355 lines
14 KiB
Python
355 lines
14 KiB
Python
|
"""A GPU worker class."""
|
||
|
|
import gc
|
||
|
|
import os
|
||
|
|
from typing import Dict, List, Tuple, Set, Optional
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.distributed
|
||
|
|
|
||
|
|
from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,
|
||
|
|
ParallelConfig, SchedulerConfig, LoRAConfig)
|
||
|
|
from vllm.model_executor import set_random_seed
|
||
|
|
from vllm.model_executor.parallel_utils import cupy_utils
|
||
|
|
from vllm.model_executor.parallel_utils.communication_op import (
|
||
|
|
broadcast_tensor_dict)
|
||
|
|
from vllm.model_executor.parallel_utils.custom_all_reduce import init_custom_ar
|
||
|
|
from vllm.model_executor.parallel_utils.parallel_state import (
|
||
|
|
ensure_model_parallel_initialized)
|
||
|
|
from vllm.sequence import SamplerOutput, SequenceGroupMetadata
|
||
|
|
from vllm.worker.cache_engine import CacheEngine
|
||
|
|
from vllm.worker.model_runner import ModelRunner
|
||
|
|
from vllm.lora.request import LoRARequest
|
||
|
|
from vllm.utils import is_hip
|
||
|
|
|
||
|
|
|
||
|
|
class Worker:
|
||
|
|
"""A worker class that executes (a partition of) the model on a GPU.
|
||
|
|
|
||
|
|
Each worker is associated with a single GPU. The worker is responsible for
|
||
|
|
maintaining the KV cache and executing the model on the GPU. In case of
|
||
|
|
distributed inference, each worker is assigned a partition of the model.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
model_config: ModelConfig,
|
||
|
|
parallel_config: ParallelConfig,
|
||
|
|
scheduler_config: SchedulerConfig,
|
||
|
|
device_config: DeviceConfig,
|
||
|
|
local_rank: int,
|
||
|
|
rank: int,
|
||
|
|
distributed_init_method: str,
|
||
|
|
lora_config: Optional[LoRAConfig] = None,
|
||
|
|
kv_cache_dtype: Optional[str] = "auto",
|
||
|
|
is_driver_worker: bool = False,
|
||
|
|
) -> None:
|
||
|
|
self.model_config = model_config
|
||
|
|
self.parallel_config = parallel_config
|
||
|
|
self.scheduler_config = scheduler_config
|
||
|
|
self.device_config = device_config
|
||
|
|
self.local_rank = local_rank
|
||
|
|
self.rank = rank
|
||
|
|
self.distributed_init_method = distributed_init_method
|
||
|
|
self.lora_config = lora_config
|
||
|
|
self.is_driver_worker = is_driver_worker
|
||
|
|
if self.is_driver_worker:
|
||
|
|
assert self.rank == 0, "The driver worker must have rank 0."
|
||
|
|
|
||
|
|
self.model_runner = ModelRunner(model_config,
|
||
|
|
parallel_config,
|
||
|
|
scheduler_config,
|
||
|
|
device_config,
|
||
|
|
lora_config=self.lora_config,
|
||
|
|
kv_cache_dtype=kv_cache_dtype,
|
||
|
|
is_driver_worker=False)
|
||
|
|
# TODO align
|
||
|
|
"""
|
||
|
|
self.model_runner = ModelRunner(model_config,
|
||
|
|
parallel_config,
|
||
|
|
scheduler_config,
|
||
|
|
device_config,
|
||
|
|
lora_config=self.lora_config,
|
||
|
|
kv_cache_dtype=kv_cache_dtype,
|
||
|
|
is_driver_worker=is_driver_worker)
|
||
|
|
"""
|
||
|
|
# Uninitialized cache engine. Will be initialized by
|
||
|
|
# self.init_cache_engine().
|
||
|
|
self.cache_config = None
|
||
|
|
self.cache_engine = None
|
||
|
|
self.cache_events = None
|
||
|
|
self.gpu_cache = None
|
||
|
|
|
||
|
|
def init_model(self, cupy_port: Optional[int] = None) -> None:
|
||
|
|
if self.device_config.device.type == "cuda":
