From e147d771f3c5cc74971fd168ca4a7da555719836 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:47:28 +0200 Subject: [PATCH 01/20] Remove Lark grammars --- outlines/__init__.py | 2 - outlines/grammars.py | 14 ------ outlines/grammars/arithmetic.lark | 18 ------- outlines/grammars/common.lark | 80 ------------------------------- outlines/grammars/json.lark | 19 -------- tests/test_grammars.py | 31 ------------ 6 files changed, 164 deletions(-) delete mode 100644 outlines/grammars.py delete mode 100644 outlines/grammars/arithmetic.lark delete mode 100644 outlines/grammars/common.lark delete mode 100644 outlines/grammars/json.lark delete mode 100644 tests/test_grammars.py diff --git a/outlines/__init__.py b/outlines/__init__.py index 307d2ba6..d41000bd 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -1,6 +1,5 @@ """Outlines is a Generative Model Programming Framework.""" import outlines.generate -import outlines.grammars import outlines.models import outlines.processors import outlines.types @@ -16,5 +15,4 @@ "Function", "prompt", "vectorize", - "grammars", ] diff --git a/outlines/grammars.py b/outlines/grammars.py deleted file mode 100644 index f0c12296..00000000 --- a/outlines/grammars.py +++ /dev/null @@ -1,14 +0,0 @@ -from pathlib import Path - -GRAMMAR_PATH = Path(__file__).parent / "grammars" - - -def read_grammar(grammar_file_name, base_grammar_path=GRAMMAR_PATH): - """Read grammar file from default grammar path""" - full_path = base_grammar_path / grammar_file_name - with open(full_path) as file: - return file.read() - - -arithmetic = read_grammar("arithmetic.lark") -json = read_grammar("json.lark") diff --git a/outlines/grammars/arithmetic.lark b/outlines/grammars/arithmetic.lark deleted file mode 100644 index 2332650c..00000000 --- a/outlines/grammars/arithmetic.lark +++ /dev/null @@ -1,18 +0,0 @@ -?start: sum - -?sum: product -| sum "+" product -> add -| sum "-" product -> sub - -?product: atom -| product "*" atom -> mul -| product "/" atom -> div - -?atom: NUMBER -> number -| "-" atom -> neg -| "(" sum ")" - -%import common.NUMBER -%import common.WS_INLINE - -%ignore WS_INLINE diff --git a/outlines/grammars/common.lark b/outlines/grammars/common.lark deleted file mode 100644 index 801c27e9..00000000 --- a/outlines/grammars/common.lark +++ /dev/null @@ -1,80 +0,0 @@ -// Adapted from https://github.com/lark-parser/lark/blob/master/lark/grammars/common.lark - -// Lark License: -// Copyright Β© 2017 Erez Shinan -// -// Permission is hereby granted, free of charge, to any person obtaining a copy of -// this software and associated documentation files (the "Software"), to deal in -// the Software without restriction, including without limitation the rights to -// use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -// the Software, and to permit persons to whom the Software is furnished to do so, -// subject to the following conditions: -// -// The above copyright notice and this permission notice shall be included in all -// copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -// FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -// COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -// IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - -// Basic terminals for common use - - -// -// Numbers -// - -DIGIT: "0".."9" -HEXDIGIT: "a".."f"|"A".."F"|DIGIT - -INT: DIGIT+ -SIGNED_INT: ["+"|"-"] INT -DECIMAL: INT "." INT? | "." INT - -// float = /-?\d+(\.\d+)?([eE][+-]?\d+)?/ -_EXP: ("e"|"E") SIGNED_INT -FLOAT: INT _EXP | DECIMAL _EXP? -SIGNED_FLOAT: ["+"|"-"] FLOAT - -NUMBER: FLOAT | INT -SIGNED_NUMBER: ["+"|"-"] NUMBER - -// -// TODO: Working escaped_string -// -UNESCAPED_STRING: /\"[^"]*\"/ - - - -// -// Names (Variables) -// -LCASE_LETTER: "a".."z" -UCASE_LETTER: "A".."Z" - -LETTER: UCASE_LETTER | LCASE_LETTER -WORD: LETTER+ - -CNAME: ("_"|LETTER) ("_"|LETTER|DIGIT)* - - -// -// Whitespace -// -WS_INLINE: (" "|/\t/)+ -WS: /[ \t\f\r\n]/+ - -CR : /\r/ -LF : /\n/ -NEWLINE: (CR? LF)+ - - -// Comments -SH_COMMENT: /#[^\n]*/ -CPP_COMMENT: /\/\/[^\n]*/ -C_COMMENT: "/*" /(.|\n)*?/ "*/" -SQL_COMMENT: /--[^\n]*/ diff --git a/outlines/grammars/json.lark b/outlines/grammars/json.lark deleted file mode 100644 index 72af448c..00000000 --- a/outlines/grammars/json.lark +++ /dev/null @@ -1,19 +0,0 @@ -?start: value - -?value: object -| array -| UNESCAPED_STRING -| SIGNED_NUMBER -> number -| "true" -> true -| "false" -> false -| "null" -> null - -array : "[" [value ("," value)*] "]" -object : "{" [pair ("," pair)*] "}" -pair : UNESCAPED_STRING ":" value - -%import common.UNESCAPED_STRING -%import common.SIGNED_NUMBER -%import common.WS - -%ignore WS diff --git a/tests/test_grammars.py b/tests/test_grammars.py deleted file mode 100644 index 3d704f09..00000000 --- a/tests/test_grammars.py +++ /dev/null @@ -1,31 +0,0 @@ -import pytest - -import outlines.grammars as grammars -from outlines.fsm.guide import CFGGuide - - -@pytest.mark.parametrize("grammar", [grammars.json, grammars.arithmetic]) -def test_grammar_module(grammar): - class MockTokenizer: - vocabulary = {"(": 