900 lines
31 KiB
Python
900 lines
31 KiB
Python
import sys
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import json
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import traceback
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import multiprocessing
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import time
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from re import compile, Match, Pattern
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from threading import Lock
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from functools import partial
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from typing import Callable, Coroutine, Iterator, List, Optional, Tuple, Union, Dict
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from typing_extensions import TypedDict, Literal
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import llama_cpp
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import anyio
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from anyio.streams.memory import MemoryObjectSendStream
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from starlette.concurrency import run_in_threadpool, iterate_in_threadpool
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from fastapi import Depends, FastAPI, APIRouter, Request, Response
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from fastapi.middleware import Middleware
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from fastapi.routing import APIRoute
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from pydantic import BaseModel, Field
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from pydantic_settings import BaseSettings
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from sse_starlette.sse import EventSourceResponse
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from starlette_context import plugins
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from starlette_context.middleware import RawContextMiddleware
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import numpy as np
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import numpy.typing as npt
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# Disable warning for model and model_alias settings
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BaseSettings.model_config["protected_namespaces"] = ()
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class Settings(BaseSettings):
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model: str = Field(
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description="The path to the model to use for generating completions."
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)
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model_alias: Optional[str] = Field(
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default=None,
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description="The alias of the model to use for generating completions.",
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)
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# Model Params
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n_gpu_layers: int = Field(
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default=0,
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ge=-1,
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description="The number of layers to put on the GPU. The rest will be on the CPU. Set -1 to move all to GPU.",
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)
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main_gpu: int = Field(
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default=0,
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ge=0,
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description="Main GPU to use.",
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)
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tensor_split: Optional[List[float]] = Field(
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default=None,
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description="Split layers across multiple GPUs in proportion.",
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)
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vocab_only: bool = Field(
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default=False, description="Whether to only return the vocabulary."
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)
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use_mmap: bool = Field(
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default=llama_cpp.llama_mmap_supported(),
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description="Use mmap.",
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)
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use_mlock: bool = Field(
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default=llama_cpp.llama_mlock_supported(),
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description="Use mlock.",
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)
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# Context Params
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seed: int = Field(
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default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random."
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)
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n_ctx: int = Field(default=2048, ge=1, description="The context size.")
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n_batch: int = Field(
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default=512, ge=1, description="The batch size to use per eval."
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)
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n_threads: int = Field(
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default=max(multiprocessing.cpu_count() // 2, 1),
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ge=1,
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description="The number of threads to use.",
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)
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n_threads_batch: int = Field(
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default=max(multiprocessing.cpu_count() // 2, 1),
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ge=0,
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description="The number of threads to use when batch processing.",
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)
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rope_scaling_type: int = Field(default=llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED)
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rope_freq_base: float = Field(default=0.0, description="RoPE base frequency")
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rope_freq_scale: float = Field(
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default=0.0, description="RoPE frequency scaling factor"
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)
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yarn_ext_factor: float = Field(default=-1.0)
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yarn_attn_factor: float = Field(default=1.0)
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yarn_beta_fast: float = Field(default=32.0)
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yarn_beta_slow: float = Field(default=1.0)
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yarn_orig_ctx: int = Field(default=0)
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mul_mat_q: bool = Field(
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default=True, description="if true, use experimental mul_mat_q kernels"
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)
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f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
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logits_all: bool = Field(default=True, description="Whether to return logits.")
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embedding: bool = Field(default=True, description="Whether to use embeddings.")
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# Sampling Params
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last_n_tokens_size: int = Field(
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default=64,
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ge=0,
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description="Last n tokens to keep for repeat penalty calculation.",
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)
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# LoRA Params
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lora_base: Optional[str] = Field(
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default=None,
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description="Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.",
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)
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lora_path: Optional[str] = Field(
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default=None,
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description="Path to a LoRA file to apply to the model.",
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)
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# Backend Params
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numa: bool = Field(
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default=False,
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description="Enable NUMA support.",
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)
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# Chat Format Params
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chat_format: str = Field(
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default="llama-2",
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description="Chat format to use.",
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)
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clip_model_path: Optional[str] = Field(
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default=None,
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description="Path to a CLIP model to use for multi-modal chat completion.",
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)
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# Cache Params
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cache: bool = Field(
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default=False,
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description="Use a cache to reduce processing times for evaluated prompts.",
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)
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cache_type: Literal["ram", "disk"] = Field(
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default="ram",
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description="The type of cache to use. Only used if cache is True.",
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)
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cache_size: int = Field(
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default=2 << 30,
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description="The size of the cache in bytes. Only used if cache is True.",
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)
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# Misc
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verbose: bool = Field(
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default=True, description="Whether to print debug information."
