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import json
import multiprocessing
from threading import Lock
from functools import partial
from typing import Iterator, List, Optional, Union, Dict
from typing_extensions import TypedDict, Literal
import llama_cpp
import anyio
from anyio.streams.memory import MemoryObjectSendStream
from starlette.concurrency import run_in_threadpool, iterate_in_threadpool
from fastapi import Depends, FastAPI, APIRouter, Request
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings
from sse_starlette.sse import EventSourceResponse
class Settings(BaseSettings):
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.",
)
n_ctx: int = Field(default=2048, ge=1, description="The context size.")
n_gpu_layers: int = Field(
default=0,
ge=0,
description="The number of layers to put on the GPU. The rest will be on the CPU.",
)
seed: int = Field(
default=1337, description="Random seed. -1 for random."
)
n_batch: int = Field(
default=512, ge=1, description="The batch size to use per eval."
)
n_threads: int = Field(
default=max(multiprocessing.cpu_count() // 2, 1),
ge=1,
description="The number of threads to use.",
)
f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
use_mlock: bool = Field(
default=llama_cpp.llama_mlock_supported(),
description="Use mlock.",
)
use_mmap: bool = Field(
default=llama_cpp.llama_mmap_supported(),
description="Use mmap.",
)
embedding: bool = Field(default=True, description="Whether to use embeddings.")
low_vram: bool = Field(
default=False,
description="Whether to use less VRAM. This will reduce performance.",
)
last_n_tokens_size: int = Field(
default=64,
ge=0,
description="Last n tokens to keep for repeat penalty calculation.",
)
logits_all: bool = Field(default=True, description="Whether to return logits.")
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.",
)
vocab_only: bool = Field(
default=False, description="Whether to only return the vocabulary."
)
verbose: bool = Field(
default=True, description="Whether to print debug information."
)
host: str = Field(
default="localhost", description="Listen address"
)
port: int = Field(
default=8000, description="Listen port"
)
interrupt_requests: bool = Field(
default=True,
description="Whether to interrupt requests when a new request is received.",
)
router = APIRouter()
settings: Optional[Settings] = None
llama: Optional[llama_cpp.Llama] = None
def create_app(settings: Optional[Settings] = None):
if settings is None:
settings = Settings()
app = FastAPI(
title="🦙 llama.cpp Python API",
version="0.0.1",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(router)
global llama
llama = llama_cpp.Llama(
model_path=settings.model,
n_gpu_layers=settings.n_gpu_layers,
seed=settings.seed,
f16_kv=settings.f16_kv,
use_mlock=settings.use_mlock,
use_mmap=settings.use_mmap,
embedding=settings.embedding,
logits_all=settings.logits_all,
n_threads=settings.n_threads,
n_batch=settings.n_batch,
n_ctx=settings.n_ctx,
last_n_tokens_size=settings.last_n_tokens_size,
vocab_only=settings.vocab_only,
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)
cache = llama_cpp.LlamaCache(capacity_bytes=settings.cache_size)
llama.set_cache(cache)
def set_settings(_settings: Settings):
global settings
settings = _settings
set_settings(settings)
return app
llama_outer_lock = Lock()
llama_inner_lock = Lock()
def get_llama():
# NOTE: This double lock allows the currently streaming llama model to
# check if any other requests are pending in the same thread and cancel
# the stream if so.
llama_outer_lock.acquire()
release_outer_lock = True
try:
llama_inner_lock.acquire()
try:
llama_outer_lock.release()
release_outer_lock = False
yield llama
finally:
llama_inner_lock.release()
finally:
if release_outer_lock:
llama_outer_lock.release()
def get_settings():
yield settings
model_field = Field(description="The model to use for generating completions.")
max_tokens_field = Field(
default=16, ge=1, le=2048, description="The maximum number of tokens to generate."
