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* Initial plan * 更新环境变量文档以匹配最新代码 Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
232 lines
10 KiB
Markdown
232 lines
10 KiB
Markdown
[简体中文](../zh/usage/environment_variables.md)
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# FastDeploy Environment Variables
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FastDeploy's environment variables are defined in `fastdeploy/envs.py` at the root of the repository. Below is the documentation:
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```python
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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": lambda: os.getenv("FD_CPU_USE_BF16", "False"),
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# CUDA architecture versions used when building FastDeploy (string list, e.g. [80,90])
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"FD_BUILDING_ARCS": lambda: os.getenv("FD_BUILDING_ARCS", "[]"),
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# Log directory
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"FD_LOG_DIR": lambda: os.getenv("FD_LOG_DIR", "log"),
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# Enable debug mode (0 or 1)
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"FD_DEBUG": lambda: int(os.getenv("FD_DEBUG", "0")),
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# FastDeploy log retention days
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"FD_LOG_BACKUP_COUNT": lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
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# Model download source, can be "AISTUDIO", "MODELSCOPE" or "HUGGINGFACE"
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"FD_MODEL_SOURCE": lambda: os.getenv("FD_MODEL_SOURCE", "AISTUDIO"),
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# Model download cache directory
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"FD_MODEL_CACHE": 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": lambda: int(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": lambda: int(os.getenv("FD_STOP_SEQS_MAX_LEN", "8")),
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# GPU devices to use (comma-separated string, e.g. 0,1,2)
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"CUDA_VISIBLE_DEVICES": lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),
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# Whether to use HuggingFace tokenizer (0 or 1)
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"FD_USE_HF_TOKENIZER": lambda: bool(int(os.getenv("FD_USE_HF_TOKENIZER", "0"))),
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# ZMQ send high-water mark (HWM) during initialization
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"FD_ZMQ_SNDHWM": lambda: os.getenv("FD_ZMQ_SNDHWM", 0),
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# Directory for caching KV quantization parameters
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"FD_CACHE_PARAMS": lambda: os.getenv("FD_CACHE_PARAMS", "none"),
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# Attention backend ("NATIVE_ATTN", "APPEND_ATTN", or "MLA_ATTN")
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"FD_ATTENTION_BACKEND": lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
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# Sampling class ("base", "base_non_truncated", "air", or "rejection")
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"FD_SAMPLING_CLASS": lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
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# MoE backend ("cutlass", "marlin", or "triton")
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"FD_MOE_BACKEND": lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),
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# Whether to use Machete for wint4 dense GEMM
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"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
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# Whether to disable recompute the request when the KV cache is full
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"FD_DISABLED_RECOVER": lambda: os.getenv("FD_DISABLED_RECOVER", "0"),
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# Triton kernel JIT compilation directory
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"FD_TRITON_KERNEL_CACHE_DIR": lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),
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# Switch from standalone PD to centralized inference (0 or 1)
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"FD_PD_CHANGEABLE": lambda: os.getenv("FD_PD_CHANGEABLE", "0"),
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# Whether to use DeepGemm for FP8 blockwise MoE
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"FD_USE_DEEP_GEMM": lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "0"))),
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# Whether to use aggregate send
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"FD_USE_AGGREGATE_SEND": lambda: bool(int(os.getenv("FD_USE_AGGREGATE_SEND", "0"))),
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# Whether to open Trace
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"TRACES_ENABLE": lambda: os.getenv("TRACES_ENABLE", "false"),
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# Set trace server name
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"FD_SERVICE_NAME": lambda: os.getenv("FD_SERVICE_NAME", "FastDeploy"),
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# Set trace host name
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"FD_HOST_NAME": lambda: os.getenv("FD_HOST_NAME", "localhost"),
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# Set trace exporter
