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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
if self._current_model:
self._current_model.close()
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:
self._current_model.close()
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"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.Llava15ChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "obsidian":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.ObsidianChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.ObsidianChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "llava-1-6":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.Llava16ChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.Llava16ChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "moondream":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.MoondreamChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.MoondreamChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "nanollava":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.NanoLlavaChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.NanoLlavaChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "llama-3-vision-alpha":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.Llama3VisionAlpha.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.Llama3VisionAlpha(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "minicpm-v-2.6":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.MiniCPMv26ChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.MiniCPMv26ChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
elif settings.chat_format == "qwen2.5-vl":
assert settings.clip_model_path is not None, "clip model not found"
if settings.hf_model_repo_id is not None:
chat_handler = (
llama_cpp.llama_chat_format.Qwen25VLChatHandler.from_pretrained(
repo_id=settings.hf_model_repo_id,
filename=settings.clip_model_path,
verbose=settings.verbose,
)
)
else:
chat_handler = llama_cpp.llama_chat_format.Qwen25VLChatHandler(
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, str]]] = 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)
elif value_type == "str":
kv_overrides[key] = 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,
split_mode=settings.split_mode,
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,
rpc_servers=settings.rpc_servers,
# Context Params
seed=settings.seed,
n_ctx=settings.n_ctx,
n_batch=settings.n_batch,
n_ubatch=settings.n_ubatch,
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,
flash_attn=settings.flash_attn,
# 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