51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
# Copyright 2025 SGLang Team. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import functools
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from typing import Dict, Tuple
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import torch
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def get_cuda_stream() -> int:
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return torch.cuda.current_stream().cuda_stream
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_cache_buf: Dict[Tuple[str, torch.device], torch.Tensor] = {}
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def _get_cache_buf(name: str, bytes: int, device: torch.device) -> torch.Tensor:
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key = (name, device)
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buf = _cache_buf.get(key)
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if buf is None:
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buf = torch.empty(bytes, dtype=torch.uint8, device=device)
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_cache_buf[key] = buf
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return buf
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def _to_tensor_scalar_tuple(x):
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if isinstance(x, torch.Tensor):
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return (x, 0)
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else:
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return (None, x)
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@functools.lru_cache(maxsize=1)
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def is_arch_support_pdl() -> bool:
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# Hopper arch's compute capability == 9.0
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device = torch.cuda.current_device()
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major, minor = torch.cuda.get_device_capability(device)
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return major >= 9
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