1, ")": 2, "a": 3, "eos": 4} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 4 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: s - s: "(" s ")" | /a+/ - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - assert isinstance(fsm, CFGGuide) From fc06746450ec1ffd9bea7486effa2c5e3d66cc5e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:47:56 +0200 Subject: [PATCH 02/20] Remove prompts and functions --- outlines/__init__.py | 4 - outlines/function.py | 117 -------------- outlines/prompts.py | 338 ----------------------------------------- tests/test_function.py | 133 ---------------- tests/test_prompts.py | 314 -------------------------------------- 5 files changed, 906 deletions(-) delete mode 100644 outlines/function.py delete mode 100644 outlines/prompts.py delete mode 100644 tests/test_function.py delete mode 100644 tests/test_prompts.py diff --git a/outlines/__init__.py b/outlines/__init__.py index d41000bd..22c85bbb 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -5,14 +5,10 @@ import outlines.types from outlines.base import vectorize from outlines.caching import clear_cache, disable_cache, get_cache -from outlines.function import Function -from outlines.prompts import prompt __all__ = [ "clear_cache", "disable_cache", "get_cache", - "Function", - "prompt", "vectorize", ] diff --git a/outlines/function.py b/outlines/function.py deleted file mode 100644 index 48577be8..00000000 --- a/outlines/function.py +++ /dev/null @@ -1,117 +0,0 @@ -import importlib.util -from dataclasses import dataclass -from typing import TYPE_CHECKING, Callable, Optional, Tuple, Union - -import requests - -from outlines import generate, models - -if TYPE_CHECKING: - from outlines.generate.api import SequenceGenerator - from outlines.prompts import Prompt - - -@dataclass -class Function: - """Represents an Outlines function. - - Functions are a convenient way to encapsulate a prompt template, a language - model and a Pydantic model that define the output structure. Once defined, - the function can be called with arguments that will be used to render the - prompt template. - - """ - - prompt_template: "Prompt" - schema: Union[str, Callable, object] - model_name: str - generator: Optional["SequenceGenerator"] = None - - @classmethod - def from_github(cls, program_path: str, function_name: str = "fn"): - """Load a function stored on GitHub""" - program_content = download_from_github(program_path) - function = extract_function_from_file(program_content, function_name) - - return function - - def init_generator(self): - """Load the model and initialize the generator.""" - model = models.transformers(self.model_name) - self.generator = generate.json(model, self.schema) - - def __call__(self, *args, **kwargs): - """Call the function. - - .. warning:: - - This currently does not support batching. - - Parameters - ---------- - args - Values to pass to the prompt template as positional arguments. - kwargs - Values to pass to the prompt template as keyword arguments. - - """ - if self.generator is None: - self.init_generator() - - prompt = self.prompt_template(*args, **kwargs) - return self.generator(prompt) - - -def download_from_github(short_path: str): - """Download the file in which the function is stored on GitHub.""" - GITHUB_BASE_URL = "https://raw.githubusercontent.com" - BRANCH = "main" - - path = short_path.split("/") - if len(path) < 3: - raise ValueError( - "Please provide a valid path in the form {USERNAME}/{REPO_NAME}/{PATH_TO_FILE}." - ) - elif short_path[-3:] == ".py": - raise ValueError("Do not append the `.py` extension to the program name.") - - username = path[0] - repo = path[1] - path_to_file = path[2:] - - url = "/".join([GITHUB_BASE_URL, username, repo, BRANCH] + path_to_file) + ".py" - result = requests.get(url) - - if result.status_code == 200: - return result.text - elif result.status_code == 404: - raise ValueError( - f"Program could not be found at {url}. Please make sure you entered the GitHub username, repository name and path to the program correctly." - ) - else: - result.raise_for_status() - - -def extract_function_from_file(content: str, function_name: str) -> Tuple[Callable]: - """Extract a function object from a downloaded file.""" - - spec = importlib.util.spec_from_loader( - "outlines_function", loader=None, origin="github" - ) - if spec is not None: - module = importlib.util.module_from_spec(spec) - exec(content, module.