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507 lines
21 KiB
Python
507 lines
21 KiB
Python
"""
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Python only API for running all GPT4All models.
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"""
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from __future__ import annotations
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import os
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import re
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import sys
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import time
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import warnings
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from contextlib import contextmanager
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from pathlib import Path
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from typing import Any, Dict, Iterable, List, Optional, Union, overload
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import requests
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from requests.exceptions import ChunkedEncodingError
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from tqdm import tqdm
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from urllib3.exceptions import IncompleteRead, ProtocolError
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from . import _pyllmodel
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# TODO: move to config
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DEFAULT_MODEL_DIRECTORY = os.path.join(str(Path.home()), ".cache", "gpt4all").replace("\\", "\\\\")
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DEFAULT_MODEL_CONFIG = {
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"systemPrompt": "",
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"promptTemplate": "### Human: \n{0}\n\n### Assistant:\n",
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}
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ConfigType = Dict[str, str]
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MessageType = Dict[str, str]
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class Embed4All:
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"""
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Python class that handles embeddings for GPT4All.
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"""
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MIN_DIMENSIONALITY = 64
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def __init__(self, model_name: Optional[str] = None, n_threads: Optional[int] = None, **kwargs):
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"""
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Constructor
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Args:
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n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
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"""
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self.gpt4all = GPT4All(model_name or 'all-MiniLM-L6-v2-f16.gguf', n_threads=n_threads, **kwargs)
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@overload
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def embed(
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self, text: str, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
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atlas: bool = ...,
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) -> list[float]: ...
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@overload
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def embed(
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self, text: list[str], prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
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atlas: bool = ...,
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) -> list[list[float]]: ...
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def embed(self, text, prefix=None, dimensionality=None, long_text_mode="truncate", atlas=False):
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"""
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Generate one or more embeddings.
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Args:
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text: A text or list of texts to generate embeddings for.
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prefix: The model-specific prefix representing the embedding task, without the trailing colon. For Nomic
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Embed this can be `search_query`, `search_document`, `classification`, or `clustering`.
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dimensionality: The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.
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long_text_mode: How to handle texts longer than the model can accept. One of `mean` or `truncate`.
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atlas: Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens
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with long_text_mode="mean" will raise an error. Disabled by default.
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Returns:
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An embedding or list of embeddings of your text(s).
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"""
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if dimensionality is None:
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dimensionality = -1
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else:
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if dimensionality <= 0:
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raise ValueError(f'Dimensionality must be None or a positive integer, got {dimensionality}')
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if dimensionality < self.MIN_DIMENSIONALITY:
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warnings.warn(
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f'Dimensionality {dimensionality} is less than the suggested minimum of {self.MIN_DIMENSIONALITY}.'
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' Performance may be degraded.'
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)
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try:
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do_mean = {"mean": True, "truncate": False}[long_text_mode]
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except KeyError:
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raise ValueError(f"Long text mode must be one of 'mean' or 'truncate', got {long_text_mode!r}")
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return self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas)
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class GPT4All:
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"""
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Python class that handles instantiation, downloading, generation and chat with GPT4All models.
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"""
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def __init__(
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self,
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model_name: str,
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model_path: Optional[Union[str, os.PathLike[str]]] = None,
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model_type: Optional[str] = None,
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allow_download: bool = True,
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n_threads: Optional[int] = None,
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device: Optional[str] = "cpu",
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n_ctx: int = 2048,
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ngl: int = 100,
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verbose: bool = False,
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):
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"""
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Constructor
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Args:
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model_name: Name of GPT4All or custom model. Including ".gguf" file extension is optional but encouraged.
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model_path: Path to directory containing model file or, if file does not exist, where to download model.
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Default is None, in which case models will be stored in `~/.cache/gpt4all/`.
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model_type: Model architecture. This argument currently does not have any functionality and is just used as
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descriptive identifier for user. Default is None.
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allow_download: Allow API to download models from gpt4all.io. Default is True.
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n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
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device: The processing unit on which the GPT4All model will run. It can be set to:
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- "cpu": Model will run on the central processing unit.
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- "gpu": Model will run on the best available graphics processing unit, irrespective of its vendor.
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- "amd", "nvidia", "intel": Model will run on the best available GPU from the specified vendor.
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Alternatively, a specific GPU name can also be provided, and the model will run on the GPU that matches the name if it's available.