|
||
|
|
# torch.distributed.all_reduce does not free the input tensor until
|
||
|
|
# the synchronization point. This causes the memory usage to grow
|
||
|
|
# as the number of all_reduce calls increases. This env var disables
|
||
|
|
# this behavior.
|
||
|
|
# Related issue:
|
||
|
|
# https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
|
||
|
|
os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
|
||
|
|
|
||
|
|
# This env var set by Ray causes exceptions with graph building.
|
||
|
|
os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
|
||
|
|
self.device = torch.device(f"cuda:{self.local_rank}")
|
||
|
|
torch.cuda.set_device(self.device)
|
||
|
|
|
||
|
|
_check_if_gpu_supports_dtype(self.model_config.dtype)
|
||
|
|
torch.cuda.empty_cache()
|
||
|
|
self.init_gpu_memory = torch.cuda.mem_get_info()[0]
|
||
|
|
else:
|
||
|
|
raise RuntimeError(
|
||
|
|
f"Not support device type: {self.device_config.device}")
|
||
|
|
# Initialize the distributed environment.
|
||
|
|
init_distributed_environment(self.parallel_config, self.rank,
|
||
|
|
cupy_port, self.distributed_init_method)
|
||
|
|
# Initialize the model.
|
||
|
|
set_random_seed(self.model_config.seed)
|
||
|
|
|
||
|
|
def load_model(self):
|
||
|
|
self.model_runner.load_model()
|
||
|
|
|
||
|
|
@torch.inference_mode()
|
||
|
|
def profile_num_available_blocks(
|
||
|
|
self,
|
||
|
|
block_size: int,
|
||
|
|
gpu_memory_utilization: float,
|
||
|
|
cpu_swap_space: int,
|
||
|
|
cache_dtype: str,
|
||
|
|
) -> Tuple[int, int]:
|
||
|
|
"""Profiles the peak memory usage of the model and returns the maximum
|
||
|
|
number of GPU and CPU cache blocks that can be allocated.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
block_size: The size of the cache block.
|
||
|
|
gpu_memory_utilization: The fraction of the total GPU memory to use.
|
||
|
|
cpu_swap_space: The size of the CPU swap space in bytes.
|
||
|
|
"""
|
||
|
|
# Profile the memory usage of the model and get the maximum number of
|
||
|
|
# cache blocks that can be allocated with the remaining free memory.
|
||
|
|
torch.cuda.empty_cache()
|
||
|
|
|
||
|
|
# Execute a forward pass with dummy inputs to profile the memory usage
|
||
|
|
# of the model.
|
||
|
|
self.model_runner.profile_run()
|
||
|
|
|
||
|
|
# Calculate the number of blocks that can be allocated with the
|
||
|
|
# profiled peak memory.
|
||
|
|
torch.cuda.synchronize()
|
||
|
|
free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
|
||
|
|
# NOTE(woosuk): Here we assume that the other processes using the same
|
||
|
|
# GPU did not change their memory usage during the profiling.
|
||
|
|
peak_memory = self.init_gpu_memory - free_gpu_memory
|
||
|
|
|
||
|
|
cache_block_size = CacheEngine.get_cache_block_size(
|
||
|
|
block_size, cache_dtype, self.model_config, self.parallel_config)
|
||
|
|
num_gpu_blocks = int(
|
||
|
|
(total_gpu_memory * gpu_memory_utilization - peak_memory) //
|
||
|
|
cache_block_size)
|
||
|
|
num_cpu_blocks = int(cpu_swap_space // cache_block_size)
|
||
|
|
num_gpu_blocks = max(num_gpu_blocks, 0)
|
||
|
|
num_cpu_blocks = max(num_cpu_blocks, 0)
|
||
|
|
if self.model_runner.lora_manager:
|
||
|
|
self.model_runner.remove_all_loras()
|
||
|
|
gc.collect()
|
||
|
|
torch.cuda.empty_cache()
|
||
|
|
return num_gpu_blocks, num_cpu_blocks
|
||
|
|
|
||
|
|
def init_cache_engine(self, cache_config: CacheConfig) -> None:
|
||
|
|
self.cache_config = cache_config
|
||
|
|
self.cache_engine = CacheEngine(self.cache_config, self.model_config,
|
||
|
|
self.parallel_config)
|
||
|
|
self.cache_events = self.cache_engine.events
|
||
|
|
self.gpu_cache = self.cache_engine.gpu_cache
|
||
|
|
self.model_runner.set_block_size(self.cache_engine.block_size)
|
||
|
|
|
||
|
|
def warm_up_model(self) -> None:
|
||
|
|
if not self.model_config.enforce_eager:
|
||
|
|
self.model_runner.capture_model(self.gpu_cache)
|
||
|
|
# Reset the seed to ensure that the random state is not affected by
|
||
|
|
# the model initialization and profiling.
|
||
|
|
set_random_seed(self.model_config.seed)
|
||
|
|
|
||
|
|
def cache_swap(
|
||
|
|
self,
|
||
|
|
blocks_to_swap_in: Dict[int, int],
|
||
|
|
blocks_to_swap_out: Dict[int, int],
|
||
|
|
blocks_to_copy: Dict[int, List[int]],
|
||
|
|
) -> None:
