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from __future__ import annotations

import json

from typing import Dict, Optional, Union, List

import llama_cpp

import llama_cpp.llama_speculative as llama_speculative

import llama_cpp.llama_tokenizer as llama_tokenizer

from llama_cpp.server.settings import ModelSettings

class LlamaProxy:

def __init__(self, models: List[ModelSettings]) -> None:

assert len(models) > 0, "No models provided!"

self._model_settings_dict: dict[str, ModelSettings] = {}

for model in models:

if not model.model_alias:

model.model_alias = model.model

self._model_settings_dict[model.model_alias] = model

self._current_model: Optional[llama_cpp.Llama] = None

self._current_model_alias: Optional[str] = None

self._default_model_settings: ModelSettings = models[0]

self._default_model_alias: str = self._default_model_settings.model_alias # type: ignore

# Load default model

self._current_model = self.load_llama_from_model_settings(

self._default_model_settings

)

self._current_model_alias = self._default_model_alias

def __call__(self, model: Optional[str] = None) -> llama_cpp.Llama:

if model is None:

model = self._default_model_alias

if model not in self._model_settings_dict:

model = self._default_model_alias

if model == self._current_model_alias:

if self._current_model is not None:

return self._current_model

self._current_model = None

settings = self._model_settings_dict[model]

self._current_model = self.load_llama_from_model_settings(settings)

self._current_model_alias = model

return self._current_model

def __getitem__(self, model: str):

return self._model_settings_dict[model].model_dump()

def __setitem__(self, model: str, settings: Union[ModelSettings, str, bytes]):

if isinstance(settings, (bytes, str)):

settings = ModelSettings.model_validate_json(settings)

self._model_settings_dict[model] = settings

def __iter__(self):

for model in self._model_settings_dict:

yield model

def free(self):

if self._current_model:

del self._current_model

@staticmethod

def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama:

chat_handler = None

if settings.chat_format == "llava-1-5":

assert settings.clip_model_path is not None, "clip model not found"

chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(

clip_model_path=settings.clip_model_path, verbose=settings.verbose

)

elif settings.chat_format == "hf-autotokenizer":

assert (

settings.hf_pretrained_model_name_or_path is not None

), "hf_pretrained_model_name_or_path must be set for hf-autotokenizer"

chat_handler = (

llama_cpp.llama_chat_format.hf_autotokenizer_to_chat_completion_handler(

settings.hf_pretrained_model_name_or_path

)

)

elif settings.chat_format == "hf-tokenizer-config":

assert (

settings.hf_tokenizer_config_path is not None

), "hf_tokenizer_config_path must be set for hf-tokenizer-config"

chat_handler = llama_cpp.llama_chat_format.hf_tokenizer_config_to_chat_completion_handler(

json.load(open(settings.hf_tokenizer_config_path))

)

tokenizer: Optional[llama_cpp.BaseLlamaTokenizer] = None

if settings.hf_pretrained_model_name_or_path is not None:

tokenizer = llama_tokenizer.LlamaHFTokenizer.from_pretrained(

settings.hf_pretrained_model_name_or_path

)

draft_model = None

if settings.draft_model is not None:

draft_model = llama_speculative.LlamaPromptLookupDecoding(

num_pred_tokens=settings.draft_model_num_pred_tokens

)

kv_overrides: Optional[Dict[str, Union[bool, int, float]]] = None

if settings.kv_overrides is not None:

assert isinstance(settings.kv_overrides, list)

kv_overrides = {}

for kv in settings.kv_overrides:

key, value = kv.split("=")

if ":" in value:

value_type, value = value.split(":")

if value_type == "bool":

kv_overrides[key] = value.lower() in ["true", "1"]

elif value_type == "int":

kv_overrides[key] = int(value)

elif value_type == "float":

kv_overrides[key] = float(value)

else:

raise ValueError(f"Unknown value type {value_type}")

import functools

kwargs = {}

if settings.hf_model_repo_id is not None:

create_fn = functools.partial(

llama_cpp.Llama.from_pretrained,

repo_id=settings.hf_model_repo_id,

filename=settings.model,

)

else:

create_fn = llama_cpp.Llama

kwargs["model_path"] = settings.model

_model = create_fn(

**kwargs,

# Model Params

n_gpu_layers=settings.n_gpu_layers,

main_gpu=settings.main_gpu,

tensor_split=settings.tensor_split,

vocab_only=settings.vocab_only,

use_mmap=settings.use_mmap,

use_mlock=settings.use_mlock,

kv_overrides=kv_overrides,

# Context Params

seed=settings.seed,

n_ctx=settings.n_ctx,

n_batch=settings.n_batch,

n_threads=settings.n_threads,

n_threads_batch=settings.n_threads_batch,

rope_scaling_type=settings.rope_scaling_type,

rope_freq_base=settings.rope_freq_base,

rope_freq_scale=settings.rope_freq_scale,

yarn_ext_factor=settings.yarn_ext_factor,

yarn_attn_factor=settings.yarn_attn_factor,

yarn_beta_fast=settings.yarn_beta_fast,

yarn_beta_slow=settings.yarn_beta_slow,

yarn_orig_ctx=settings.yarn_orig_ctx,

mul_mat_q=settings.mul_mat_q,

logits_all=settings.logits_all,

embedding=settings.embedding,

offload_kqv=settings.offload_kqv,

# Sampling Params

last_n_tokens_size=settings.last_n_tokens_size,

# LoRA Params

lora_base=settings.lora_base,

lora_path=settings.lora_path,

# Backend Params

numa=settings.numa,

# Chat Format Params

chat_format=settings.chat_format,

chat_handler=chat_handler,

# Speculative Decoding

draft_model=draft_model,

# KV Cache Quantization

type_k=settings.type_k,

type_v=settings.type_v,

# Tokenizer

tokenizer=tokenizer,

# Misc

verbose=settings.verbose,

)

if settings.cache:

if settings.cache_type == "disk":

if settings.verbose:

print(f"Using disk cache with size {settings.cache_size}")

cache = llama_cpp.LlamaDiskCache(capacity_bytes=settings.cache_size)

else:

if settings.verbose:

print(f"Using ram cache with size {settings.cache_size}")

cache = llama_cpp.LlamaRAMCache(capacity_bytes=settings.cache_size)

_model.set_cache(cache)

return _model