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)
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# Server Params
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host: str = Field(default="localhost", description="Listen address")
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port: int = Field(default=8000, description="Listen port")
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interrupt_requests: bool = Field(
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default=True,
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description="Whether to interrupt requests when a new request is received.",
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)
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class ErrorResponse(TypedDict):
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"""OpenAI style error response"""
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message: str
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type: str
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param: Optional[str]
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code: Optional[str]
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class ErrorResponseFormatters:
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"""Collection of formatters for error responses.
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Args:
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request (Union[CreateCompletionRequest, CreateChatCompletionRequest]):
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Request body
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match (Match[str]): Match object from regex pattern
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Returns:
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Tuple[int, ErrorResponse]: Status code and error response
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"""
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@staticmethod
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def context_length_exceeded(
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request: Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
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match, # type: Match[str] # type: ignore
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) -> Tuple[int, ErrorResponse]:
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"""Formatter for context length exceeded error"""
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context_window = int(match.group(2))
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prompt_tokens = int(match.group(1))
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completion_tokens = request.max_tokens
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if hasattr(request, "messages"):
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# Chat completion
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message = (
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"This model's maximum context length is {} tokens. "
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"However, you requested {} tokens "
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"({} in the messages, {} in the completion). "
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"Please reduce the length of the messages or completion."
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)
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else:
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# Text completion
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message = (
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"This model's maximum context length is {} tokens, "
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"however you requested {} tokens "
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"({} in your prompt; {} for the completion). "
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"Please reduce your prompt; or completion length."
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)
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return 400, ErrorResponse(
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message=message.format(
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context_window,
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completion_tokens + prompt_tokens,
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prompt_tokens,
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completion_tokens,
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),
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type="invalid_request_error",
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param="messages",
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code="context_length_exceeded",
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)
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@staticmethod
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def model_not_found(
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request: Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
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match, # type: Match[str] # type: ignore
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) -> Tuple[int, ErrorResponse]:
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"""Formatter for model_not_found error"""
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model_path = str(match.group(1))
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message = f"The model `{model_path}` does not exist"
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return 400, ErrorResponse(
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message=message,
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type="invalid_request_error",
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param=None,
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code="model_not_found",
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)
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class RouteErrorHandler(APIRoute):
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"""Custom APIRoute that handles application errors and exceptions"""
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# key: regex pattern for original error message from llama_cpp
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# value: formatter function
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pattern_and_formatters: Dict[
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"Pattern",
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Callable[
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[
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Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
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"Match[str]",
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],
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Tuple[int, ErrorResponse],
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],
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] = {
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compile(
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r"Requested tokens \((\d+)\) exceed context window of (\d+)"
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): ErrorResponseFormatters.context_length_exceeded,
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compile(
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r"Model path does not exist: (.+)"
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): ErrorResponseFormatters.model_not_found,
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}
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def error_message_wrapper(
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self,
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error: Exception,
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body: Optional[
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Union[
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"CreateChatCompletionRequest",
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"CreateCompletionRequest",
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"CreateEmbeddingRequest",
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]
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] = None,
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) -> Tuple[int, ErrorResponse]:
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"""Wraps error message in OpenAI style error response"""
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print(f"Exception: {str(error)}", file=sys.stderr)
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traceback.print_exc(file=sys.stderr)
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if body is not None and isinstance(
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body,
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(
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CreateCompletionRequest,
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CreateChatCompletionRequest,
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),
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):
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# When text completion or chat completion
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for pattern, callback in self.pattern_and_formatters.items():
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match = pattern.search(str(error))
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if match is not None:
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return callback(body, match)
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# Wrap other errors as internal server error
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return 500, ErrorResponse(
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message=str(error),
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type="internal_server_error",
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param=None,
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code=None,
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)
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def get_route_handler(
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self,
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) -> Callable[[Request], Coroutine[None, None, Response]]:
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"""Defines custom route handler that catches exceptions and formats
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in OpenAI style error response"""
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original_route_handler = super().get_route_handler()
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async def custom_route_handler(request: Request) -> Response:
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try:
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start_sec = time.perf_counter()
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response = await original_route_handler(request)
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elapsed_time_ms = int((time.perf_counter() - start_sec) * 1000)
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response.headers["openai-processing-ms"] = f"{elapsed_time_ms}"
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return response
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except Exception as exc:
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json_body = await request.json()
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try:
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if "messages" in json_body:
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# Chat completion
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body: Optional[
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Union[
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CreateChatCompletionRequest,
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CreateCompletionRequest,