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Default is "cpu".
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Note: If a selected GPU device does not have sufficient RAM to accommodate the model, an error will be thrown, and the GPT4All instance will be rendered invalid. It's advised to ensure the device has enough memory before initiating the model.
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n_ctx: Maximum size of context window
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ngl: Number of GPU layers to use (Vulkan)
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verbose: If True, print debug messages.
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"""
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self.model_type = model_type
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# Retrieve model and download if allowed
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self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download, verbose=verbose)
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self.model = _pyllmodel.LLModel(self.config["path"], n_ctx, ngl)
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if device is not None and device != "cpu":
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self.model.init_gpu(device)
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self.model.load_model()
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# Set n_threads
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if n_threads is not None:
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self.model.set_thread_count(n_threads)
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self._is_chat_session_activated: bool = False
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self.current_chat_session: List[MessageType] = empty_chat_session()
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self._current_prompt_template: str = "{0}"
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@staticmethod
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def list_models() -> List[ConfigType]:
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"""
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Fetch model list from https://gpt4all.io/models/models2.json.
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Returns:
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Model list in JSON format.
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"""
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resp = requests.get("https://gpt4all.io/models/models2.json")
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if resp.status_code != 200:
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raise ValueError(f'Request failed: HTTP {resp.status_code} {resp.reason}')
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return resp.json()
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@staticmethod
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def retrieve_model(
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model_name: str,
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model_path: Optional[Union[str, os.PathLike[str]]] = None,
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allow_download: bool = True,
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verbose: bool = False,
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) -> ConfigType:
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"""
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Find model file, and if it doesn't exist, download the model.
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Args:
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model_name: Name of model.
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model_path: Path to find model. Default is None in which case path is set to
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~/.cache/gpt4all/.
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allow_download: Allow API to download model from gpt4all.io. Default is True.
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verbose: If True (default), print debug messages.
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Returns:
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Model config.
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"""
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model_filename = append_extension_if_missing(model_name)
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# get the config for the model
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config: ConfigType = DEFAULT_MODEL_CONFIG
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if allow_download:
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available_models = GPT4All.list_models()
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for m in available_models:
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if model_filename == m["filename"]:
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config.update(m)
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config["systemPrompt"] = config["systemPrompt"].strip()
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# change to Python-style formatting
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config["promptTemplate"] = config["promptTemplate"].replace("%1", "{0}", 1).replace("%2", "{1}", 1)
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break
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# Validate download directory
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if model_path is None:
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try:
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os.makedirs(DEFAULT_MODEL_DIRECTORY, exist_ok=True)
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except OSError as exc:
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raise ValueError(
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f"Failed to create model download directory at {DEFAULT_MODEL_DIRECTORY}: {exc}. "
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"Please specify model_path."
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)
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model_path = DEFAULT_MODEL_DIRECTORY
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else:
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model_path = str(model_path).replace("\\", "\\\\")
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if not os.path.exists(model_path):
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raise ValueError(f"Invalid model directory: {model_path}")
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model_dest = os.path.join(model_path, model_filename).replace("\\", "\\\\")
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if os.path.exists(model_dest):
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config.pop("url", None)
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config["path"] = model_dest
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if verbose:
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print("Found model file at", model_dest, file=sys.stderr)
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# If model file does not exist, download
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elif allow_download:
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url = config.pop("url", None)
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config["path"] = GPT4All.download_model(model_filename, model_path, verbose=verbose, url=url)
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else:
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raise ValueError("Failed to retrieve model")
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return config
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@staticmethod
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def download_model(
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model_filename: str,
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model_path: Union[str, os.PathLike[str]],
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verbose: bool = True,
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url: Optional[str] = None,
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) -> str:
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"""
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Download model from https://gpt4all.io.
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Args:
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model_filename: Filename of model (with .gguf extension).
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model_path: Path to download model to.
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verbose: If True (default), print debug messages.
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url: the models remote url (e.g. may be hosted on HF)
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Returns:
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Model file destination.