__dict__) - - try: - fn = getattr(module, function_name) - except AttributeError: - raise AttributeError( - "Could not find an `outlines.Function` instance in the remote file. Make sure that the path you specified is correct." - ) - - if not isinstance(fn, module.outlines.Function): - raise TypeError( - f"The `{function_name}` variable in the program must be an instance of `outlines.Function`" - ) - - return fn diff --git a/outlines/prompts.py b/outlines/prompts.py deleted file mode 100644 index 01e900c9..00000000 --- a/outlines/prompts.py +++ /dev/null @@ -1,338 +0,0 @@ -import functools -import inspect -import json -import re -import textwrap -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Type, cast - -from jinja2 import Environment, StrictUndefined -from pydantic import BaseModel - - -@dataclass -class Prompt: - """Represents a prompt function. - - We return a `Prompt` class instead of a simple function so the - template defined in prompt functions can be accessed. - - """ - - template: str - signature: inspect.Signature - - def __post_init__(self): - self.parameters: List[str] = list(self.signature.parameters.keys()) - - def __call__(self, *args, **kwargs) -> str: - """Render and return the template. - - Returns - ------- - The rendered template as a Python ``str``. - - """ - bound_arguments = self.signature.bind(*args, **kwargs) - bound_arguments.apply_defaults() - return render(self.template, **bound_arguments.arguments) - - def __str__(self): - return self.template - - -def prompt(fn: Callable) -> Prompt: - """Decorate a function that contains a prompt template. - - This allows to define prompts in the docstring of a function and simplify their - manipulation by providing some degree of encapsulation. It uses the `render` - function internally to render templates. - - >>> import outlines - >>> - >>> @outlines.prompt - >>> def build_prompt(question): - ... "I have a ${question}" - ... - >>> prompt = build_prompt("How are you?") - - This API can also be helpful in an "agent" context where parts of the prompt - are set when the agent is initialized and never modified later. In this situation - we can partially apply the prompt function at initialization. - - >>> import outlines - >>> import functools as ft - ... - >>> @outlines.prompt - ... def solve_task(name: str, objective: str, task: str): - ... '''Your name is {{name}}. - .. Your overall objective is to {{objective}}. - ... Please solve the following task: {{task}} - ... ''' - ... - >>> hal = ft.partial(solve_task, "HAL", "Travel to Jupiter") - - Returns - ------- - A `Prompt` callable class which will render the template when called. - - """ - - signature = inspect.signature(fn) - - # The docstring contains the template that will be rendered to be used - # as a prompt to the language model. - docstring = fn.__doc__ - if docstring is None: - raise TypeError("Could not find a template in the function's docstring.") - - template = cast(str, docstring) - - return Prompt(template, signature) - - -def render(template: str, **values: Optional[Dict[str, Any]]) -> str: - r"""Parse a Jinaj2 template and translate it into an Outlines graph. - - This function removes extra whitespaces and linebreaks from templates to - allow users to enter prompts more naturally than if they used Python's - constructs directly. See the examples for a detailed explanation. - - Examples - -------- - - Outlines follow Jinja2's syntax - - >>> import outlines - >>> outline = outlines.render("I like {{food}} and {{sport}}", food="tomatoes", sport="tennis") - I like tomatoes and tennis - - If the first line of the template is empty, `render` removes it - - >>> from outlines import render - >>> - >>> tpl = ''' - ... A new string''' - >>> tpl - ... '\nA new string' - >>> render(tpl) - ... 'a new string' - - Similarly, `render` ignores linebreaks introduced by placing the closing quotes - underneath the text: - - >>> tpl = ''' - ... A new string - ... ''' - >>> tpl - ... '\nA new string\n' - >>> render(tpl) - ... 'A new string' - - If you want to insert a linebreak at the end of the rendered template, you will - need to leave an empty line at the end of the template: - - >>> tpl = ''' - ... A new string - ... - ... ''' - >>> tpl - ... '\nA new string\n\n' - >>> render(tpl) - ... 'A new string\n' - - `render` removes the identation in docstrings. This is particularly important - when using prompt functions - - >>> tpl = ''' - ... a string - ... and another string''' - >>> tpl - ... '\n a string\n and another string' - >>> render(tpl) - ... 'a string\nand another string' - - The indentation of the first line is assumed to be the same as the second line's - - >>> tpl = '''a string - ... and another''' - >>> tpl - ... 'a string\n and another' - >>> render(tpl) - ... 'a string\nand another' - - To get a different indentation for the first and the second line, we can start the - prompt on the string's second line: - - >>> tpl = ''' - ... First line - ... Second line''' - >>> render(tpl) - ... 