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