aphrodite-engine/aphrodite/task_handler/worker.py

"""A GPU worker class."""
import gc
import os
from typing import Dict, List, Tuple, Set, Optional

import torch
import torch.distributed

from aphrodite.common.config import (CacheConfig, ModelConfig, ParallelConfig,
                                     SchedulerConfig, LoRAConfig, DeviceConfig)
from aphrodite.common.utils import in_wsl
from aphrodite.modeling import set_random_seed
from aphrodite.modeling.megatron import cupy_utils
from aphrodite.modeling.megatron.communication_op import (broadcast_tensor_dict
                                                          )
from aphrodite.modeling.megatron.custom_all_reduce import init_custom_ar
from aphrodite.modeling.megatron.parallel_state import (
    ensure_model_parallel_initialized)
from aphrodite.common.sequence import SamplerOutput, SequenceGroupMetadata
from aphrodite.task_handler.cache_engine import CacheEngine
from aphrodite.task_handler.model_runner import ModelRunner
from aphrodite.lora.request import LoRARequest
from aphrodite.common.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",
        kv_quant_params_path: Optional[str] = None,
        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,
            kv_quant_params_path=kv_quant_params_path,
            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)

            # Patch for torch.cuda.is_available() unexpected error in WSL;
            # always call torch.cuda.device_count() before initialising device
            if in_wsl():
                torch.cuda.device_count()
            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)
        if not self.parallel_config.disable_custom_all_reduce:
            init_custom_ar()
        # 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: 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: Profile swapping overhead and optimize if needed.
        if cache_events is not None:
            for event in cache_events:  # pylint: disable=not-an-iterable
                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]:
        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: We don't initialize CuPy process group when world size
        # is 1.
        # TODO: 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)


def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
    # Check if the GPU supports the dtype.
    if torch_dtype == torch.bfloat16:
        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.")