'First Line\n Second Line' - - Parameters - ---------- - template - A string that contains a template written with the Jinja2 syntax. - **values - Map from the variables in the template to their value. - - Returns - ------- - A string that contains the rendered template. - - """ - # Dedent, and remove extra linebreak - cleaned_template = inspect.cleandoc(template) - - # Add linebreak if there were any extra linebreaks that - # `cleandoc` would have removed - ends_with_linebreak = template.replace(" ", "").endswith("\n\n") - if ends_with_linebreak: - cleaned_template += "\n" - - # Remove extra whitespaces, except those that immediately follow a newline symbol. - # This is necessary to avoid introducing whitespaces after backslash `\` characters - # used to continue to the next line without linebreak. - cleaned_template = re.sub(r"(?![\r\n])(\b\s+)", " ", cleaned_template) - - env = Environment( - trim_blocks=True, - lstrip_blocks=True, - keep_trailing_newline=True, - undefined=StrictUndefined, - ) - env.filters["name"] = get_fn_name - env.filters["description"] = get_fn_description - env.filters["source"] = get_fn_source - env.filters["signature"] = get_fn_signature - env.filters["schema"] = get_schema - env.filters["args"] = get_fn_args - - jinja_template = env.from_string(cleaned_template) - - return jinja_template.render(**values) - - -def get_fn_name(fn: Callable): - """Returns the name of a callable.""" - if not callable(fn): - raise TypeError("The `name` filter only applies to callables.") - - if not hasattr(fn, "__name__"): - name = type(fn).__name__ - else: - name = fn.__name__ - - return name - - -def get_fn_args(fn: Callable): - """Returns the arguments of a function with annotations and default values if provided.""" - if not callable(fn): - raise TypeError("The `args` filter only applies to callables.") - - arg_str_list = [] - signature = inspect.signature(fn) - arg_str_list = [str(param) for param in signature.parameters.values()] - arg_str = ", ".join(arg_str_list) - return arg_str - - -def get_fn_description(fn: Callable): - """Returns the first line of a callable's docstring.""" - if not callable(fn): - raise TypeError("The `description` filter only applies to callables.") - - docstring = inspect.getdoc(fn) - if docstring is None: - description = "" - else: - description = docstring.split("\n")[0].strip() - - return description - - -def get_fn_source(fn: Callable): - """Return the source code of a callable.""" - if not callable(fn): - raise TypeError("The `source` filter only applies to callables.") - - source = textwrap.dedent(inspect.getsource(fn)) - re_search = re.search(re.compile(r"(\bdef\b.*)", re.DOTALL), source) - if re_search is not None: - source = re_search.group(0) - else: - raise TypeError("Could not read the function's source code") - - return source - - -def get_fn_signature(fn: Callable): - """Return the signature of a callable.""" - if not callable(fn): - raise TypeError("The `source` filter only applies to callables.") - - source = textwrap.dedent(inspect.getsource(fn)) - re_search = re.search(re.compile(r"\(([^)]+)\)"), source) - if re_search is None: - signature = "" - else: - signature = re_search.group(1) - - return signature - - -@functools.singledispatch -def get_schema(model: Any): - raise NotImplementedError( - f"No schema rendering function defined for type {type(model)}." - ) - - -@get_schema.register(dict) -def get_schema_dict(model: Dict): - """Return a pretty-printed dictionary""" - return json.dumps(model, indent=2) - - -@get_schema.register(type(BaseModel)) -def get_schema_pydantic(model: Type[BaseModel]): - """Return the schema of a Pydantic model.""" - if not type(model) == type(BaseModel): - raise TypeError("The `schema` filter only applies to Pydantic models.") - - if hasattr(model, "model_json_schema"): - def_key = "$defs" - raw_schema = model.model_json_schema() - else: # pragma: no cover - def_key = "definitions" - raw_schema = model.schema() - - definitions = raw_schema.get(def_key, None) - schema = parse_pydantic_schema(raw_schema, definitions) - - return json.dumps(schema, indent=2) - - -def parse_pydantic_schema(raw_schema, definitions): - """Parse the output of `Basemodel.[schema|model_json_schema]()`. - - This recursively follows the references to other schemas in case - of nested models. Other schemas are stored under the "definitions" - key in the schema of the top-level model. - - """ - simple_schema = {} - for name, value in raw_schema["properties"].items(): - if "description" in value: - simple_schema[name] = value["description"] - elif "$ref" in value: - refs = value["$ref"].split("/") - simple_schema[name] = parse_pydantic_schema( - definitions[refs[2]], definitions - ) - else: - simple_schema[name] = f"<{name}>" - - return simple_schema diff --git a/tests/test_function.py b/tests/test_function.py deleted file mode 100644 index 24e132d4..00000000 --- a/tests/test_function.py +++ /dev/null @@ -1,133 +0,0 @@ -import pytest -import responses -from pydantic import BaseModel -from requests.exceptions import HTTPError - -import outlines -from outlines.function import Function, download_from_github, extract_function_from_file - - -def test_function_basic(): - @outlines.prompt - def test_template(text: str): - """{{ text }}""" - - class Foo(BaseModel): - id: int - - fn = Function(test_template, Foo, "hf-internal-testing/tiny-random-GPTJForCausalLM") - - assert fn.generator is None - - result = fn("test") - assert isinstance(result, BaseModel) - - -def test_download_from_github_invalid(): - with pytest.raises(ValueError, match="Please provide"): - download_from_github("outlines/program") - - with pytest.raises(ValueError, match="Do not append"): - download_from_github("outlines-dev/outlines/program.py") - - -@responses.activate -def test_download_from_github_success(): - responses.add( - responses.GET, - "https://raw.githubusercontent.com/outlines-dev/outlines/main/program.py", - body="import outlines\n", - status=200, - ) - - file = download_from_github("outlines-dev/outlines/program") - assert file == "import outlines\n" - - responses.add( - responses.GET, - "https://raw.githubusercontent.com/outlines-dev/outlines/main/foo/bar/program.py", - body="import outlines\n", - status=200, - ) - - file = download_from_github("outlines-dev/outlines/foo/bar/program") - assert file == "import outlines\n" - - -@responses.activate -def test_download_from_github_error(): - responses.add( - responses.GET, - "https://raw.githubusercontent.com/foo/bar/main/program.py", - json={"error": "not found"}, - status=404, - ) - - with pytest.raises(ValueError, match="Program could not be found at"): - download_from_github("foo/bar/program") - - responses.add( - responses.GET, - "https://raw.githubusercontent.com/foo/bar/main/program.py", - json={"error": "Internal Server Error"}, - status=500, - ) - - with pytest.raises(HTTPError, match="500 Server Error"): - download_from_github("foo/bar/program") - - -def test_extract_function_from_file(): - content = """ -import outlines -from pydantic import BaseModel - -model = "gpt2" - - -@outlines.prompt -def prompt(): - '''Hello''' - - -class User(BaseModel): - id: int - name: str - - -function = outlines.Function( - prompt, - User, - "gpt2", -) - """ - - fn = extract_function_from_file(content, "function") - assert ( - str(type(fn)) == "" - ) # because imported via `exec` - - -def test_extract_function_from_file_no_function(): - content = """ -import outlines -from pydantic import BaseModel - -@outlines.prompt -def prompt(): - '''Hello''' - - -class User(BaseModel): - id: int - name: str - -program = outlines.Function( - prompt, - User, - "gpt2", -) - """ - - with pytest.raises(AttributeError, match="Could not find"): - extract_function_from_file(content, "function") diff --git a/tests/test_prompts.py b/tests/test_prompts.py deleted file mode 100644 index a0433c0e..00000000 --- a/tests/test_prompts.py +++ /dev/null @@ -1,314 +0,0 @@ -from typing import Dict, List - -import pytest -from pydantic import BaseModel, Field - -import outlines -from outlines.prompts import render - - -def test_render(): - tpl = """ - A test string""" - assert render(tpl) == "A test string" - - tpl = """ - A test string - """ - assert render(tpl) == "A test string" - - tpl = """ - A test - Another test - """ - assert render(tpl) == "A test\nAnother test" - - tpl = """A test - Another test - """ - assert render(tpl) == "A test\nAnother test" - - tpl = """ - A test line - An indented line - """ - assert render(tpl) == "A test line\n An indented line" - - tpl = """ - A test line - An indented line - - """ - assert render(tpl) == "A test line\n An indented line\n" - - -def test_render_escaped_linebreak(): - tpl = """ - A long test \ - that we break \ - in several lines - """ - assert render(tpl) == "A long test that we break in several lines" - - tpl = """ - Break in \ - several lines \ - But respect the indentation - on line breaks. - And after everything \ - Goes back to normal - """ - assert ( - render(tpl) - == "Break in several lines But respect the indentation\n on line breaks.\nAnd after everything Goes back to normal" - ) - - -def test_render_jinja(): - """Make sure that we can use basic Jinja2 syntax, and give examples - of how we can use it for basic use cases. - """ - - # Notice the newline after the end of the loop - examples = ["one", "two"] - prompt = render( - """ - {% for e in examples %} - Example: {{e}} - {% endfor -%}""", - examples=examples, - ) - assert prompt == "Example: one\nExample: two\n" - - # We can remove the newline by cloing with -%} - examples = ["one", "two"] - prompt = render( - """ - {% for e in examples %} - Example: {{e}} - {% endfor -%} - - Final""", - examples=examples, - ) - assert prompt == "Example: one\nExample: two\nFinal" - - # Same for conditionals - tpl = """ - {% if is_true %} - true - {% endif -%} - - final - """ - assert render(tpl, is_true=True) == "true\nfinal" - assert render(tpl, is_true=False) == "final" - - -def test_prompt_basic(): - @outlines.prompt - def test_tpl(variable): - """{{variable}} test""" - - assert test_tpl.template == "{{variable}} test" - assert test_tpl.parameters == ["variable"] - - with pytest.raises(TypeError): - test_tpl(v="test") - - p = test_tpl("test") - assert p == "test test" - - p = test_tpl(variable="test") - assert p == "test test" - - @outlines.prompt - def test_single_quote_tpl(variable): - "${variable} test" - - p = test_tpl("test") - assert p == "test test" - - -def test_prompt_kwargs(): - @outlines.prompt - def test_kwarg_tpl(var, other_var="other"): - """{{var}} and {{other_var}}""" - - assert test_kwarg_tpl.template == "{{var}} and {{other_var}}" - assert test_kwarg_tpl.parameters == ["var", "other_var"] - - p = test_kwarg_tpl("test") - assert p == "test and other" - - p = test_kwarg_tpl("test", other_var="kwarg") - assert