sequence import (MultiModalData, SamplerOutput, Sequence,
SequenceGroup)
from vllm.transformers_utils.detokenizer import Detokenizer
from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
get_tokenizer_group)
from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
usage_message)
from vllm.utils import Counter
logger = init_logger(__name__)
_LOCAL_LOGGING_INTERVAL_SEC = 5
def _load_generation_config_dict(model_config: ModelConfig):
try:
return GenerationConfig.from_pretrained(
model_config.model,
revision=model_config.revision,
).to_diff_dict()
except OSError:
# Not found.
return {}
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` class wraps this class for offline batched inference and the
`AsyncLLMEngine` class wraps this class for online serving.
NOTE: The config arguments are derived from the `EngineArgs` class. For the
comprehensive list of arguments, see `EngineArgs`.
Args:
model_config: The configuration related to the LLM model.
cache_config: The configuration related to the KV cache memory
management.
parallel_config: The configuration related to distributed execution.
scheduler_config: The configuration related to the request scheduler.
device_config: The configuration related to the device.
lora_config (Optional): The configuration related to serving multi-LoRA.
vision_language_config (Optional): The configuration related to vision
language models.
speculative_config (Optional): The configuration related to speculative
decoding.
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
"""
def __init__(
self,
args: argparse.ArgumentParser,
model_config: ModelConfig,
cache_config: CacheConfig,
parallel_config: ParallelConfig,
scheduler_config: SchedulerConfig,
device_config: DeviceConfig,
load_config: LoadConfig,
lora_config: Optional[LoRAConfig],
vision_language_config: Optional[VisionLanguageConfig],
speculative_config: Optional[SpeculativeConfig],
decoding_config: Optional[DecodingConfig],
executor_class: Type[ExecutorBase],
log_stats: bool,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
) -> None:
logger.info(
f"Initializing an LLM engine (v{vllm.__version__}) with config: "
f"model={model_config.model!r}, "
f"speculative_config={speculative_config!r}, "
f"tokenizer={model_config.tokenizer!r}, "
f"skip_tokenizer_init={model_config.skip_tokenizer_init}, "
f"tokenizer_mode={model_config.tokenizer_mode}, "
f"revision={model_config.revision}, "
f"tokenizer_revision={model_config.tokenizer_revision}, "
f"trust_remote_code={model_config.trust_remote_code}, "
f"dtype={model_config.dtype}, "
f"max_seq_len={model_config.max_model_len}, "
f"download_dir={load_config.download_dir!r}, "
f"load_format={load_config.load_format}, "
f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
f"disable_custom_all_reduce="
f"{parallel_config.disable_custom_all_reduce}, "
f"quantization={model_config.quantization}, "
f"enforce_eager={model_config.enforce_eager}, "
f"kv_cache_dtype={cache_config.cache_dtype}, "
f"quantization_param_path={model_config.quantization_param_path}, "
f"device_config={device_config.device}, "
f"decoding_config={decoding_config!r}, "
f"seed={model_config.seed})")
# TODO(woosuk): Print more configs in debug mode.
self.model_config = model_config
self.cache_config = cache_config
self.lora_config = lora_config
self.vision_language_config = vision_language_config
self.parallel_config = parallel_config
self.scheduler_config = scheduler_config
self.device_config = device_config
self.speculative_config = speculative_config
self.load_config = load_config
self.decoding_config = decoding_config or DecodingConfig()
self.log_stats = log_stats
if not self.model_config.skip_tokenizer_init:
self.tokenizer: BaseTokenizerGroup
self._init_tokenizer()
self.detokenizer = Detokenizer(self.tokenizer)
else:
self.detokenizer = None
self.tokenizer = None
self.seq_counter = Counter()
self.generation_config_fields = _load_generation_config_dict(
model_config)
self.model_executor = executor_class(
args=args,
model_config=model_config,
cache_config=cache_config,
parallel_config=parallel_config,
scheduler_config=scheduler_config,
device_config=device_config,
lora_config=lora_config,
vision_language_config=vision_language_config,
speculative_config=speculative_config,
load_config=load_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(model_config),
usage_context,
extra_kvs={
# Common configuration
"dtype":
str(model_config.dtype),
"tensor_parallel_size":
parallel_config.tensor_parallel_size,
"block_size":
cache_config.block_size,
"gpu_memory_utilization":
cache_config.gpu_memory_utilization,
# Quantization
"quantization":
model_config.quantization,
"kv_cache_dtype":
cache_config.cache_dtype,
# Feature flags
"enable_lora":
bool(lora_config),
"enable_prefix_caching":
cache_config.enable_prefix_caching,
"enforce_eager":
model_config.enforce_eager,
"disable_custom_all_reduce":
parallel_config.disable_custom_all_reduce,
})
if self.tokenizer:
# Ping the tokenizer to ensure liveness if it runs in a
# different process.
self.tokenizer.ping()
# 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.
self.scheduler = Scheduler(scheduler_config, cache_config, lora_config)
# Metric Logging.
if self.log_stats:
self.stat_logger = StatLogger(
local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
labels=dict(model_name=model_config.model))
self.stat_logger.info("cache_config", self.cache_config)
# 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,
self.get_tokenizer_for_seq,
stop_checker=StopChecker(
self.scheduler_config.max_model_len,
self.get_tokenizer_for_seq,
),
))
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.
"""
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(f"Overriding {num_gpu_blocks=} with "
f"{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)
@classmethod
def from_engine_args(
cls,
engine_args: EngineArgs,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
args: argparse.ArgumentParser = None,
) -> "LLMEngine":
"""Creates an LLM engine from the engine arguments."""
# Create the engine configs.
engine_config = engine_args.create_engine_config()
# Initialize the cluster and specify the executor class.
if engine_config.device_config.device_type == "neuron":
from vllm.executor.neuron_executor import NeuronExecutor
executor_class = NeuronExecutor
elif engine_config.device_config.device_type == "cpu":
from vllm.executor.cpu_executor import CPUExecutor
executor_class = CPUExecutor
elif engine_config.parallel_config.worker_use_ray:
initialize_ray_cluster(engine_config.parallel_config)
from vllm.executor.ray_gpu_executor import RayGPUExecutor
executor_class = RayGPUExecutor
else:
assert engine_config.parallel_config.world_size == 1, (
"Ray is required if parallel_config.world_size > 1.")
# from vllm.executor.gpu_executor import GPUExecutor
from serve.gpu_executor import GPUExecutor
executor_class = GPUExecutor
# Create the LLM engine.
engine = cls(
**engine_config.to_dict(),
executor_class=executor_class,
log_stats=not engine_args.disable_log_stats,
usage_context=usage_context,
args=args,
)
return engine
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 get_tokenizer(self) -> "PreTrainedTokenizer":
return self.tokenizer.get_lora_tokenizer(None)
def get_tokenizer_for_seq(self,
sequence: Sequence) -> "PreTrainedTokenizer":
return self.tokenizer.get_lora_tokenizer(sequence.lora_request)
def _init_tokenizer(self, **tokenizer_init_kwargs):
init_kwargs = dict(
tokenizer_id=self.model_config.tokenizer,
enable_lora=bool(self.lora_config),
max_num_seqs=self.scheduler_config.max_num_seqs,
max_input_length=None,
tokenizer_mode=self.model_config.tokenizer_mode,
trust_remote_code=self.model_config.trust_remote_code,
revision=self.model_config.tokenizer_revision)
init_kwargs.update(tokenizer_init_kwargs)
self.tokenizer = get_tokenizer_group(
self.parallel_config.tokenizer_pool_config, **init_kwargs)
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 encode_request(
self,
request_id: str, # pylint: disable=unused-argument
prompt: Optional[str],
prompt_token_ids: Optional[List[int]] = None,
lora_request: Optional[LoRARequest] = None,
):
if prompt_token_ids is None:
assert prompt is not None
prompt_token_ids = self.tokenizer.encode(request_id=request_id,
prompt=prompt,
lora_request=lora_request)
return prompt_token_ids
def add_request(
self,
request_id: str,
prompt: Optional[str],
sampling_params: SamplingParams,
prompt_token_ids: Optional[List[int]] = None,
arrival_time: Optional[float] = None,
lora_request: Optional[LoRARequest] = None,
multi_modal_data: Optional[MultiModalData] = None,
) -> 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 string. Can be None if prompt_token_ids is
provided.
sampling_params: The sampling parameters for text generation.
prompt_token_ids: The token IDs of the prompt. If None, we
use the tokenizer to convert the prompts to token IDs.
arrival_time: The arrival time of the request. If None, we use
the current monotonic time.
multi_modal_data: Multi modal data per request.