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CreateEmbeddingRequest,
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]
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] = CreateChatCompletionRequest(**json_body)
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elif "prompt" in json_body:
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# Text completion
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body = CreateCompletionRequest(**json_body)
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else:
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# Embedding
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body = CreateEmbeddingRequest(**json_body)
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except Exception:
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# Invalid request body
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body = None
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# Get proper error message from the exception
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(
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status_code,
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error_message,
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) = self.error_message_wrapper(error=exc, body=body)
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return JSONResponse(
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{"error": error_message},
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status_code=status_code,
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)
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return custom_route_handler
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router = APIRouter(route_class=RouteErrorHandler)
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settings: Optional[Settings] = None
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llama: Optional[llama_cpp.Llama] = None
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def create_app(settings: Optional[Settings] = None):
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if settings is None:
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settings = Settings()
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middleware = [
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Middleware(RawContextMiddleware, plugins=(plugins.RequestIdPlugin(),))
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]
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app = FastAPI(
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middleware=middleware,
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title="🦙 llama.cpp Python API",
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version="0.0.1",
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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app.include_router(router)
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global llama
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##
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chat_handler = None
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if settings.chat_format == "llava-1-5":
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assert settings.clip_model_path is not None
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chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(
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clip_model_path=settings.clip_model_path, verbose=settings.verbose
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)
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##
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llama = llama_cpp.Llama(
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model_path=settings.model,
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# Model Params
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n_gpu_layers=settings.n_gpu_layers,
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main_gpu=settings.main_gpu,
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tensor_split=settings.tensor_split,
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vocab_only=settings.vocab_only,
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use_mmap=settings.use_mmap,
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use_mlock=settings.use_mlock,
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# Context Params
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seed=settings.seed,
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n_ctx=settings.n_ctx,
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n_batch=settings.n_batch,
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n_threads=settings.n_threads,
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n_threads_batch=settings.n_threads_batch,
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rope_scaling_type=settings.rope_scaling_type,
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rope_freq_base=settings.rope_freq_base,
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rope_freq_scale=settings.rope_freq_scale,
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yarn_ext_factor=settings.yarn_ext_factor,
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yarn_attn_factor=settings.yarn_attn_factor,
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yarn_beta_fast=settings.yarn_beta_fast,
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yarn_beta_slow=settings.yarn_beta_slow,
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yarn_orig_ctx=settings.yarn_orig_ctx,
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mul_mat_q=settings.mul_mat_q,
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f16_kv=settings.f16_kv,
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logits_all=settings.logits_all,
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embedding=settings.embedding,
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# Sampling Params
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last_n_tokens_size=settings.last_n_tokens_size,
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# LoRA Params
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lora_base=settings.lora_base,
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lora_path=settings.lora_path,
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# Backend Params
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numa=settings.numa,
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# Chat Format Params
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chat_format=settings.chat_format,
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chat_handler=chat_handler,
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# Misc
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verbose=settings.verbose,
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)
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if settings.cache:
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if settings.cache_type == "disk":
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if settings.verbose:
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print(f"Using disk cache with size {settings.cache_size}")
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cache = llama_cpp.LlamaDiskCache(capacity_bytes=settings.cache_size)
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else:
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if settings.verbose:
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print(f"Using ram cache with size {settings.cache_size}")
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cache = llama_cpp.LlamaRAMCache(capacity_bytes=settings.cache_size)
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cache = llama_cpp.LlamaCache(capacity_bytes=settings.cache_size)
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llama.set_cache(cache)
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def set_settings(_settings: Settings):
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global settings
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settings = _settings
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set_settings(settings)
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return app
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llama_outer_lock = Lock()
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llama_inner_lock = Lock()
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def get_llama():
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# NOTE: This double lock allows the currently streaming llama model to
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# check if any other requests are pending in the same thread and cancel
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# the stream if so.
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llama_outer_lock.acquire()
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release_outer_lock = True
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try:
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llama_inner_lock.acquire()
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try:
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llama_outer_lock.release()
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release_outer_lock = False
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yield llama
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finally:
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llama_inner_lock.release()
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finally:
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if release_outer_lock:
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llama_outer_lock.release()
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def get_settings():
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yield settings
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async def get_event_publisher(
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request: Request,
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inner_send_chan: MemoryObjectSendStream,
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iterator: Iterator,
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):
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async with inner_send_chan:
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try:
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async for chunk in iterate_in_threadpool(iterator):
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await inner_send_chan.send(dict(data=json.dumps(chunk)))
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if await request.is_disconnected():
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raise anyio.get_cancelled_exc_class()()
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if settings.interrupt_requests and llama_outer_lock.locked():
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await inner_send_chan.send(dict(data="[DONE]"))
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raise anyio.get_cancelled_exc_class()()
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await inner_send_chan.send(dict(data="[DONE]"))
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except anyio.get_cancelled_exc_class() as e:
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print("disconnected")
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with anyio.move_on_after(1, shield=True):
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print(f"Disconnected from client (via refresh/close) {request.client}")
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raise e
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model_field = Field(
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description="The model to use for generating completions.", default=None
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)
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max_tokens_field = Field(
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default=16, ge=1, description="The maximum number of tokens to generate."