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"TRACES_EXPORTER": lambda: os.getenv("TRACES_EXPORTER", "console"),
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# Set trace exporter_otlp_endpoint
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"EXPORTER_OTLP_ENDPOINT": lambda: os.getenv("EXPORTER_OTLP_ENDPOINT"),
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# Set trace exporter_otlp_headers
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"EXPORTER_OTLP_HEADERS": lambda: os.getenv("EXPORTER_OTLP_HEADERS"),
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# Enable kv cache block scheduler v1 (no need for kv_cache_ratio)
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"ENABLE_V1_KVCACHE_SCHEDULER": lambda: int(os.getenv("ENABLE_V1_KVCACHE_SCHEDULER", "1")),
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# Set prealloc block num for decoder
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"FD_ENC_DEC_BLOCK_NUM": lambda: int(os.getenv("FD_ENC_DEC_BLOCK_NUM", "2")),
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# Enable max prefill of one execute step
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"FD_ENABLE_MAX_PREFILL": lambda: int(os.getenv("FD_ENABLE_MAX_PREFILL", "0")),
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# Whether to use PLUGINS
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"FD_PLUGINS": lambda: None if "FD_PLUGINS" not in os.environ else os.environ["FD_PLUGINS"].split(","),
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# Set trace attribute job_id
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"FD_JOB_ID": lambda: os.getenv("FD_JOB_ID"),
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# Support max connections
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"FD_SUPPORT_MAX_CONNECTIONS": lambda: int(os.getenv("FD_SUPPORT_MAX_CONNECTIONS", "1024")),
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# Offset for Tensor Parallelism group GID
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"FD_TP_GROUP_GID_OFFSET": lambda: int(os.getenv("FD_TP_GROUP_GID_OFFSET", "1000")),
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# Enable multi api server
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"FD_ENABLE_MULTI_API_SERVER": lambda: bool(int(os.getenv("FD_ENABLE_MULTI_API_SERVER", "0"))),
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# Whether to use Torch model format
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"FD_FOR_TORCH_MODEL_FORMAT": lambda: bool(int(os.getenv("FD_FOR_TORCH_MODEL_FORMAT", "0"))),
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# Force disable default chunked prefill
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"FD_DISABLE_CHUNKED_PREFILL": lambda: bool(int(os.getenv("FD_DISABLE_CHUNKED_PREFILL", "0"))),
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# Whether to use new get_output and save_output method (0 or 1)
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"FD_USE_GET_SAVE_OUTPUT_V1": lambda: bool(int(os.getenv("FD_USE_GET_SAVE_OUTPUT_V1", "0"))),
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# Whether to enable model cache feature
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"FD_ENABLE_MODEL_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_CACHE", "0"))),
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# Enable internal module to access LLMEngine
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"FD_ENABLE_INTERNAL_ADAPTER": lambda: int(os.getenv("FD_ENABLE_INTERNAL_ADAPTER", "0")),
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# LLMEngine receive requests port, used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ZMQ_RECV_REQUEST_SERVER_PORT": lambda: os.getenv("FD_ZMQ_RECV_REQUEST_SERVER_PORT", "8200"),
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# LLMEngine send response port, used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ZMQ_SEND_RESPONSE_SERVER_PORT": lambda: os.getenv("FD_ZMQ_SEND_RESPONSE_SERVER_PORT", "8201"),
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# LLMEngine receive requests port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ZMQ_RECV_REQUEST_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_RECV_REQUEST_SERVER_PORTS", "8200"),
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# LLMEngine send response port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ZMQ_SEND_RESPONSE_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_SEND_RESPONSE_SERVER_PORTS", "8201"),
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# LLMEngine receive control command port, used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ZMQ_CONTROL_CMD_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_CONTROL_CMD_SERVER_PORTS", "8202"),
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# Whether to enable the decode caches requests for preallocating resource
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"FD_ENABLE_CACHE_TASK": lambda: os.getenv("FD_ENABLE_CACHE_TASK", "0"),
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# Batched token timeout in EP
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"FD_EP_BATCHED_TOKEN_TIMEOUT": lambda: float(os.getenv("FD_EP_BATCHED_TOKEN_TIMEOUT", "0.1")),
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# Max pre-fetch requests number in PD
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"FD_EP_MAX_PREFETCH_TASK_NUM": lambda: int(os.getenv("FD_EP_MAX_PREFETCH_TASK_NUM", "8")),
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# Enable or disable model caching. When enabled, the quantized model is stored as a cache for future inference to improve loading efficiency
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"FD_ENABLE_MODEL_LOAD_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_LOAD_CACHE", "0"))),
