262 lines
10 KiB
Python
262 lines
10 KiB
Python
import time
|
|
from typing import Any, List, Optional
|
|
|
|
try:
|
|
import ray
|
|
except ImportError:
|
|
ray = None
|
|
|
|
from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
|
|
SchedulerConfig)
|
|
from cacheflow.core.scheduler import Scheduler
|
|
from cacheflow.logger import init_logger
|
|
from cacheflow.outputs import RequestOutput
|
|
from cacheflow.sampling_params import SamplingParams
|
|
from cacheflow.server.arg_utils import ServerArgs
|
|
from cacheflow.server.ray_utils import initialize_cluster
|
|
from cacheflow.server.tokenizer_utils import (get_tokenizer,
|
|
detokenize_incrementally)
|
|
from cacheflow.sequence import Sequence, SequenceGroup, SequenceStatus
|
|
from cacheflow.utils import Counter
|
|
from cacheflow.worker.worker import Worker
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class LLMServer:
|
|
|
|
def __init__(
|
|
self,
|
|
model_config: ModelConfig,
|
|
cache_config: CacheConfig,
|
|
parallel_config: ParallelConfig,
|
|
scheduler_config: SchedulerConfig,
|
|
distributed_init_method: str,
|
|
stage_devices: List[List[Any]],
|
|
log_stats: bool,
|
|
) -> None:
|
|
logger.info(
|
|
"Initializing an LLM server with config: "
|
|
f"model={model_config.model!r}, "
|
|
f"dtype={model_config.dtype}, "
|
|
f"use_dummy_weights={model_config.use_dummy_weights}, "
|
|
f"download_dir={model_config.download_dir!r}, "
|
|
f"use_np_weights={model_config.use_np_weights}, "
|
|
f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
|
|
f"seed={model_config.seed})"
|
|
)
|
|
# TODO(woosuk): Print more configs in debug mode.
|
|
|
|
self.model_config = model_config
|
|
self.cache_config = cache_config
|
|
self.parallel_config = parallel_config
|
|
self.scheduler_config = scheduler_config
|
|
self.log_stats = log_stats
|
|
self._verify_args()
|
|
|
|
self.tokenizer = get_tokenizer(model_config.model)
|
|
self.seq_counter = Counter()
|
|
|
|
# Create the parallel GPU workers.
|
|
self.workers: List[Worker] = []
|
|
assert len(stage_devices) == 1, "Only support one stage for now."
|
|
for rank, node_resource, _ in stage_devices[0]:
|
|
worker_cls = Worker
|
|
if self.parallel_config.use_ray:
|
|
worker_cls = ray.remote(
|
|
num_cpus=0,
|
|
num_gpus=1,
|
|
resources={node_resource: 1e-5},
|
|
)(worker_cls).remote
|
|
|
|
worker = worker_cls(
|
|
model_config,
|
|
parallel_config,
|
|
scheduler_config,
|
|
rank,
|
|
distributed_init_method,
|
|
)
|
|
self.workers.append(worker)
|
|
# Profile the memory usage and initialize the cache.
|
|
self._init_cache()
|
|
|
|
# Create the scheduler.
|
|
self.scheduler = Scheduler(scheduler_config, cache_config, log_stats)
|
|
|
|
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)
|
|
|
|
def _init_cache(self) -> None:
|
|
# Get the maximum number of blocks that can be allocated on GPU and CPU.
|
|
num_blocks = self._run_workers(
|
|
"profile_num_available_blocks",
|
|
get_all_outputs=True,
|
|
block_size=self.cache_config.block_size,
|
|
gpu_memory_utilization=self.cache_config.gpu_memory_utilization,
|
|
cpu_swap_space=self.cache_config.swap_space_bytes,
|
|
)
|
|
|
|
# Since we use a shared centralized controller, we take the minimum
|
|
# number of blocks across all workers to make sure all the memory
|
|
# operators can be applied to all workers.
|
|
num_gpu_blocks = min(b[0] for b in num_blocks)
|
|
num_cpu_blocks = min(b[1] for b in num_blocks)
|
|
# FIXME(woosuk): Change to debug log.
|
|
logger.info(f'# GPU blocks: {num_gpu_blocks}, '
|
|
f'# CPU blocks: {num_cpu_blocks}')
|
|
self.cache_config.num_gpu_blocks = num_gpu_blocks
|
|
self.cache_config.num_cpu_blocks = num_cpu_blocks
|
|
|
|
# Initialize the cache.
|
|
self._run_workers("init_cache_engine", cache_config=self.cache_config)
|
|
|
|
@classmethod
|
|
def from_server_args(cls, server_args: ServerArgs) -> "LLMServer":
|
|
# Create the server configs.
|
|
server_configs = server_args.create_server_configs()
|
|
parallel_config = server_configs[2]
|
|
# Initialize the cluster.
|
|
distributed_init_method, devices = initialize_cluster(parallel_config)
