d9e98f42e4
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com> Co-authored-by: Roger Wang <ywang@roblox.com>
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
from typing import Any, Dict, List, Optional, Set, Tuple
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import torch
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from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.sequence import ExecuteModelRequest, SamplerOutput
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from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
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make_async)
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logger = init_logger(__name__)
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class TPUExecutor(ExecutorBase):
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def _init_executor(self) -> None:
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assert not self.scheduler_config.chunked_prefill_enabled, (
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"Chunked prefill is not yet supported for TPU backend")
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assert not self.speculative_config, (
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"Speculative decoding is not yet supported for TPU backend")
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if self.model_config.dtype in (torch.float16, torch.float32):
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logger.warning(
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"The TPU backend currently does not support %s. "
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"Using bfloat16 instead.", self.model_config.dtype)
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self.model_config.dtype = torch.bfloat16
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# Instantiate the worker and load the model to the device.
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self.driver_worker = self._create_worker()
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self.driver_worker.init_device()
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self.driver_worker.load_model()
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def _get_worker_kwargs(
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self,
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local_rank: int = 0,
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rank: int = 0,
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distributed_init_method: Optional[str] = None,
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) -> Dict[str, Any]:
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"""Return worker init args for a given rank."""
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if distributed_init_method is None:
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distributed_init_method = get_distributed_init_method(
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get_ip(), get_open_port())
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return dict(
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model_config=self.model_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config,
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device_config=self.device_config,
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cache_config=self.cache_config,
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load_config=self.load_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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multimodal_config=self.multimodal_config,
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is_driver_worker=rank == 0,
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)
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def _create_worker(
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self,
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local_rank: int = 0,
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rank: int = 0,
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distributed_init_method: Optional[str] = None,
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):
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from vllm.worker.tpu_worker import TPUWorker
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worker = TPUWorker(**self._get_worker_kwargs(local_rank, rank,
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distributed_init_method))
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return worker
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def initialize_cache(
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self,
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num_gpu_blocks: int,
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num_cpu_blocks: int,
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) -> None:
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"""Initialize the KV cache by invoking the underlying worker."""
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# NOTE: This is logged in the executor because there can be >1 worker
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# with other executors. We could log in the engine level, but work
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# remains to abstract away the device for non-GPU configurations.
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logger.info("# TPU blocks: %d, # CPU blocks: %d", num_gpu_blocks,
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num_cpu_blocks)
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self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
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def determine_num_available_blocks(self) -> Tuple[int, int]:
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"""Determine the number of available KV blocks by invoking the
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underlying worker.
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"""
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return self.driver_worker.determine_num_available_blocks()
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def execute_model(
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self,
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execute_model_req: ExecuteModelRequest,
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) -> List[SamplerOutput]:
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output = self.driver_worker.execute_model(execute_model_req)
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return output
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def add_lora(self, lora_request: LoRARequest) -> bool:
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raise NotImplementedError("LoRA is not implemented for TPU backend.")
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def remove_lora(self, lora_id: int) -> bool:
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raise NotImplementedError("LoRA is not implemented for TPU backend.")
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def pin_lora(self, lora_id: int) -> bool:
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raise NotImplementedError("LoRA is not implemented for TPU backend.")
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def list_loras(self) -> Set[int]:
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raise NotImplementedError("LoRA is not implemented for TPU backend.")
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def check_health(self) -> None:
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# TPUExecutor will always be healthy as long as it's running.
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return
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class TPUExecutorAsync(TPUExecutor, ExecutorAsyncBase):
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async def execute_model_async(
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self,
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sexecute_model_req: ExecuteModelRequest,
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) -> SamplerOutput:
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output = await make_async(self.driver_worker.execute_model
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)(sexecute_model_req)
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return output
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