mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import TYPE_CHECKING, Optional
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import torch
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import vllm.envs as envs
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from vllm.logger import init_logger
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from .interface import Platform, PlatformEnum, _Backend
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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else:
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VllmConfig = None
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logger = init_logger(__name__)
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class TpuPlatform(Platform):
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_enum = PlatformEnum.TPU
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device_name: str = "tpu"
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device_type: str = "tpu"
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dispatch_key: str = "XLA"
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ray_device_key: str = "TPU"
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device_control_env_var: str = "TPU_VISIBLE_CHIPS"
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supported_quantization: list[str] = [
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"tpu_int8", "compressed-tensors", "compressed_tensors"
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]
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@classmethod
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def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
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dtype: torch.dtype, kv_cache_dtype: Optional[str],
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block_size: int, use_v1: bool,
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use_mla: bool) -> str:
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if (selected_backend != _Backend.PALLAS
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and selected_backend != _Backend.PALLAS_VLLM_V1):
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logger.info("Cannot use %s backend on TPU.", selected_backend)
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if use_v1:
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logger.info("Using Pallas V1 backend.")
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return "vllm.v1.attention.backends.pallas.PallasAttentionBackend"
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else:
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logger.info("Using Pallas backend.")
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return "vllm.attention.backends.pallas.PallasAttentionBackend"
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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return "tpu"
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@classmethod
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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raise NotImplementedError
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@classmethod
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def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
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return not envs.VLLM_USE_V1
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@classmethod
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def inference_mode(cls):
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return torch.no_grad()
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@classmethod
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def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
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from vllm.config import CompilationLevel
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cache_config = vllm_config.cache_config
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if cache_config and cache_config.block_size is None:
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cache_config.block_size = 16
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compilation_config = vllm_config.compilation_config
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# TPU only supports DYNAMO_ONCE compilation level
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if compilation_config.level != CompilationLevel.DYNAMO_ONCE:
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logger.info("[TPU] Forcing DYNAMO_ONCE compilation level")
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compilation_config.level = CompilationLevel.DYNAMO_ONCE
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if compilation_config.backend == "":
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compilation_config.backend = "openxla"
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assert vllm_config.speculative_config is None, \
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"TPU does not support speculative decoding"
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if vllm_config.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.", vllm_config.model_config.dtype)
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vllm_config.model_config.dtype = torch.bfloat16
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parallel_config = vllm_config.parallel_config
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scheduler_config = vllm_config.scheduler_config
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if parallel_config.worker_cls == "auto":
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if envs.VLLM_USE_V1:
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parallel_config.worker_cls = \
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"vllm.v1.worker.tpu_worker.TPUWorker"
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else:
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if scheduler_config.is_multi_step:
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parallel_config.worker_cls = \
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"vllm.worker.multi_step_tpu_worker.MultiStepTPUWorker"
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else:
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parallel_config.worker_cls = \
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"vllm.worker.tpu_worker.TPUWorker"
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# Adjust scheduler config for V1
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# TODO: Add support for these
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if envs.VLLM_USE_V1 and vllm_config.cache_config.enable_prefix_caching:
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logger.warning("[V1][TPU] Disable prefix caching")
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vllm_config.cache_config.enable_prefix_caching = False
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assert not vllm_config.speculative_config, (
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"Speculative decoding is not yet supported for TPU backend")
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@classmethod
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def is_pin_memory_available(cls):
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logger.warning("Pin memory is not supported on TPU.")
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return False
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@classmethod
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def get_device_communicator_cls(cls) -> str:
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return "vllm.distributed.device_communicators.tpu_communicator.TpuCommunicator" # noqa
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@classmethod
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def use_all_gather(cls) -> bool:
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return True
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