vllm/vllm/worker/neuron_worker.py

"""A Neuron worker class."""
from typing import Dict, List, Optional, Tuple

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.communication_op import (
    broadcast_tensor_dict)
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


class Worker:
    """A worker class that executes the model on a group of neuron cores.
    """

    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,
                                        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) -> None:
        # Initialize the distributed environment.
        _init_distributed_environment(self.parallel_config,
                                      self.rank,
                                      self.distributed_init_method,
                                      distributed_backend="gloo")

        # 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 = 128,
        gpu_memory_utilization: float = 0.9,
        cpu_swap_space: int = 0,
        cache_dtype: str = "float16",
    ) -> Tuple[int, int]:
        """Simply returns max_num_seqs as num_gpu_blocks, 0 as num_cpu_blocks."""
        num_gpu_blocks = self.scheduler_config.max_num_seqs
        num_cpu_blocks = 0
        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.model_runner.set_block_size(self.cache_engine.block_size)

    def warm_up_model(self) -> None:
        # Warm up is maintained in transformers-neuronx
        pass

    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.
        if cache_events is not None:
            raise NotImplementedError(
                "cache operations are not implemented for neuron backend.")

    @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 _init_distributed_environment(
    parallel_config: ParallelConfig,
    rank: int,
    distributed_init_method: Optional[str] = None,
    distributed_backend: 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:
        distributed_backend = distributed_backend if distributed_backend else "nccl"
        torch.distributed.init_process_group(
            backend=distributed_backend,
            world_size=parallel_config.world_size,
            rank=rank,
            init_method=distributed_init_method,
        )

    # A small all_reduce for warmup.
    torch.distributed.all_reduce(torch.zeros(1))
    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
                                      parallel_config.pipeline_parallel_size)