p == "test and kwarg" - - p = test_kwarg_tpl("test", "test") - assert p == "test and test" - - -def test_no_prompt(): - with pytest.raises(TypeError, match="template"): - - @outlines.prompt - def test_empty(variable): - pass - - with pytest.raises(TypeError, match="template"): - - @outlines.prompt - def test_only_code(variable): - return variable - - -def test_prompt_function(): - def empty_fn(): - pass - - def with_description(): - """A description. - - But this is ignored. - """ - pass - - @outlines.prompt - def name_description_ppt(fn): - """ - {{fn|name}}: {{fn|description}} - """ - - rendered = name_description_ppt(empty_fn) - assert rendered == "empty_fn: " - - rendered = name_description_ppt(with_description) - assert rendered == "with_description: A description." - - def with_signature(one: int, two: List[str], three: float = 1.0): - pass - - @outlines.prompt - def name_signature_ppt(fn): - """ - {{fn|name}}: {{fn|signature}} - """ - - rendered = name_signature_ppt(with_signature) - assert rendered == "with_signature: one: int, two: List[str], three: float = 1.0" - - def test_function_call(one, two=2): - return one + two - - @outlines.prompt - def source_ppt(fn): - """ - {{fn|source}} - """ - - rendered = source_ppt(test_function_call) - assert rendered == "def test_function_call(one, two=2):\n return one + two\n" - - -def test_prompt_pydantic_response(): - class SimpleResponse(BaseModel): - one: str = Field(description="a description") - two: str - - @outlines.prompt - def source_ppt(model): - "{{model | schema }}" - - prompt = source_ppt(SimpleResponse) - assert prompt == '{\n "one": "a description",\n "two": ""\n}' - - class NestedResponse(BaseModel): - answer: str - thought: SimpleResponse - - prompt = source_ppt(NestedResponse) - assert ( - prompt - == '{\n "answer": "",\n "thought": {\n "one": "a description",\n "two": ""\n }\n}' - ) - - class ConvolutedResponse(BaseModel): - part_one: NestedResponse - part_two: SimpleResponse - - prompt = source_ppt(ConvolutedResponse) - assert ( - prompt - == '{\n "part_one": {\n "answer": "",\n "thought": {\n "one": "a description",\n "two": ""\n }\n },\n "part_two": {\n "one": "a description",\n "two": ""\n }\n}' - ) - - -def test_prompt_dict_response(): - response = {"one": "a description", "two": ""} - - @outlines.prompt - def source_ppt(model): - "{{model | schema }}" - - prompt = source_ppt(response) - assert prompt == '{\n "one": "a description",\n "two": ""\n}' - - -def test_prompt_args(): - def no_args(): - pass - - def with_args(x, y, z): - pass - - def with_annotations(x: bool, y: str, z: Dict[int, List[str]]): - pass - - def with_defaults(x=True, y="Hi", z={4: ["I", "love", "outlines"]}): - pass - - def with_annotations_and_defaults( - x: bool = True, - y: str = "Hi", - z: Dict[int, List[str]] = {4: ["I", "love", "outlines"]}, - ): - pass - - def with_all( - x1, - y1, - z1, - x2: bool, - y2: str, - z2: Dict[int, List[str]], - x3=True, - y3="Hi", - z3={4: ["I", "love", "outlines"]}, - x4: bool = True, - y4: str = "Hi", - z4: Dict[int, List[str]] = {4: ["I", "love", "outlines"]}, - ): - pass - - @outlines.prompt - def args_prompt(fn): - """args: {{ fn | args }}""" - - assert args_prompt(no_args) == "args: " - assert args_prompt(with_args) == "args: x, y, z" - assert ( - args_prompt(with_annotations) - == "args: x: bool, y: str, z: Dict[int, List[str]]" - ) - assert ( - args_prompt(with_defaults) - == "args: x=True, y='Hi', z={4: ['I', 'love', 'outlines']}" - ) - assert ( - args_prompt(with_annotations_and_defaults) - == "args: x: bool = True, y: str = 'Hi', z: Dict[int, List[str]] = {4: ['I', 'love', 'outlines']}" - ) - assert ( - args_prompt(with_all) - == "args: x1, y1, z1, x2: bool, y2: str, z2: Dict[int, List[str]], x3=True, y3='Hi', z3={4: ['I', 'love', 'outlines']}, x4: bool = True, y4: str = 'Hi', z4: Dict[int, List[str]] = {4: ['I', 'love', 'outlines']}" - ) From e40bbecfde6ddbb4140b47ea8a78ba0d5f83c978 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:49:18 +0200 Subject: [PATCH 03/20] Remove custom types --- outlines/__init__.py | 1 - outlines/types/__init__.py | 4 -- outlines/types/airports.py | 10 ---- outlines/types/countries.py | 19 ------ outlines/types/email.py | 11 ---- outlines/types/isbn.py | 12 ---- outlines/types/locales.py | 21 ------- outlines/types/phone_numbers.py | 16 ----- outlines/types/zip_codes.py | 13 ---- tests/test_types.py | 103 -------------------------------- 10 files changed, 210 deletions(-) delete mode 100644 outlines/types/__init__.py delete mode 100644 outlines/types/airports.py delete mode 100644 outlines/types/countries.py delete mode 100644 outlines/types/email.py delete mode 100644 outlines/types/isbn.py delete mode 100644 outlines/types/locales.py delete mode 100644 outlines/types/phone_numbers.py delete mode 100644 outlines/types/zip_codes.py delete mode 100644 tests/test_types.py diff --git a/outlines/__init__.py b/outlines/__init__.py index 22c85bbb..667f7be0 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -2,7 +2,6 @@ import outlines.generate