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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
"""CUDA device operations for the SRT platform layer."""
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from typing import Optional
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import torch
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from sglang.srt.platforms.device_mixin import (
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DeviceCapability,
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DeviceMixin,
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PlatformEnum,
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)
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from sglang.srt.platforms.interface import SRTPlatform
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class CudaDeviceMixin(DeviceMixin):
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"""CUDA implementation of the shared device operations."""
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_enum: PlatformEnum = PlatformEnum.CUDA
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device_name: str = "cuda"
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device_type: str = "cuda"
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def get_device_total_memory(self, device_id: int = 0) -> int:
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return int(torch.cuda.get_device_properties(device_id).total_memory)
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def get_current_memory_usage(
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self, device: Optional["torch.device"] = None
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) -> float:
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return float(torch.cuda.max_memory_allocated(device))
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def get_device(self, local_rank: int) -> "torch.device":
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return torch.device("cuda", local_rank)
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def set_device(self, device: "torch.device") -> None:
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torch.cuda.set_device(device)
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def get_device_name(self, device_id: int = 0) -> str:
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return str(torch.cuda.get_device_name(device_id))
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def get_device_uuid(self, device_id: int = 0) -> str:
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return str(torch.cuda.get_device_properties(device_id).uuid)
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def get_device_capability(self, device_id: int = 0) -> DeviceCapability:
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major, minor = torch.cuda.get_device_capability(device_id)
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return DeviceCapability(major, minor)
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def empty_cache(self) -> None:
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torch.cuda.empty_cache()
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def synchronize(self) -> None:
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torch.cuda.synchronize()
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def get_available_memory(self, device_id: int = 0) -> tuple[int, int]:
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return torch.cuda.mem_get_info(device_id)
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def get_torch_distributed_backend_str(self) -> str:
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return "nccl"
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@classmethod
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def seed_everything(cls, seed: int | None = None) -> None:
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if seed is not None:
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super().seed_everything(seed)
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torch.cuda.manual_seed_all(seed)
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class CudaSRTPlatform(CudaDeviceMixin, SRTPlatform):
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"""Default in-tree CUDA SRT platform."""
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def supports_fp8(self) -> bool:
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return True
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def support_cuda_graph(self) -> bool:
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return True
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def support_piecewise_cuda_graph(self) -> bool:
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return True
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