|
||
|
|
# Issue cache operations.
|
||
|
|
issued_cache_op = False
|
||
|
|
if blocks_to_swap_in:
|
||
|
|
self.cache_engine.swap_in(blocks_to_swap_in)
|
||
|
|
issued_cache_op = True
|
||
|
|
if blocks_to_swap_out:
|
||
|
|
self.cache_engine.swap_out(blocks_to_swap_out)
|
||
|
|
issued_cache_op = True
|
||
|
|
if blocks_to_copy:
|
||
|
|
self.cache_engine.copy(blocks_to_copy)
|
||
|
|
issued_cache_op = True
|
||
|
|
|
||
|
|
cache_events = self.cache_events if issued_cache_op else None
|
||
|
|
|
||
|
|
# Wait for cache operations to finish.
|
||
|
|
# TODO(woosuk): Profile swapping overhead and optimize if needed.
|
||
|
|
if cache_events is not None:
|
||
|
|
for event in cache_events:
|
||
|
|
event.wait()
|
||
|
|
|
||
|
|
@torch.inference_mode()
|
||
|
|
def execute_model(
|
||
|
|
self,
|
||
|
|
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None,
|
||
|
|
blocks_to_swap_in: Optional[Dict[int, int]] = None,
|
||
|
|
blocks_to_swap_out: Optional[Dict[int, int]] = None,
|
||
|
|
blocks_to_copy: Optional[Dict[int, List[int]]] = None,
|
||
|
|
) -> Optional[SamplerOutput]:
|
||
|
|
# Issue cache operations.
|
||
|
|
issued_cache_op = False
|
||
|
|
if blocks_to_swap_in:
|
||
|
|
self.cache_engine.swap_in(blocks_to_swap_in)
|
||
|
|
issued_cache_op = True
|
||
|
|
if blocks_to_swap_out:
|
||
|
|
self.cache_engine.swap_out(blocks_to_swap_out)
|
||
|
|
issued_cache_op = True
|
||
|
|
if blocks_to_copy:
|
||
|
|
self.cache_engine.copy(blocks_to_copy)
|
||
|
|
issued_cache_op = True
|
||
|
|
|
||
|
|
cache_events = self.cache_events if issued_cache_op else None
|
||
|
|
|
||
|
|
# Wait for cache operations to finish.
|
||
|
|
# TODO(woosuk): Profile swapping overhead and optimize if needed.
|
||
|
|
if cache_events is not None:
|
||
|
|
for event in cache_events:
|
||
|
|
event.wait()
|
||
|
|
# If there is no input, we don't need to execute the model.
|
||
|
|
if not seq_group_metadata_list:
|
||
|
|
return {}
|
||
|
|
|
||
|
|
output = self.model_runner.execute_model(seq_group_metadata_list,
|
||
|
|
self.gpu_cache)
|
||
|
|
return output
|
||
|
|
|
||
|
|
# TODO align
|
||
|
|
"""
|
||
|
|
@torch.inference_mode()
|
||
|
|
def execute_model(
|
||
|
|
self,
|
||
|
|
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None,
|
||
|
|
blocks_to_swap_in: Optional[Dict[int, int]] = None,
|
||
|
|
blocks_to_swap_out: Optional[Dict[int, int]] = None,
|
||
|
|
blocks_to_copy: Optional[Dict[int, List[int]]] = None,
|
||
|
|
) -> Optional[SamplerOutput]:
|
||
|
|
if self.is_driver_worker:
|
||
|
|
assert seq_group_metadata_list is not None
|
||
|
|
num_seq_groups = len(seq_group_metadata_list)
|
||
|
|
assert blocks_to_swap_in is not None
|
||
|
|
assert blocks_to_swap_out is not None
|
||
|
|
assert blocks_to_copy is not None
|
||
|
|
data = {
|
||
|
|
"num_seq_groups": num_seq_groups,
|
||
|
|
"blocks_to_swap_in": blocks_to_swap_in,
|
||
|
|
"blocks_to_swap_out": blocks_to_swap_out,
|
||
|
|
"blocks_to_copy": blocks_to_copy,
|
||
|
|
}
|
||
|
|
broadcast_tensor_dict(data, src=0)
|
||
|
|
else:
|
||
|
|
data = broadcast_tensor_dict(src=0)
|
||
|
|
num_seq_groups = data["num_seq_groups"]
|
||
|
|
blocks_to_swap_in = data["blocks_to_swap_in"]
|
||
|
|
blocks_to_swap_out = data["blocks_to_swap_out"]
|
||
|
|
blocks_to_copy = data["blocks_to_copy"]
|
||
|
|
|
||
|
|
self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy)
