mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. 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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Environment variables used by FastDeploy.
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"""
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import os
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from typing import Any, Callable
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environment_variables: dict[str, Callable[[], Any]] = {
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# Whether to use BF16 on CPU.
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"FD_CPU_USE_BF16":
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lambda: os.getenv("FD_CPU_USE_BF16", "False"),
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# Cuda architecture to build FastDeploy.This is a list of strings
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# such as [80,90].
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"FD_BUILDING_ARCS":
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lambda: os.getenv("FD_BUILDING_ARCS", "[]"),
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# Log directory.
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"FD_LOG_DIR":
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lambda: os.getenv("FD_LOG_DIR", "log"),
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# Whether to use debug mode, can set 0 or 1
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"FD_DEBUG":
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lambda: os.getenv("FD_DEBUG", "0"),
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# Number of days to keep fastdeploy logs.
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"FD_LOG_BACKUP_COUNT":
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lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
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# Model download cache directory.
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"FD_MODEL_CACHE":
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lambda: os.getenv("FD_MODEL_CACHE", None),
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# Maximum number of stop sequences.
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"FD_MAX_STOP_SEQS_NUM":
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lambda: os.getenv("FD_MAX_STOP_SEQS_NUM", "5"),
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# Maximum length of stop sequences.
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"FD_STOP_SEQS_MAX_LEN":
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lambda: os.getenv("FD_STOP_SEQS_MAX_LEN", "8"),
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# GPU devices that will be used. This is a string that
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# splited by comma, such as 0,1,2.
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"CUDA_VISIBLE_DEVICES":
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lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),
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# Whether to use HuggingFace tokenizer.
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"FD_USE_HF_TOKENIZER":
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lambda: os.getenv("FD_USE_HF_TOKENIZER", 0),
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# Set the high watermark (HWM) for receiving data during ZMQ initialization
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"FD_ZMQ_SNDHWM":
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lambda: os.getenv("FD_ZMQ_SNDHWM", 10000),
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# cache kv quant params directory
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"FD_CACHE_PARAMS":
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lambda: os.getenv("FD_CACHE_PARAMS", "none"),
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# Set attention backend. "NATIVE_ATTN", "APPEND_ATTN"
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# and "MLA_ATTN" can be set currently.
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"FD_ATTENTION_BACKEND":
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lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
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# Set sampling class. "base", "air" and "rejection" can be set currently.
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"FD_SAMPLING_CLASS":
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lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
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# Set moe backend."cutlass","marlin" and "triton" can be set currently.
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"FD_MOE_BACKEND":
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lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),
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# Set triton kernel JIT compilation directory.
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"FD_TRITON_KERNEL_CACHE_DIR":
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lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),
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# Whether transition from standalone PD decoupling to centralized inference
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"FD_PD_CHANGEABLE":
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lambda: os.getenv("FD_PD_CHANGEABLE", "1"),
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}
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def __getattr__(name: str):
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# lazy evaluation of environment variables
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if name in environment_variables:
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return environment_variables[name]()
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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def __dir__():
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return list(environment_variables.keys())
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