import outlines.models import outlines.processors -import outlines.types from outlines.base import vectorize from outlines.caching import clear_cache, disable_cache, get_cache diff --git a/outlines/types/__init__.py b/outlines/types/__init__.py deleted file mode 100644 index f4d2b8cd..00000000 --- a/outlines/types/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import airports, countries -from .email import Email -from .isbn import ISBN -from .locales import locale diff --git a/outlines/types/airports.py b/outlines/types/airports.py deleted file mode 100644 index b4ed6784..00000000 --- a/outlines/types/airports.py +++ /dev/null @@ -1,10 +0,0 @@ -"""Generate valid airport codes.""" -from enum import Enum - -from pyairports.airports import AIRPORT_LIST - -AIRPORT_IATA_LIST = list( - {(airport[3], airport[3]) for airport in AIRPORT_LIST if airport[3] != ""} -) - -IATA = Enum("Airport", AIRPORT_IATA_LIST) # type:ignore diff --git a/outlines/types/countries.py b/outlines/types/countries.py deleted file mode 100644 index bbfc0dde..00000000 --- a/outlines/types/countries.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Generate valid country codes and names.""" -from enum import Enum - -import pycountry - -ALPHA_2_CODE = [(country.alpha_2, country.alpha_2) for country in pycountry.countries] -Alpha2 = Enum("Alpha_2", ALPHA_2_CODE) # type:ignore - -ALPHA_3_CODE = [(country.alpha_3, country.alpha_3) for country in pycountry.countries] -Alpha3 = Enum("Alpha_2", ALPHA_3_CODE) # type:ignore - -NUMERIC_CODE = [(country.numeric, country.numeric) for country in pycountry.countries] -Numeric = Enum("Numeric_code", NUMERIC_CODE) # type:ignore - -NAME = [(country.name, country.name) for country in pycountry.countries] -Name = Enum("Name", NAME) # type:ignore - -FLAG = [(country.flag, country.flag) for country in pycountry.countries] -Flag = Enum("Flag", FLAG) # type:ignore diff --git a/outlines/types/email.py b/outlines/types/email.py deleted file mode 100644 index 45f8c4b2..00000000 --- a/outlines/types/email.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Email Address types.""" -from pydantic import WithJsonSchema -from typing_extensions import Annotated - -# Taken from StackOverflow -# https://stackoverflow.com/a/201378/14773537 -EMAIL_REGEX = r"""(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9]))\.){3}(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9])|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])""" -Email = Annotated[ - str, - WithJsonSchema({"type": "string", "pattern": EMAIL_REGEX}), -] diff --git a/outlines/types/isbn.py b/outlines/types/isbn.py deleted file mode 100644 index 5aebb067..00000000 --- a/outlines/types/isbn.py +++ /dev/null @@ -1,12 +0,0 @@ -"""ISBN type""" -from pydantic import WithJsonSchema -from typing_extensions import Annotated - -# Matches any ISBN number. Note that this is not completely correct as not all -# 10 or 13 digits numbers are valid ISBNs. See https://en.wikipedia.org/wiki/ISBN -# Taken from O'Reilly's Regular Expression Cookbook: -# https://www.oreilly.com/library/view/regular-expressions-cookbook/9781449327453/ch04s13.html -# TODO: Can this be represented by a grammar or do we need semantic checks? -ISBN_REGEX = r"(?:ISBN(?:-1[03])?:? )?(?=[0-9X]{10}$|(?=(?:[0-9]+[- ]){3})[- 0-9X]{13}$|97[89][0-9]{10}$|(?=(?:[0-9]+[- ]){4})[- 0-9]{17}$)(?:97[89][- ]?)?[0-9]{1,5}[- ]?[0-9]+[- ]?[0-9]+[- ]?[0-9X]" - -ISBN = Annotated[str, WithJsonSchema({"type": "string", "pattern": ISBN_REGEX})] diff --git a/outlines/types/locales.py b/outlines/types/locales.py deleted file mode 100644 index c5d251ba..00000000 --- a/outlines/types/locales.py +++ /dev/null @@ -1,21 +0,0 @@ -from dataclasses import dataclass - -from outlines.types.phone_numbers import USPhoneNumber -from outlines.types.zip_codes import USZipCode - - -@dataclass -class US: - ZipCode = USZipCode - PhoneNumber = USPhoneNumber - - -def locale(locale_str: str): - locales = {"us": US} - - if locale_str not in locales: - raise NotImplementedError( - f"The locale {locale_str} is not supported yet. Please don't hesitate to create custom types for you locale and open a Pull Request." - ) - - return locales[locale_str] diff --git a/outlines/types/phone_numbers.py b/outlines/types/phone_numbers.py deleted file mode 100644 index 618687e7..00000000 --- a/outlines/types/phone_numbers.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Phone number types. - -We currently only support US phone numbers. We can however imagine having custom types -for each country, for instance leveraging the `phonenumbers` library. - -""" -from pydantic import WithJsonSchema -from typing_extensions import Annotated - -US_PHONE_NUMBER = r"(\([0-9]{3}\) |[0-9]{3}-)[0-9]{3}-[0-9]{4}" - - -USPhoneNumber = Annotated[ - str, - WithJsonSchema({"type": "string", "pattern": US_PHONE_NUMBER}), -] diff --git a/outlines/types/zip_codes.py b/outlines/types/zip_codes.py deleted file mode 100644 index 67d994d5..00000000 --- a/outlines/types/zip_codes.