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 `best_of` number of :class:`~vllm.Sequence` objects.
- Create a :class:`~vllm.SequenceGroup` object
from the list of :class:`~vllm.Sequence`.
- Add the :class:`~vllm.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 lora_request is not None and not self.lora_config:
raise ValueError(f"Got lora_request {lora_request} but LoRA is "
"not enabled!")
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.")
if arrival_time is None:
arrival_time = time.time()
prompt_token_ids = self.encode_request(
request_id=request_id,
prompt=prompt,
prompt_token_ids=prompt_token_ids,
lora_request=lora_request)
# Create the sequences.
block_size = self.cache_config.block_size
seq_id = next(self.seq_counter)
eos_token_id = None
if self.tokenizer:
eos_token_id = self.tokenizer.get_lora_tokenizer(
lora_request).eos_token_id
else:
logger.warning("Use None for EOS token id because tokenizer is "
"not initialized")
seq = Sequence(seq_id, prompt, prompt_token_ids, block_size,
eos_token_id, lora_request)
# Defensive copy of SamplingParams, which are used by the sampler,
# this doesn't deep-copy LogitsProcessor objects
sampling_params = sampling_params.clone()
# Add the eos token id into the sampling_params to support min_tokens
# processing
if seq.eos_token_id is not None:
sampling_params.all_stop_token_ids.add(seq.eos_token_id)
sampling_params.update_from_generation_config(
self.generation_config_fields)
# Create the sequence group.
seq_group = SequenceGroup(request_id, [seq], sampling_params,
arrival_time, lora_request, multi_modal_data)
# Add the sequence group to the scheduler.
self.scheduler.add_seq_group(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 the
:meth:`~vllm.core.scheduler.Scheduler.abort_seq_group`
from class :class:`~vllm.core.scheduler.Scheduler`.
Example:
>>> # initialize engine and add a request with request_id
>>> request_id = str(0)
>>> # abort the request
>>> engine.abort_request(request_id)
"""
self.scheduler.abort_seq_group(request_id)
def get_model_config(self) -> ModelConfig:
"""Gets the model configuration."""
return self.model_config
def get_num_unfinished_requests(self) -> int:
"""Gets the number of unfinished requests."""
return self.scheduler.get_num_unfinished_seq_groups()
def has_unfinished_requests(self) -> bool:
"""Returns True if there are unfinished requests."""
return self.scheduler.has_unfinished_seqs()
def _process_model_outputs(
self, output: List[SamplerOutput],
scheduled_seq_groups: List[SequenceGroup],
ignored_seq_groups: List[SequenceGroup]) -> List[RequestOutput]:
"""Apply the model output to the sequences in the scheduled seq groups.
Returns RequestOutputs that can be returned to the client.
"""
now = time.time()
# Organize outputs by [sequence group][step] instead of
# [step][sequence group].
output_by_sequence_group = create_output_by_sequence_group(
sampler_outputs=output, num_seq_groups=len(scheduled_seq_groups))
# Update the scheduled sequence groups with the model outputs.
for scheduled_seq_group, outputs in zip(scheduled_seq_groups,
output_by_sequence_group):
seq_group = scheduled_seq_group.seq_group
seq_group.update_num_computed_tokens(
scheduled_seq_group.token_chunk_size)
# If uncomputed tokens > 0, it means prefill is chunked.
# We don't need to process outputs in that case.
if seq_group.get_num_uncomputed_tokens() == 0:
self.output_processor.process_outputs(seq_group, outputs)
# Free the finished sequence groups.
self.scheduler.free_finished_seq_groups()
# Create the outputs.
request_outputs: List[RequestOutput] = []
for scheduled_seq_group in scheduled_seq_groups:
seq_group = scheduled_seq_group.seq_group
seq_group.maybe_set_first_token_time(now)
request_output = RequestOutput.from_seq_group(seq_group)
request_outputs.append(request_output)
for seq_group in ignored_seq_groups:
request_output = RequestOutput.from_seq_group(seq_group)
request_outputs.append(request_output)
return request_outputs
def step(self) -> List[RequestOutput]:
"""Performs one decoding iteration and returns newly generated results.