)
temperature_field = Field(
default=0.8,
ge=0.0,
le=2.0,
description="Adjust the randomness of the generated text.\n\n"
+ "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.",
)
top_p_field = Field(
default=0.95,
ge=0.0,
le=1.0,
description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
+ "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.",
)
stop_field = Field(
default=None,
description="A list of tokens at which to stop generation. If None, no stop tokens are used.",
)
stream_field = Field(
default=False,
description="Whether to stream the results as they are generated. Useful for chatbots.",
)
top_k_field = Field(
default=40,
ge=0,
description="Limit the next token selection to the K most probable tokens.\n\n"
+ "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.",
)
repeat_penalty_field = Field(
default=1.1,
ge=0.0,
description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n"
+ "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
)
presence_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
)
frequency_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
)
mirostat_mode_field = Field(
default=0,
ge=0,
le=2,
description="Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)"
)
mirostat_tau_field = Field(
default=5.0,
ge=0.0,
le=10.0,
description="Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text"
)
mirostat_eta_field = Field(
default=0.1,
ge=0.001,
le=1.0,
description="Mirostat learning rate"
)
class CreateCompletionRequest(BaseModel):
prompt: Union[str, List[str]] = Field(
default="", description="The prompt to generate completions for."
)
suffix: Optional[str] = Field(
default=None,
description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.",
)
max_tokens: int = max_tokens_field
temperature: float = temperature_field
top_p: float = top_p_field
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
echo: bool = Field(
default=False,
description="Whether to echo the prompt in the generated text. Useful for chatbots.",
)
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated.",
)
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
logprobs: Optional[int] = Field(None)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
best_of: Optional[int] = 1
user: Optional[str] = Field(None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
class Config:
schema_extra = {
"example": {
"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
"stop": ["\n", "###"],
}
}
def make_logit_bias_processor(
llama: llama_cpp.Llama,
logit_bias: Dict[str, float],
logit_bias_type: Optional[Literal["input_ids", "tokens"]],
):
if logit_bias_type is None:
logit_bias_type = "input_ids"
to_bias: Dict[int, float] = {}
if logit_bias_type == "input_ids":
for input_id, score in logit_bias.items():
input_id = int(input_id)
to_bias[input_id] = score
elif logit_bias_type == "tokens":
for token, score in logit_bias.items():
token = token.encode('utf-8')
for input_id in llama.tokenize(token, add_bos=False):
to_bias[input_id] = score
def logit_bias_processor(
input_ids: List[int],
scores: List[float],
) -> List[float]:
new_scores = [None] * len(scores)
for input_id, score in enumerate(scores):
new_scores[input_id] = score + to_bias.get(input_id, 0.0)
return new_scores
return logit_bias_processor
@router.post(
"/v1/completions",
)
async def create_completion(
request: Request,
body: CreateCompletionRequest,
llama: llama_cpp.Llama = Depends(get_llama),
):
if isinstance(body.prompt, list):
assert len(body.prompt) <= 1
body.prompt = body.prompt[0] if len(body.prompt) > 0 else ""
exclude = {
"n",
"best_of",
"logit_bias",
"logit_bias_type",
"user",
}
kwargs = body.dict(exclude=exclude)
if body.logit_bias is not None:
kwargs['logits_processor'] = llama_cpp.LogitsProcessorList([
make_logit_bias_processor(llama, body.logit_bias, body.logit_bias_type),
])
if body.stream:
send_chan, recv_chan = anyio.create_memory_object_stream(10)
async def event_publisher(inner_send_chan: MemoryObjectSendStream):
async with inner_send_chan:
try:
iterator: Iterator[llama_cpp.CompletionChunk] = await run_in_threadpool(llama, **kwargs) # type: ignore
async for chunk in iterate_in_threadpool(iterator):
await inner_send_chan.send(dict(data=json.dumps(chunk)))
if await request.is_disconnected():
raise anyio.get_cancelled_exc_class()()
if settings.interrupt_requests and llama_outer_lock.locked():
await inner_send_chan.send(dict(data="[DONE]"))
raise anyio.get_cancelled_exc_class()()
await inner_send_chan.send(dict(data="[DONE]"))
except anyio.get_cancelled_exc_class() as e:
print("disconnected")
with anyio.move_on_after(1, shield=True):
print(
f"Disconnected from client (via refresh/close) {request.client}"
)
raise e
return EventSourceResponse(
recv_chan, data_sender_callable=partial(event_publisher, send_chan)
)
else:
completion: llama_cpp.Completion = await run_in_threadpool(llama, **kwargs) # type: ignore
return completion
class CreateEmbeddingRequest(BaseModel):
model: Optional[str] = model_field
input: Union[str, List[str]] = Field(description="The input to embed.")