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)
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temperature_field = Field(
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default=0.8,
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ge=0.0,
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le=2.0,
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description="Adjust the randomness of the generated text.\n\n"
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+ "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.",
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)
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top_p_field = Field(
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default=0.95,
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ge=0.0,
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le=1.0,
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description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
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+ "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.",
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)
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min_p_field = Field(
|
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default=0.05,
|
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ge=0.0,
|
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le=1.0,
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description="Sets a minimum base probability threshold for token selection.\n\n"
|
|
+ "The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.",
|
|
)
|
|
|
|
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"
|
|
)
|
|
|
|
grammar = Field(
|
|
default=None,
|
|
description="A CBNF grammar (as string) to be used for formatting the model's output.",
|
|
)
|
|
|
|
|
|
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
|
|
min_p: float = min_p_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)
|
|
seed: 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(default=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)
|
|
mirostat_mode: int = mirostat_mode_field
|
|
mirostat_tau: float = mirostat_tau_field
|
|
mirostat_eta: float = mirostat_eta_field
|
|
grammar: Optional[str] = None
|
|
|
|
model_config = {
|
|
"json_schema_extra": {
|
|
"examples": [
|
|
{
|
|
"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
|
|
"stop": ["\n", "###"],
|
|
}
|
|
]
|
|
}
|
|
}
|
|
|
|
|
|
def _logit_bias_tokens_to_input_ids(
|
|
llama: llama_cpp.Llama,
|
|
logit_bias: Dict[str, float],
|
|
) -> Dict[str, float]:
|
|
to_bias: Dict[str, float] = {}
|
|
for token, score in logit_bias.items():
|
|
token = token.encode("utf-8")
|
|
for input_id in llama.tokenize(token, add_bos=False, special=True):
|
|
to_bias[str(input_id)] = score
|
|
return to_bias
|
|
|
|
|
|
@router.post(
|
|
"/v1/completions",
|
|
)
|
|
@router.post("/v1/engines/copilot-codex/completions")
|
|
async def create_completion(
|
|
request: Request,
|
|
body: CreateCompletionRequest,
|
|
llama: llama_cpp.Llama = Depends(get_llama),
|
|
) -> llama_cpp.Completion:
|
|
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_type",
|
|
"user",
|
|
}
|
|
kwargs = body.model_dump(exclude=exclude)
|
|
|
|
if body.logit_bias is not None:
|
|
kwargs["logit_bias"] = (
|
|
_logit_bias_tokens_to_input_ids(llama, body.logit_bias)
|
|
if body.logit_bias_type == "tokens"
|
|
else body.logit_bias
|
|
)
|
|
|
|
if body.grammar is not None:
|
|
kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
|
|
|
|
iterator_or_completion: Union[
|
|
llama_cpp.CreateCompletionResponse,
|
|
Iterator[llama_cpp.CreateCompletionStreamResponse],
|
|
] = await run_in_threadpool(llama, **kwargs)
|
|
|
|
if isinstance(iterator_or_completion, Iterator):
|
|
# EAFP: It's easier to ask for forgiveness than permission
|
|
first_response = await run_in_threadpool(next, iterator_or_completion)
|
|
|
|
# If no exception was raised from first_response, we can assume that
|
|
# the iterator is valid and we can use it to stream the response.
|
|
def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]:
|
|
yield first_response
|
|
yield from iterator_or_completion
|
|
|
|
send_chan, recv_chan = anyio.create_memory_object_stream(10)
|
|
return EventSourceResponse(
|
|
recv_chan,
|
|
data_sender_callable=partial( # type: ignore
|
|
get_event_publisher,
|
|
request=request,
|
|
inner_send_chan=send_chan,
|
|
iterator=iterator(),
|
|
),
|
|
)
|
|
else:
|
|
return iterator_or_completion
|
|
|
|
|
|
class CreateEmbeddingRequest(BaseModel):
|
|
model: Optional[str] = model_field
|
|
input: Union[str, List[str]] = Field(description="The input to embed.")
