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[Core] Use platform-agnostic device control for DP engine core (#17245)

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Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
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Jade Zheng 2025-05-13 03:09:16 +08:00 committed by GitHub
parent b9fd0d7a69
commit 289199feb6
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GPG Key ID: B5690EEEBB952194
4 changed files with 30 additions and 39 deletions
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26
vllm/platforms/cuda.py
View File

@ -34,24 +34,6 @@ pynvml = import_pynvml()
torch.backends.cuda.enable_cudnn_sdp(False)
def device_id_to_physical_device_id(device_id: int) -> int:
if "CUDA_VISIBLE_DEVICES" in os.environ:
device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
if device_ids == [""]:
msg = (
"CUDA_VISIBLE_DEVICES is set to empty string, which means"
" GPU support is disabled. If you are using ray, please unset"
" the environment variable `CUDA_VISIBLE_DEVICES` inside the"
" worker/actor. "
"Check https://github.com/vllm-project/vllm/issues/8402 for"
" more information.")
raise RuntimeError(msg)
physical_device_id = device_ids[device_id]
return int(physical_device_id)
else:
return device_id
def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
@wraps(fn)
@ -338,7 +320,7 @@ class NvmlCudaPlatform(CudaPlatformBase):
device_id: int = 0
) -> Optional[DeviceCapability]:
try:
physical_device_id = device_id_to_physical_device_id(device_id)
physical_device_id = cls.device_id_to_physical_device_id(device_id)
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
return DeviceCapability(major=major, minor=minor)
@ -360,20 +342,20 @@ class NvmlCudaPlatform(CudaPlatformBase):
@classmethod
@with_nvml_context
def get_device_name(cls, device_id: int = 0) -> str:
physical_device_id = device_id_to_physical_device_id(device_id)
physical_device_id = cls.device_id_to_physical_device_id(device_id)
return cls._get_physical_device_name(physical_device_id)
@classmethod
@with_nvml_context
def get_device_uuid(cls, device_id: int = 0) -> str:
physical_device_id = device_id_to_physical_device_id(device_id)
physical_device_id = cls.device_id_to_physical_device_id(device_id)
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
return pynvml.nvmlDeviceGetUUID(handle)
@classmethod
@with_nvml_context
def get_device_total_memory(cls, device_id: int = 0) -> int:
physical_device_id = device_id_to_physical_device_id(device_id)
physical_device_id = cls.device_id_to_physical_device_id(device_id)
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)

19
vllm/platforms/interface.py
View File

@ -1,5 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
import enum
import os
import platform
import random
from platform import uname
@ -161,6 +162,24 @@ class Platform:
def is_sleep_mode_available(self) -> bool:
return self._enum == PlatformEnum.CUDA
@classmethod
def device_id_to_physical_device_id(cls, device_id: int):
if cls.device_control_env_var in os.environ:
device_ids = os.environ[cls.device_control_env_var].split(",")
if device_ids == [""]:
msg = (f"{cls.device_control_env_var} is set to empty string, "
"which means current platform support is disabled. If "
"you are using ray, please unset the environment "
f"variable `{cls.device_control_env_var}` inside the "
"worker/actor. Check "
"https://github.com/vllm-project/vllm/issues/8402 for "
"more information.")
raise RuntimeError(msg)
physical_device_id = device_ids[device_id]
return int(physical_device_id)
else:
return device_id
@classmethod
def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
dtype: torch.dtype, kv_cache_dtype: Optional[str],

11
vllm/platforms/rocm.py
View File

@ -95,15 +95,6 @@ def with_amdsmi_context(fn):
return wrapper
def device_id_to_physical_device_id(device_id: int) -> int:
if "CUDA_VISIBLE_DEVICES" in os.environ:
device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
physical_device_id = device_ids[device_id]
return int(physical_device_id)
else:
return device_id
@cache
def on_mi250_mi300() -> bool:
GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
@ -238,7 +229,7 @@ class RocmPlatform(Platform):
@with_amdsmi_context
@lru_cache(maxsize=8)
def get_device_name(cls, device_id: int = 0) -> str:
physical_device_id = device_id_to_physical_device_id(device_id)
physical_device_id = cls.device_id_to_physical_device_id(device_id)
handle = amdsmi_get_processor_handles()[physical_device_id]
asic_info = amdsmi_get_gpu_asic_info(handle)
device_name: str = asic_info["device_id"]

13
vllm/v1/engine/core.py
View File

@ -622,13 +622,12 @@ class DPEngineCoreProc(EngineCoreProc):
assert 0 <= local_dp_rank <= dp_rank < dp_size
from vllm.platforms import current_platform
if current_platform.is_cuda_alike():
from vllm.platforms.cuda import device_id_to_physical_device_id
tp_size = vllm_config.parallel_config.tensor_parallel_size
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
str(device_id_to_physical_device_id(i))
for i in range(local_dp_rank * tp_size, (local_dp_rank + 1) *
tp_size))
device_control_env_var = current_platform.device_control_env_var
tp_size = vllm_config.parallel_config.tensor_parallel_size
os.environ[device_control_env_var] = ",".join(
str(current_platform.device_id_to_physical_device_id(i))
for i in range(local_dp_rank * tp_size, (local_dp_rank + 1) *
tp_size))
self.local_dp_rank = local_dp_rank
self.dp_group = vllm_config.parallel_config.stateless_init_dp_group()