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"""
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def get_download_url(model_filename):
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if url:
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return url
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return f"https://gpt4all.io/models/gguf/{model_filename}"
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# Download model
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download_path = os.path.join(model_path, model_filename).replace("\\", "\\\\")
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download_url = get_download_url(model_filename)
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def make_request(offset=None):
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headers = {}
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if offset:
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print(f"\nDownload interrupted, resuming from byte position {offset}", file=sys.stderr)
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headers['Range'] = f'bytes={offset}-' # resume incomplete response
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response = requests.get(download_url, stream=True, headers=headers)
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if response.status_code not in (200, 206):
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raise ValueError(f'Request failed: HTTP {response.status_code} {response.reason}')
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if offset and (response.status_code != 206 or str(offset) not in response.headers.get('Content-Range', '')):
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raise ValueError('Connection was interrupted and server does not support range requests')
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return response
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response = make_request()
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total_size_in_bytes = int(response.headers.get("content-length", 0))
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block_size = 2**20 # 1 MB
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with open(download_path, "wb") as file, \
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tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) as progress_bar:
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try:
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while True:
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last_progress = progress_bar.n
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try:
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for data in response.iter_content(block_size):
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file.write(data)
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progress_bar.update(len(data))
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except ChunkedEncodingError as cee:
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if cee.args and isinstance(pe := cee.args[0], ProtocolError):
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if len(pe.args) >= 2 and isinstance(ir := pe.args[1], IncompleteRead):
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assert progress_bar.n <= ir.partial # urllib3 may be ahead of us but never behind
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# the socket was closed during a read - retry
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response = make_request(progress_bar.n)
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continue
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raise
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if total_size_in_bytes != 0 and progress_bar.n < total_size_in_bytes:
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if progress_bar.n == last_progress:
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raise RuntimeError('Download not making progress, aborting.')
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# server closed connection prematurely - retry
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response = make_request(progress_bar.n)
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continue
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break
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except Exception:
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if verbose:
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print("Cleaning up the interrupted download...", file=sys.stderr)
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try:
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os.remove(download_path)
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except OSError:
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pass
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raise
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if os.name == 'nt':
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time.sleep(2) # Sleep for a little bit so Windows can remove file lock
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if verbose:
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print("Model downloaded at:", download_path, file=sys.stderr)
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return download_path
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def generate(
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self,
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prompt: str,
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max_tokens: int = 200,
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temp: float = 0.7,
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top_k: int = 40,
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top_p: float = 0.4,
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min_p: float = 0.0,
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repeat_penalty: float = 1.18,
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repeat_last_n: int = 64,
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n_batch: int = 8,
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n_predict: Optional[int] = None,
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streaming: bool = False,
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callback: _pyllmodel.ResponseCallbackType = _pyllmodel.empty_response_callback,
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) -> Union[str, Iterable[str]]:
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"""
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Generate outputs from any GPT4All model.
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Args:
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prompt: The prompt for the model the complete.
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max_tokens: The maximum number of tokens to generate.
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temp: The model temperature. Larger values increase creativity but decrease factuality.
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top_k: Randomly sample from the top_k most likely tokens at each generation step. Set this to 1 for greedy decoding.
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top_p: Randomly sample at each generation step from the top most likely tokens whose probabilities add up to top_p.
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min_p: Randomly sample at each generation step from the top most likely tokens whose probabilities are at least min_p.
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repeat_penalty: Penalize the model for repetition. Higher values result in less repetition.
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repeat_last_n: How far in the models generation history to apply the repeat penalty.
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n_batch: Number of prompt tokens processed in parallel. Larger values decrease latency but increase resource requirements.
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n_predict: Equivalent to max_tokens, exists for backwards compatibility.
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streaming: If True, this method will instead return a generator that yields tokens as the model generates them.
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callback: A function with arguments token_id:int and response:str, which receives the tokens from the model as they are generated and stops the generation by returning False.
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Returns:
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Either the entire completion or a generator that yields the completion token by token.
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"""
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if re.search(r"%1(?![0-9])", self._current_prompt_template):
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raise ValueError("Prompt template containing a literal '%1' is not supported. For a prompt "
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"placeholder, please use '{0}' instead.")