user: Optional[str]
class Config:
schema_extra = {
"example": {
"input": "The food was delicious and the waiter...",
}
}
@router.post(
"/v1/embeddings",
)
async def create_embedding(
request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
return await run_in_threadpool(
llama.create_embedding, **request.dict(exclude={"user"})
)
class ChatCompletionRequestMessage(BaseModel):
role: Literal["system", "user", "assistant"] = Field(
default="user", description="The role of the message."
)
content: str = Field(default="", description="The content of the message.")
class CreateChatCompletionRequest(BaseModel):
messages: List[ChatCompletionRequestMessage] = Field(
default=[], description="A list of messages to generate completions for."
)
max_tokens: int = max_tokens_field
temperature: float = temperature_field
top_p: float = top_p_field
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
stop: Optional[List[str]] = stop_field
stream: bool = stream_field
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
user: Optional[str] = Field(None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
class Config:
schema_extra = {
"example": {
"messages": [
ChatCompletionRequestMessage(
role="system", content="You are a helpful assistant."
),
ChatCompletionRequestMessage(
role="user", content="What is the capital of France?"
),
]
}
}
@router.post(
"/v1/chat/completions",
)
async def create_chat_completion(
request: Request,
body: CreateChatCompletionRequest,
llama: llama_cpp.Llama = Depends(get_llama),
settings: Settings = Depends(get_settings),
) -> Union[llama_cpp.ChatCompletion]: # type: ignore
exclude = {
"n",
"logit_bias",
"logit_bias_type",
"user",
}
kwargs = body.dict(exclude=exclude)
if body.logit_bias is not None:
kwargs['logits_processor'] = llama_cpp.LogitsProcessorList([
make_logit_bias_processor(llama, body.logit_bias, body.logit_bias_type),
])
if body.stream:
send_chan, recv_chan = anyio.create_memory_object_stream(10)
async def event_publisher(inner_send_chan: MemoryObjectSendStream):
async with inner_send_chan:
try:
iterator: Iterator[llama_cpp.ChatCompletionChunk] = await run_in_threadpool(llama.create_chat_completion, **kwargs) # type: ignore
async for chat_chunk in iterate_in_threadpool(iterator):
await inner_send_chan.send(dict(data=json.dumps(chat_chunk)))
if await request.is_disconnected():
raise anyio.get_cancelled_exc_class()()
if settings.interrupt_requests and llama_outer_lock.locked():
await inner_send_chan.send(dict(data="[DONE]"))
raise anyio.get_cancelled_exc_class()()
await inner_send_chan.send(dict(data="[DONE]"))
except anyio.get_cancelled_exc_class() as e:
print("disconnected")
with anyio.move_on_after(1, shield=True):
print(
f"Disconnected from client (via refresh/close) {request.client}"
)
raise e
return EventSourceResponse(
recv_chan,
data_sender_callable=partial(event_publisher, send_chan),
)
else:
completion: llama_cpp.ChatCompletion = await run_in_threadpool(
llama.create_chat_completion, **kwargs # type: ignore
)
return completion
class ModelData(TypedDict):
id: str
object: Literal["model"]
owned_by: str
permissions: List[str]
class ModelList(TypedDict):
object: Literal["list"]
data: List[ModelData]
@router.get("/v1/models")
async def get_models(
settings: Settings = Depends(get_settings),
) -> ModelList:
assert llama is not None
return {
"object": "list",
"data": [
{
"id": settings.model_alias
if settings.model_alias is not None
else llama.model_path,
"object": "model",
"owned_by": "me",
"permissions": [],
}
],
}