|
|
# Create the LLM server.
|
|
server = cls(*server_configs, distributed_init_method, devices,
|
|
log_stats=not server_args.disable_log_stats)
|
|
return server
|
|
|
|
def add_request(
|
|
self,
|
|
request_id: str,
|
|
prompt: str,
|
|
sampling_params: SamplingParams,
|
|
prompt_token_ids: Optional[List[int]] = None,
|
|
arrival_time: Optional[float] = None,
|
|
) -> None:
|
|
if arrival_time is None:
|
|
arrival_time = time.time()
|
|
if prompt_token_ids is None:
|
|
prompt_token_ids = self.tokenizer.encode(prompt)
|
|
|
|
# Create the sequences.
|
|
block_size = self.cache_config.block_size
|
|
seqs: List[Sequence] = []
|
|
for _ in range(sampling_params.best_of):
|
|
seq_id = next(self.seq_counter)
|
|
seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
|
|
seqs.append(seq)
|
|
|
|
# Create the sequence group.
|
|
seq_group = SequenceGroup(request_id, seqs, sampling_params,
|
|
arrival_time)
|
|
|
|
# Add the sequence group to the scheduler.
|
|
self.scheduler.add_seq_group(seq_group)
|
|
|
|
def get_num_unfinished_requests(self) -> int:
|
|
return self.scheduler.get_num_unfinished_seq_groups()
|
|
|
|
def has_unfinished_requests(self) -> bool:
|
|
return self.scheduler.has_unfinished_seqs()
|
|
|
|
def step(self) -> List[RequestOutput]:
|
|
seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
|
|
if (not seq_group_metadata_list) and scheduler_outputs.is_empty():
|
|
# Nothing to do.
|
|
return []
|
|
|
|
# Execute the model.
|
|
output = self._run_workers(
|
|
"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,
|
|
)
|
|
# Update the scheduler with the model outputs.
|
|
seq_groups = self.scheduler.update(output)
|
|
|
|
# Decode the sequences.
|
|
self._decode_sequences(seq_groups)
|
|
# Stop the sequences that meet the stopping criteria.
|
|
self._stop_sequences(seq_groups)
|
|
# Free the finished sequence groups.
|
|
self.scheduler.free_finished_seq_groups()
|
|
|
|
# Create the outputs.
|
|
request_outputs: List[RequestOutput] = []
|
|
for seq_group in seq_groups:
|
|
request_output = RequestOutput.from_seq_group(seq_group)
|
|
request_outputs.append(request_output)
|
|
return request_outputs
|
|
|
|
def _decode_sequences(self, seq_groups: List[SequenceGroup]) -> None:
|
|
# Decode the sequence outputs.
|
|
for seq_group in seq_groups:
|
|
for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
|
|
new_token, new_output_text = detokenize_incrementally(
|
|
self.tokenizer,
|
|
seq.output_tokens,
|
|
seq.get_last_token_id(),
|
|
skip_special_tokens=True,
|
|
)
|
|
seq.output_tokens.append(new_token)
|
|
seq.output_text = new_output_text
|
|
|
|
def _stop_sequences(self, seq_groups: List[SequenceGroup]) -> None:
|
|
# Stop the sequences.
|
|
for seq_group in seq_groups:
|
|
sampling_params = seq_group.sampling_params
|
|
for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
|
|
# Check if the sequence has generated a stop string.
|
|
stopped = False
|
|
for stop_str in sampling_params.stop:
|
|
if seq.output_text.endswith(stop_str):
|
|
# Truncate the output text so that the stop string is
|
|
# not included in the output.
|
|
seq.output_text = seq.output_text[:-len(stop_str)]
|
|
self.scheduler.free_seq(seq,
|
|
SequenceStatus.FINISHED_STOPPED)
|
|
stopped = True
|
|
break
|
|
if stopped:
|
|
continue
|
|
|
|
# Check if the sequence has reached max_tokens.
|
|
if seq.get_output_len() == sampling_params.max_tokens:
|
|
self.scheduler.free_seq(
|
|
seq, SequenceStatus.FINISHED_LENGTH_CAPPED)
|
|
continue
|
|
# Check if the sequence has generated the EOS token.
|
|
if not sampling_params.ignore_eos:
|
|
if seq.get_last_token_id() == self.tokenizer.eos_token_id:
|
|
self.scheduler.free_seq(seq,
|
|
SequenceStatus.FINISHED_STOPPED)
|
|
continue
|
|
|
|
def _run_workers(
|
|
self,
|
|
method: str,
|
|
get_all_outputs: bool = False,
|
|
*args,
|
|
**kwargs,
|
|
) -> Any:
|
|
all_outputs = []
|
|
for worker in self.workers:
|
|
executor = getattr(worker, method)
|
|
if self.parallel_config.use_ray:
|
|
executor = executor.remote
|
|
|
|
output = executor(*args, **kwargs)
|
|
all_outputs.append(output)
|
|
|
|
if self.parallel_config.use_ray:
|
|
all_outputs = ray.get(all_outputs)
|
|
|
|
if get_all_outputs:
|
|
return all_outputs
|
|
|
|
# Make sure all workers have the same results.
|
|
output = all_outputs[0]
|
|
for other_output in all_outputs[1:]:
|
|
assert output == other_output
|
|
return output
|