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# Preparing the model request
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generate_kwargs: Dict[str, Any] = dict(
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temp=temp,
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top_k=top_k,
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top_p=top_p,
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min_p=min_p,
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repeat_penalty=repeat_penalty,
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repeat_last_n=repeat_last_n,
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n_batch=n_batch,
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n_predict=n_predict if n_predict is not None else max_tokens,
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)
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if self._is_chat_session_activated:
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# check if there is only one message, i.e. system prompt:
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reset = len(self.current_chat_session) == 1
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generate_kwargs["reset_context"] = reset
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self.current_chat_session.append({"role": "user", "content": prompt})
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fct_func = self._format_chat_prompt_template.__func__ # type: ignore[attr-defined]
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if fct_func is GPT4All._format_chat_prompt_template:
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if reset:
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# ingest system prompt
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self.model.prompt_model(self.current_chat_session[0]["content"], "%1",
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_pyllmodel.empty_response_callback,
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n_batch=n_batch, n_predict=0, special=True)
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prompt_template = self._current_prompt_template.format("%1", "%2")
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else:
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warnings.warn(
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"_format_chat_prompt_template is deprecated. Please use a chat session with a prompt template.",
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DeprecationWarning,
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)
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# special tokens won't be processed
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prompt = self._format_chat_prompt_template(
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self.current_chat_session[-1:],
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self.current_chat_session[0]["content"] if reset else "",
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)
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prompt_template = "%1"
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else:
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prompt_template = "%1"
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generate_kwargs["reset_context"] = True
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# Prepare the callback, process the model response
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output_collector: List[MessageType]
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output_collector = [
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{"content": ""}
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] # placeholder for the self.current_chat_session if chat session is not activated
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if self._is_chat_session_activated:
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self.current_chat_session.append({"role": "assistant", "content": ""})
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output_collector = self.current_chat_session
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def _callback_wrapper(
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callback: _pyllmodel.ResponseCallbackType,
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output_collector: List[MessageType],
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) -> _pyllmodel.ResponseCallbackType:
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def _callback(token_id: int, response: str) -> bool:
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nonlocal callback, output_collector
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output_collector[-1]["content"] += response
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return callback(token_id, response)
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return _callback
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# Send the request to the model
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if streaming:
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return self.model.prompt_model_streaming(
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prompt,
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prompt_template,
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_callback_wrapper(callback, output_collector),
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**generate_kwargs,
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)
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self.model.prompt_model(
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prompt,
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prompt_template,
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_callback_wrapper(callback, output_collector),
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**generate_kwargs,
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)
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return output_collector[-1]["content"]
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@contextmanager
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def chat_session(
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self,
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system_prompt: str = "",
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prompt_template: str = "",
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):
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"""
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Context manager to hold an inference optimized chat session with a GPT4All model.
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Args:
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system_prompt: An initial instruction for the model.
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prompt_template: Template for the prompts with {0} being replaced by the user message.
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"""
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# Code to acquire resource, e.g.:
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self._is_chat_session_activated = True
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self.current_chat_session = empty_chat_session(system_prompt or self.config["systemPrompt"])
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self._current_prompt_template = prompt_template or self.config["promptTemplate"]
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try:
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yield self
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finally:
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# Code to release resource, e.g.:
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self._is_chat_session_activated = False
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self.current_chat_session = empty_chat_session()
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self._current_prompt_template = "{0}"
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def _format_chat_prompt_template(
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self,
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messages: List[MessageType],
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default_prompt_header: str = "",
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default_prompt_footer: str = "",
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) -> str:
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"""
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Helper method for building a prompt from list of messages using the self._current_prompt_template as a template for each message.
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Args:
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messages: List of dictionaries. Each dictionary should have a "role" key
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with value of "system", "assistant", or "user" and a "content" key with a
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string value. Messages are organized such that "system" messages are at top of prompt,
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and "user" and "assistant" messages are displayed in order. Assistant messages get formatted as
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"Response: {content}".
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Returns:
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Formatted prompt.
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"""
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full_prompt = default_prompt_header + "\n\n" if default_prompt_header != "" else ""
|
|
|
|
for message in messages:
|
|
if message["role"] == "user":
|
|
user_message = self._current_prompt_template.format(message["content"])
|
|
full_prompt += user_message
|
|
if message["role"] == "assistant":
|
|
assistant_message = message["content"] + "\n"
|
|
full_prompt += assistant_message
|
|
|
|
full_prompt += "\n\n" + default_prompt_footer if default_prompt_footer != "" else ""
|
|
|
|
return full_prompt
|
|
|
|
|
|
def empty_chat_session(system_prompt: str = "") -> List[MessageType]:
|
|
return [{"role": "system", "content": system_prompt}]
|
|
|
|
|
|
def append_extension_if_missing(model_name):
|
|
if not model_name.endswith((".bin", ".gguf")):
|
|
model_name += ".gguf"
|
|
return model_name
|