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from __future__ import annotations
import multiprocessing
from typing import Optional, List, Literal, Union, Dict, cast
from typing_extensions import Self
from pydantic import Field, model_validator
from pydantic_settings import BaseSettings
import llama_cpp
# Disable warning for model and model_alias settings
BaseSettings.model_config["protected_namespaces"] = ()
class ModelSettings(BaseSettings):
"""Model settings used to load a Llama model."""
model: str = Field(
description="The path to the model to use for generating completions."
)
model_alias: Optional[str] = Field(
default=None,
description="The alias of the model to use for generating completions.",
)
# Model Params
n_gpu_layers: int = Field(
default=0,
ge=-1,
description="The number of layers to put on the GPU. The rest will be on the CPU. Set -1 to move all to GPU.",
)
split_mode: int = Field(
default=llama_cpp.LLAMA_SPLIT_MODE_LAYER,
description="The split mode to use.",
)
main_gpu: int = Field(
default=0,
ge=0,
description="Main GPU to use.",
)
tensor_split: Optional[List[float]] = Field(
default=None,
description="Split layers across multiple GPUs in proportion.",
)
vocab_only: bool = Field(
default=False, description="Whether to only return the vocabulary."
)
use_mmap: bool = Field(
default=llama_cpp.llama_supports_mmap(),
description="Use mmap.",
)
use_mlock: bool = Field(
default=llama_cpp.llama_supports_mlock(),
description="Use mlock.",
)
kv_overrides: Optional[List[str]] = Field(
default=None,
description="List of model kv overrides in the format key=type:value where type is one of (bool, int, float). Valid true values are (true, TRUE, 1), otherwise false.",
)
rpc_servers: Optional[str] = Field(
default=None,
description="comma seperated list of rpc servers for offloading",
)
# Context Params
seed: int = Field(
default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random."
)
n_ctx: int = Field(default=2048, ge=0, description="The context size.")
n_batch: int = Field(
default=512, ge=1, description="The batch size to use per eval."
)
n_ubatch: int = Field(
default=512, ge=1, description="The physical batch size used by llama.cpp"
)
n_threads: int = Field(
default=max(multiprocessing.cpu_count() // 2, 1),
ge=1,
description="The number of threads to use. Use -1 for max cpu threads",
)
n_threads_batch: int = Field(
default=max(multiprocessing.cpu_count(), 1),
ge=0,
description="The number of threads to use when batch processing. Use -1 for max cpu threads",
)
rope_scaling_type: int = Field(
default=llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED
)
rope_freq_base: float = Field(default=0.0, description="RoPE base frequency")
rope_freq_scale: float = Field(
default=0.0, description="RoPE frequency scaling factor"
)
yarn_ext_factor: float = Field(default=-1.0)
yarn_attn_factor: float = Field(default=1.0)
yarn_beta_fast: float = Field(default=32.0)
yarn_beta_slow: float = Field(default=1.0)
yarn_orig_ctx: int = Field(default=0)
mul_mat_q: bool = Field(
default=True, description="if true, use experimental mul_mat_q kernels"
)
logits_all: bool = Field(default=True, description="Whether to return logits.")
embedding: bool = Field(default=False, description="Whether to use embeddings.")
offload_kqv: bool = Field(
default=True, description="Whether to offload kqv to the GPU."
)
flash_attn: bool = Field(
default=False, description="Whether to use flash attention."
)
# Sampling Params
last_n_tokens_size: int = Field(
default=64,
ge=0,
description="Last n tokens to keep for repeat penalty calculation.",
)
# LoRA Params
lora_base: Optional[str] = Field(
default=None,
description="Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.",
)
lora_path: Optional[str] = Field(
default=None,
description="Path to a LoRA file to apply to the model.",
)
# Backend Params
numa: Union[bool, int] = Field(
default=False,
description="Enable NUMA support.",
)
# Chat Format Params
chat_format: Optional[str] = Field(
default=None,
description="Chat format to use.",
)
clip_model_path: Optional[str] = Field(
default=None,
description="Path to a CLIP model to use for multi-modal chat completion.",
)
# Cache Params
cache: bool = Field(
default=False,
description="Use a cache to reduce processing times for evaluated prompts.",
)
cache_type: Literal["ram", "disk"] = Field(
default="ram",
description="The type of cache to use. Only used if cache is True.",
)
cache_size: int = Field(
default=2 << 30,
description="The size of the cache in bytes. Only used if cache is True.",
)
# Tokenizer Options
hf_tokenizer_config_path: Optional[str] = Field(
default=None,
description="The path to a HuggingFace tokenizer_config.json file.",
)
hf_pretrained_model_name_or_path: Optional[str] = Field(
default=None,
description="The model name or path to a pretrained HuggingFace tokenizer model. Same as you would pass to AutoTokenizer.from_pretrained().",
)
# Loading from HuggingFace Model Hub
hf_model_repo_id: Optional[str] = Field(
default=None,
description="The model repo id to use for the HuggingFace tokenizer model.",
)
# Speculative Decoding
draft_model: Optional[str] = Field(
default=None,
description="Method to use for speculative decoding. One of (prompt-lookup-decoding).",
)
draft_model_num_pred_tokens: int = Field(
default=10,
description="Number of tokens to predict using the draft model.",
)
# KV Cache Quantization
type_k: Optional[int] = Field(
default=None,
description="Type of the key cache quantization.",
)
type_v: Optional[int] = Field(
default=None,
description="Type of the value cache quantization.",
)
# Misc
verbose: bool = Field(
default=True, description="Whether to print debug information."
)
@model_validator(
mode="before"
) # pre=True to ensure this runs before any other validation
def set_dynamic_defaults(self) -> Self:
# If n_threads or n_threads_batch is -1, set it to multiprocessing.cpu_count()
cpu_count = multiprocessing.cpu_count()
values = cast(Dict[str, int], self)
if values.get("n_threads", 0) == -1:
values["n_threads"] = cpu_count
if values.get("n_threads_batch", 0) == -1:
values["n_threads_batch"] = cpu_count
return self
class ServerSettings(BaseSettings):
"""Server settings used to configure the FastAPI and Uvicorn server."""
# Uvicorn Settings
host: str = Field(default="localhost", description="Listen address")
port: int = Field(default=8000, description="Listen port")
ssl_keyfile: Optional[str] = Field(
default=None, description="SSL key file for HTTPS"
)
ssl_certfile: Optional[str] = Field(
default=None, description="SSL certificate file for HTTPS"
)
# FastAPI Settings
api_key: Optional[str] = Field(
default=None,
description="API key for authentication. If set all requests need to be authenticated.",
)
interrupt_requests: bool = Field(
default=True,
description="Whether to interrupt requests when a new request is received.",
)
disable_ping_events: bool = Field(
default=False,
description="Disable EventSource pings (may be needed for some clients).",
)
root_path: str = Field(
default="",
description="The root path for the server. Useful when running behind a reverse proxy.",
)
class Settings(ServerSettings, ModelSettings):
pass
class ConfigFileSettings(ServerSettings):
"""Configuration file format settings."""
models: List[ModelSettings] = Field(default=[], description="Model configs")
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