|
|
user: Optional[str] = Field(default=None)
|
|
|
|
model_config = {
|
|
"json_schema_extra": {
|
|
"examples": [
|
|
{
|
|
"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.model_dump(exclude={"user"})
|
|
)
|
|
|
|
|
|
class ChatCompletionRequestMessage(BaseModel):
|
|
role: Literal["system", "user", "assistant", "function"] = Field(
|
|
default="user", description="The role of the message."
|
|
)
|
|
content: Optional[str] = Field(
|
|
default="", description="The content of the message."
|
|
)
|
|
|
|
|
|
class CreateChatCompletionRequest(BaseModel):
|
|
messages: List[llama_cpp.ChatCompletionRequestMessage] = Field(
|
|
default=[], description="A list of messages to generate completions for."
|
|
)
|
|
functions: Optional[List[llama_cpp.ChatCompletionFunction]] = Field(
|
|
default=None,
|
|
description="A list of functions to apply to the generated completions.",
|
|
)
|
|
function_call: Optional[llama_cpp.ChatCompletionRequestFunctionCall] = Field(
|
|
default=None,
|
|
description="A function to apply to the generated completions.",
|
|
)
|
|
tools: Optional[List[llama_cpp.ChatCompletionTool]] = Field(
|
|
default=None,
|
|
description="A list of tools to apply to the generated completions.",
|
|
)
|
|
tool_choice: Optional[llama_cpp.ChatCompletionToolChoiceOption] = Field(
|
|
default=None,
|
|
description="A tool to apply to the generated completions.",
|
|
) # TODO: verify
|
|
max_tokens: Optional[int] = Field(
|
|
default=None,
|
|
description="The maximum number of tokens to generate. Defaults to inf",
|
|
)
|
|
temperature: float = temperature_field
|
|
top_p: float = top_p_field
|
|
min_p: float = min_p_field
|
|
stop: Optional[Union[str, 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)
|
|
seed: Optional[int] = Field(None)
|
|
response_format: Optional[llama_cpp.ChatCompletionRequestResponseFormat] = Field(
|
|
default=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)
|
|
mirostat_mode: int = mirostat_mode_field
|
|
mirostat_tau: float = mirostat_tau_field
|
|
mirostat_eta: float = mirostat_eta_field
|
|
grammar: Optional[str] = None
|
|
|
|
model_config = {
|
|
"json_schema_extra": {
|
|
"examples": [
|
|
{
|
|
"messages": [
|
|
ChatCompletionRequestMessage(
|
|
role="system", content="You are a helpful assistant."
|
|
).model_dump(),
|
|
ChatCompletionRequestMessage(
|
|
role="user", content="What is the capital of France?"
|
|
).model_dump(),
|
|
]
|
|
}
|
|
]
|
|
}
|
|
}
|
|
|
|
|
|
@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),
|
|
) -> llama_cpp.ChatCompletion:
|
|
exclude = {
|
|
"n",
|
|
"logit_bias_type",
|
|
"user",
|
|
}
|
|
kwargs = body.model_dump(exclude=exclude)
|
|
|
|
if body.logit_bias is not None:
|
|
kwargs["logit_bias"] = (
|
|
_logit_bias_tokens_to_input_ids(llama, body.logit_bias)
|
|
if body.logit_bias_type == "tokens"
|
|
else body.logit_bias
|
|
)
|
|
|
|
if body.grammar is not None:
|
|
kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
|
|
|
|
iterator_or_completion: Union[
|
|
llama_cpp.ChatCompletion, Iterator[llama_cpp.ChatCompletionChunk]
|
|
] = await run_in_threadpool(llama.create_chat_completion, **kwargs)
|
|
|
|
if isinstance(iterator_or_completion, Iterator):
|
|
# EAFP: It's easier to ask for forgiveness than permission
|
|
first_response = await run_in_threadpool(next, iterator_or_completion)
|
|
|
|
# If no exception was raised from first_response, we can assume that
|
|
# the iterator is valid and we can use it to stream the response.
|
|
def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]:
|
|
yield first_response
|
|
yield from iterator_or_completion
|
|
|
|
send_chan, recv_chan = anyio.create_memory_object_stream(10)
|
|
return EventSourceResponse(
|
|
recv_chan,
|
|
data_sender_callable=partial( # type: ignore
|
|
get_event_publisher,
|
|
request=request,
|
|
inner_send_chan=send_chan,
|
|
iterator=iterator(),
|
|
),
|
|
)
|
|
else:
|
|
return iterator_or_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": [],
|
|
}
|
|
],
|
|
}
|