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polish code with new pre-commit rule (#2923)

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Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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107
fastdeploy/envs.py
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@@ -20,115 +20,62 @@ from typing import Any, Callable
environment_variables: dict[str, Callable[[], Any]] = {
# Whether to use BF16 on CPU.
"FD_CPU_USE_BF16":
lambda: os.getenv("FD_CPU_USE_BF16", "False"),
"FD_CPU_USE_BF16": lambda: os.getenv("FD_CPU_USE_BF16", "False"),
# Cuda architecture to build FastDeploy.This is a list of strings
# such as [80,90].
"FD_BUILDING_ARCS":
lambda: os.getenv("FD_BUILDING_ARCS", "[]"),
"FD_BUILDING_ARCS": lambda: os.getenv("FD_BUILDING_ARCS", "[]"),
# Log directory.
"FD_LOG_DIR":
lambda: os.getenv("FD_LOG_DIR", "log"),
"FD_LOG_DIR": lambda: os.getenv("FD_LOG_DIR", "log"),
# Whether to use debug mode, can set 0 or 1
"FD_DEBUG":
lambda: os.getenv("FD_DEBUG", "0"),
"FD_DEBUG": lambda: os.getenv("FD_DEBUG", "0"),
# Number of days to keep fastdeploy logs.
"FD_LOG_BACKUP_COUNT":
lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
"FD_LOG_BACKUP_COUNT": lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
# Model download cache directory.
"FD_MODEL_CACHE":
lambda: os.getenv("FD_MODEL_CACHE", None),
"FD_MODEL_CACHE": lambda: os.getenv("FD_MODEL_CACHE", None),
# Maximum number of stop sequences.
"FD_MAX_STOP_SEQS_NUM":
lambda: os.getenv("FD_MAX_STOP_SEQS_NUM", "5"),
"FD_MAX_STOP_SEQS_NUM": lambda: os.getenv("FD_MAX_STOP_SEQS_NUM", "5"),
# Maximum length of stop sequences.
"FD_STOP_SEQS_MAX_LEN":
lambda: os.getenv("FD_STOP_SEQS_MAX_LEN", "8"),
"FD_STOP_SEQS_MAX_LEN": lambda: os.getenv("FD_STOP_SEQS_MAX_LEN", "8"),
# GPU devices that will be used. This is a string that
# splited by comma, such as 0,1,2.
"CUDA_VISIBLE_DEVICES":
lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),
"CUDA_VISIBLE_DEVICES": lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),
# Whether to use HuggingFace tokenizer.
"FD_USE_HF_TOKENIZER":
lambda: os.getenv("FD_USE_HF_TOKENIZER", 0),
"FD_USE_HF_TOKENIZER": lambda: os.getenv("FD_USE_HF_TOKENIZER", 0),
# Set the high watermark (HWM) for receiving data during ZMQ initialization
"FD_ZMQ_SNDHWM":
lambda: os.getenv("FD_ZMQ_SNDHWM", 10000),
"FD_ZMQ_SNDHWM": lambda: os.getenv("FD_ZMQ_SNDHWM", 10000),
# cache kv quant params directory
"FD_CACHE_PARAMS":
lambda: os.getenv("FD_CACHE_PARAMS", "none"),
"FD_CACHE_PARAMS": lambda: os.getenv("FD_CACHE_PARAMS", "none"),
# Set attention backend. "NATIVE_ATTN", "APPEND_ATTN"
# and "MLA_ATTN" can be set currently.
"FD_ATTENTION_BACKEND":
lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
"FD_ATTENTION_BACKEND": lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
# Set sampling class. "base", "base_non_truncated", "air" and "rejection" can be set currently.
"FD_SAMPLING_CLASS":
lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
"FD_SAMPLING_CLASS": lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
# Set moe backend."cutlass","marlin" and "triton" can be set currently.
"FD_MOE_BACKEND":
lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),
"FD_MOE_BACKEND": lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),
# Set whether to disable recompute the request when the KV cache is full.
"FD_DISABLED_RECOVER":
lambda: os.getenv("FD_DISABLED_RECOVER", "0"),
"FD_DISABLED_RECOVER": lambda: os.getenv("FD_DISABLED_RECOVER", "0"),
# Set triton kernel JIT compilation directory.
"FD_TRITON_KERNEL_CACHE_DIR":
lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),
"FD_TRITON_KERNEL_CACHE_DIR": lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),
# Whether transition from standalone PD decoupling to centralized inference
"FD_PD_CHANGEABLE":
lambda: os.getenv("FD_PD_CHANGEABLE", "0"),
"FD_PD_CHANGEABLE": lambda: os.getenv("FD_PD_CHANGEABLE", "0"),
# Whether to use fastsafetensor load weight (0 or 1)
"FD_USE_FASTSAFETENSOR":
lambda: os.getenv("FD_USE_FASTSAFETENSOR", "0"),
"FD_USE_FASTSAFETENSOR": lambda: os.getenv("FD_USE_FASTSAFETENSOR", "0"),
# Whether to use DeepGemm for FP8 blockwise MoE.
"FD_USE_DEEP_GEMM":
lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "1"))),
"FD_USE_DEEP_GEMM": lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "1"))),
# Whether to use aggregate send.
"FD_USE_AGGREGATE_SEND":
lambda: bool(int(os.getenv("FD_USE_AGGREGATE_SEND", "0"))),
"FD_USE_AGGREGATE_SEND": lambda: bool(int(os.getenv("FD_USE_AGGREGATE_SEND", "0"))),
# Whether to open Trace.
"TRACES_ENABLE":
lambda: os.getenv("TRACES_ENABLE", "false"),
"TRACES_ENABLE": lambda: os.getenv("TRACES_ENABLE", "false"),
# set traec Server name.
"FD_SERVICE_NAME":
lambda: os.getenv("FD_SERVICE_NAME", "FastDeploy"),
"FD_SERVICE_NAME": lambda: os.getenv("FD_SERVICE_NAME", "FastDeploy"),
# set traec host name.
"FD_HOST_NAME":
lambda: os.getenv("FD_HOST_NAME", "localhost"),
"FD_HOST_NAME": lambda: os.getenv("FD_HOST_NAME", "localhost"),
# set traec exporter.
"TRACES_EXPORTER":
lambda: os.getenv("TRACES_EXPORTER", "console"),
"TRACES_EXPORTER": lambda: os.getenv("TRACES_EXPORTER", "console"),
# set traec exporter_otlp_endpoint.
"EXPORTER_OTLP_ENDPOINT":
lambda: os.getenv("EXPORTER_OTLP_ENDPOINT"),
"EXPORTER_OTLP_ENDPOINT": lambda: os.getenv("EXPORTER_OTLP_ENDPOINT"),
# set traec exporter_otlp_headers.
"EXPORTER_OTLP_HEADERS":
lambda: os.getenv("EXPORTER_OTLP_HEADERS"),
"EXPORTER_OTLP_HEADERS": lambda: os.getenv("EXPORTER_OTLP_HEADERS"),
}