.. figure:: https://i.imgur.com/sv2HssD.png
:alt: Overview of the step function
:align: center
Overview of the step function.
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
"""
seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
if not scheduler_outputs.is_empty():
output = self.model_executor.execute_model(
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)
else:
output = []
request_outputs = self._process_model_outputs(
output, scheduler_outputs.scheduled_seq_groups,
scheduler_outputs.ignored_seq_groups)
# Log stats.
if self.log_stats:
self.stat_logger.log(self._get_stats(scheduler_outputs))
return request_outputs
def do_log_stats(self) -> None:
"""Forced log when no requests active."""
if self.log_stats:
self.stat_logger.log(self._get_stats(scheduler_outputs=None))
def _get_stats(self,
scheduler_outputs: Optional[SchedulerOutputs]) -> Stats:
"""Get Stats to be Logged to Prometheus."""
now = time.time()
# KV Cache Usage in %.
num_total_gpu = self.cache_config.num_gpu_blocks
num_free_gpu = self.scheduler.block_manager.get_num_free_gpu_blocks()
gpu_cache_usage = 1.0 - (num_free_gpu / num_total_gpu)
num_total_cpu = self.cache_config.num_cpu_blocks
cpu_cache_usage = 0.
if num_total_cpu > 0:
num_free_cpu = self.scheduler.block_manager.get_num_free_cpu_blocks(
)
cpu_cache_usage = 1.0 - (num_free_cpu / num_total_cpu)
# Scheduler State
num_running = len(self.scheduler.running)
num_swapped = len(self.scheduler.swapped)
num_waiting = len(self.scheduler.waiting)
# Iteration stats if we have scheduler output.
num_prompt_tokens = 0
num_generation_tokens = 0
time_to_first_tokens = []
time_per_output_tokens = []
time_e2e_requests = []
if scheduler_outputs is not None:
prompt_run = scheduler_outputs.num_prefill_groups > 0
# Number of Tokens.
if prompt_run:
num_prompt_tokens = sum(
len(scheduled_seq_group.seq_group.prompt_token_ids)
for scheduled_seq_group in
scheduler_outputs.scheduled_seq_groups)
num_generation_tokens = sum(
scheduled_seq_group.seq_group.num_seqs()
for scheduled_seq_group in
scheduler_outputs.scheduled_seq_groups)
else:
num_generation_tokens = scheduler_outputs.num_batched_tokens
# Latency Timings.
time_last_iters = []
for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups:
seq_group = scheduled_seq_group.seq_group
# Time since last token.
# (n.b. updates seq_group.metrics.last_token_time)
time_last_iters.append(seq_group.get_last_latency(now))
# Time since arrival for all finished requests.
if seq_group.is_finished():
time_e2e_requests.append(now -
seq_group.metrics.arrival_time)
time_to_first_tokens = time_last_iters if prompt_run else []
time_per_output_tokens = [] if prompt_run else time_last_iters
return Stats(
now=now,
num_running=num_running,
num_swapped=num_swapped,
num_waiting=num_waiting,
gpu_cache_usage=gpu_cache_usage,
cpu_cache_usage=cpu_cache_usage,
num_prompt_tokens=num_prompt_tokens,
num_generation_tokens=num_generation_tokens,
time_to_first_tokens=time_to_first_tokens,
time_per_output_tokens=time_per_output_tokens,
time_e2e_requests=time_e2e_requests,
)
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) -> List[int]:
return self.model_executor.list_loras()
def check_health(self) -> None:
self.model_executor.check_health()