|
||
|
|
|
||
|
|
# If there is no input, we don't need to execute the model.
|
||
|
|
if num_seq_groups == 0:
|
||
|
|
return {}
|
||
|
|
|
||
|
|
output = self.model_runner.execute_model(seq_group_metadata_list,
|
||
|
|
self.gpu_cache)
|
||
|
|
return output
|
||
|
|
"""
|
||
|
|
|
||
|
|
def add_lora(self, lora_request: LoRARequest) -> bool:
|
||
|
|
return self.model_runner.add_lora(lora_request)
|
||
|
|
|
||
|
|
def remove_lora(self, lora_id: int) -> bool:
|
||
|
|
return self.model_runner.remove_lora(lora_id)
|
||
|
|
|
||
|
|
def list_loras(self) -> Set[int]:
|
||
|
|
return self.model_runner.list_loras()
|
||
|
|
|
||
|
|
|
||
|
|
def init_distributed_environment(
|
||
|
|
parallel_config: ParallelConfig,
|
||
|
|
rank: int,
|
||
|
|
cupy_port: Optional[int],
|
||
|
|
distributed_init_method: Optional[str] = None,
|
||
|
|
) -> None:
|
||
|
|
"""Initialize the distributed environment."""
|
||
|
|
if torch.distributed.is_initialized():
|
||
|
|
torch_world_size = torch.distributed.get_world_size()
|
||
|
|
if torch_world_size != parallel_config.world_size:
|
||
|
|
raise RuntimeError(
|
||
|
|
"torch.distributed is already initialized but the torch world "
|
||
|
|
"size does not match parallel_config.world_size "
|
||
|
|
f"({torch_world_size} vs. {parallel_config.world_size}).")
|
||
|
|
elif not distributed_init_method:
|
||
|
|
raise ValueError(
|
||
|
|
"distributed_init_method must be set if torch.distributed "
|
||
|
|
"is not already initialized")
|
||
|
|
else:
|
||
|
|
torch.distributed.init_process_group(
|
||
|
|
backend="nccl",
|
||
|
|
world_size=parallel_config.world_size,
|
||
|
|
rank=rank,
|
||
|
|
init_method=distributed_init_method,
|
||
|
|
)
|
||
|
|
|
||
|
|
if cupy_utils.is_initialized():
|
||
|
|
cupy_world_size = cupy_utils.get_world_size()
|
||
|
|
if cupy_world_size != parallel_config.world_size:
|
||
|
|
raise RuntimeError(
|
||
|
|
"cupy.distributed is already initialized but the cupy world "
|
||
|
|
"size does not match parallel_config.world_size "
|
||
|
|
f"({cupy_world_size} vs. {parallel_config.world_size}).")
|
||
|
|
elif (parallel_config.world_size > 1 and cupy_port is not None
|
||
|
|
and not is_hip()):
|
||
|
|
# NOTE(woosuk): We don't initialize CuPy process group when world size
|
||
|
|
# is 1.
|
||
|
|
# TODO(woosuk): Support multi-node connection.
|
||
|
|
cupy_utils.init_process_group(
|
||
|
|
world_size=parallel_config.world_size,
|
||
|
|
rank=rank,
|
||
|
|
host="localhost",
|
||
|
|
port=cupy_port,
|
||
|
|
)
|
||
|
|
|
||
|
|
# A small all_reduce for warmup.
|
||
|
|
torch.distributed.all_reduce(torch.zeros(1).cuda())
|
||
|
|
if cupy_utils.is_initialized():
|
||
|
|
cupy_utils.all_reduce(torch.zeros(1).cuda())
|
||
|
|
ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
|
||
|
|
parallel_config.pipeline_parallel_size)
|
||
|
|
|
||
|
|
# Initialize a custom fast all-reduce implementation.
|
||
|
|
if not parallel_config.disable_custom_all_reduce:
|
||
|
|
init_custom_ar()
|
||
|
|
|
||
|
|
|
||
|
|
def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
|
||
|
|
# Check if the GPU supports the dtype.
|
||
|
|
if torch_dtype == torch.bfloat16:
|
||
|
|
return # avoid capability error
|
||
|
|
compute_capability = torch.cuda.get_device_capability()
|
||
|
|
if compute_capability[0] < 8:
|
||
|
|
gpu_name = torch.cuda.get_device_name()
|
||
|
|
raise ValueError(
|
||
|
|
"Bfloat16 is only supported on GPUs with compute capability "
|
||
|
|
f"of at least 8.0. Your {gpu_name} GPU has compute capability "
|
||
|
|
f"{compute_capability[0]}.{compute_capability[1]}. "
|
||
|
|
"You can use float16 instead by explicitly setting the"
|
||
|
|
"`dtype` flag in CLI, for example: --dtype=half.")
|