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# Whether to clear cpu cache when clearing model weights
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"FD_ENABLE_SWAP_SPACE_CLEARING": lambda: int(os.getenv("FD_ENABLE_SWAP_SPACE_CLEARING", "0")),
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# Enable return text, used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ENABLE_RETURN_TEXT": lambda: bool(int(os.getenv("FD_ENABLE_RETURN_TEXT", "0"))),
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
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"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
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# Timeout for cache_transfer_manager process exit
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"FD_CACHE_PROC_EXIT_TIMEOUT": lambda: int(os.getenv("FD_CACHE_PROC_EXIT_TIMEOUT", "600")),
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# Count for cache_transfer_manager process error
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"FD_CACHE_PROC_ERROR_COUNT": lambda: int(os.getenv("FD_CACHE_PROC_ERROR_COUNT", "10")),
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# API_KEY required for service authentication
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"FD_API_KEY": lambda: [] if "FD_API_KEY" not in os.environ else os.environ["FD_API_KEY"].split(","),
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# The AK of bos storing the features while multi_modal infer
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"ENCODE_FEATURE_BOS_AK": lambda: os.getenv("ENCODE_FEATURE_BOS_AK"),
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# The SK of bos storing the features while multi_modal infer
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"ENCODE_FEATURE_BOS_SK": lambda: os.getenv("ENCODE_FEATURE_BOS_SK"),
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# The ENDPOINT of bos storing the features while multi_modal infer
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"ENCODE_FEATURE_ENDPOINT": lambda: os.getenv("ENCODE_FEATURE_ENDPOINT"),
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# Enable offline perf test mode for PD disaggregation
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"FD_OFFLINE_PERF_TEST_FOR_PD": lambda: int(os.getenv("FD_OFFLINE_PERF_TEST_FOR_PD", "0")),
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# Enable E2W tensor convert
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"FD_ENABLE_E2W_TENSOR_CONVERT": lambda: int(os.getenv("FD_ENABLE_E2W_TENSOR_CONVERT", "0")),
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# Engine task queue with shared memory
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"FD_ENGINE_TASK_QUEUE_WITH_SHM": lambda: int(os.getenv("FD_ENGINE_TASK_QUEUE_WITH_SHM", "0")),
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# Fill bitmask batch size
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"FD_FILL_BITMASK_BATCH": lambda: int(os.getenv("FD_FILL_BITMASK_BATCH", "4")),
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# Enable PDL
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"FD_ENABLE_PDL": lambda: int(os.getenv("FD_ENABLE_PDL", "1")),
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# Disable guidance additional feature
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"FD_GUIDANCE_DISABLE_ADDITIONAL": lambda: bool(int(os.getenv("FD_GUIDANCE_DISABLE_ADDITIONAL", "1"))),
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# LLGuidance log level
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"FD_LLGUIDANCE_LOG_LEVEL": lambda: int(os.getenv("FD_LLGUIDANCE_LOG_LEVEL", "0")),
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# Number of tokens in the group for Mixture of Experts (MoE) computation processing on HPU
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"FD_HPU_CHUNK_SIZE": lambda: int(os.getenv("FD_HPU_CHUNK_SIZE", "64")),
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# Enable FP8 calibration on HPU
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"FD_HPU_MEASUREMENT_MODE": lambda: os.getenv("FD_HPU_MEASUREMENT_MODE", "0"),
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# Prefill wait decode resource seconds
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"FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS": lambda: int(os.getenv("FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS", "30")),
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# FMQ config JSON
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"FMQ_CONFIG_JSON": lambda: os.getenv("FMQ_CONFIG_JSON", None),
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# OTLP Exporter schedule delay millis
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"FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS": lambda: int(os.getenv("FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS", "500")),
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# OTLP Exporter max export batch size
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"FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE": lambda: int(os.getenv("FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE", "64")),
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# Token processor health timeout
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"FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT": lambda: int(os.getenv("FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT", "120")),
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# XPU MoE FFN quant type map
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"FD_XPU_MOE_FFN_QUANT_TYPE_MAP": lambda: os.getenv("FD_XPU_MOE_FFN_QUANT_TYPE_MAP", ""),
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}
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```
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