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Zip code types. - -We currently only support US Zip Codes. - -""" -from pydantic import WithJsonSchema -from typing_extensions import Annotated - -# This matches Zip and Zip+4 codes -US_ZIP_CODE = r"\d{5}(?:-\d{4})?" - - -USZipCode = Annotated[str, WithJsonSchema({"type": "string", "pattern": US_ZIP_CODE})] diff --git a/tests/test_types.py b/tests/test_types.py deleted file mode 100644 index 5e60348b..00000000 --- a/tests/test_types.py +++ /dev/null @@ -1,103 +0,0 @@ -import re - -import pytest -from pydantic import BaseModel - -from outlines import types -from outlines.fsm.types import python_types_to_regex - - -@pytest.mark.parametrize( - "custom_type,test_string,should_match", - [ - (types.phone_numbers.USPhoneNumber, "12", False), - (types.phone_numbers.USPhoneNumber, "(123) 123-1234", True), - (types.phone_numbers.USPhoneNumber, "123-123-1234", True), - (types.zip_codes.USZipCode, "12", False), - (types.zip_codes.USZipCode, "12345", True), - (types.zip_codes.USZipCode, "12345-1234", True), - (types.ISBN, "ISBN 0-1-2-3-4-5", False), - (types.ISBN, "ISBN 978-0-596-52068-7", True), - # (types.ISBN, "ISBN 978-0-596-52068-1", True), wrong check digit - (types.ISBN, "ISBN-13: 978-0-596-52068-7", True), - (types.ISBN, "978 0 596 52068 7", True), - (types.ISBN, "9780596520687", True), - (types.ISBN, "ISBN-10: 0-596-52068-9", True), - (types.ISBN, "0-596-52068-9", True), - (types.Email, "eitan@gmail.com", True), - (types.Email, "99@yahoo.com", True), - (types.Email, "eitan@.gmail.com", False), - (types.Email, "myemail", False), - (types.Email, "eitan@gmail", False), - (types.Email, "eitan@my.custom.domain", True), - ], -) -def test_type_regex(custom_type, test_string, should_match): - class Model(BaseModel): - attr: custom_type - - schema = Model.model_json_schema() - assert schema["properties"]["attr"]["type"] == "string" - regex_str = schema["properties"]["attr"]["pattern"] - does_match = re.match(regex_str, test_string) is not None - assert does_match is should_match - - regex_str, format_fn = python_types_to_regex(custom_type) - assert isinstance(format_fn(1), str) - does_match = re.match(regex_str, test_string) is not None - assert does_match is should_match - - -def test_locale_not_implemented(): - with pytest.raises(NotImplementedError): - types.locale("fr") - - -@pytest.mark.parametrize( - "locale_str,base_types,locale_types", - [ - ( - "us", - ["ZipCode", "PhoneNumber"], - [types.zip_codes.USZipCode, types.phone_numbers.USPhoneNumber], - ) - ], -) -def test_locale(locale_str, base_types, locale_types): - for base_type, locale_type in zip(base_types, locale_types): - type = getattr(types.locale(locale_str), base_type) - assert type == locale_type - - -@pytest.mark.parametrize( - "custom_type,test_string,should_match", - [ - (types.airports.IATA, "CDG", True), - (types.airports.IATA, "XXX", False), - (types.countries.Alpha2, "FR", True), - (types.countries.Alpha2, "XX", False), - (types.countries.Alpha3, "UKR", True), - (types.countries.Alpha3, "XXX", False), - (types.countries.Numeric, "004", True), - (types.countries.Numeric, "900", False), - (types.countries.Name, "Ukraine", True), - (types.countries.Name, "Wonderland", False), - (types.countries.Flag, "πŸ‡ΏπŸ‡Ό", True), - (types.countries.Flag, "πŸ€—", False), - ], -) -def test_type_enum(custom_type, test_string, should_match): - type_name = custom_type.__name__ - - class Model(BaseModel): - attr: custom_type - - schema = Model.model_json_schema() - assert isinstance(schema["$defs"][type_name]["enum"], list) - does_match = test_string in schema["$defs"][type_name]["enum"] - assert does_match is should_match - - regex_str, format_fn = python_types_to_regex(custom_type) - assert isinstance(format_fn(1), str) - does_match = re.match(regex_str, test_string) is not None - assert does_match is should_match From 5127e6d58e5b759fafe155238c926f3165f0a2b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:50:03 +0200 Subject: [PATCH 04/20] Remove server integrations --- outlines/serve/__init__.py | 0 outlines/serve/serve.py | 139 ------------------------------------- 2 files changed, 139 deletions(-) delete mode 100644 outlines/serve/__init__.py delete mode 100644 outlines/serve/serve.py diff --git a/outlines/serve/__init__.py b/outlines/serve/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/outlines/serve/serve.py b/outlines/serve/serve.py deleted file mode 100644 index 998fbc45..00000000 --- a/outlines/serve/serve.py +++ /dev/null @@ -1,139 +0,0 @@ -# _______________________________ -# / Don't want to self-host? \ -# \ Try .json at http://dottxt.co / -# ------------------------------- -# \ ^__^ -# \ (oo)\_______ -# (__)\ )\/\ -# ||----w | -# || || -# -# -# Copyright 2024- the Outlines developers -# Copyright 2023 the vLLM developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import argparse -import json -from typing import AsyncGenerator - -import uvicorn -from fastapi import FastAPI, Request -from fastapi.responses import JSONResponse, Response, StreamingResponse -from vllm.engine.arg_utils import AsyncEngineArgs -from vllm.engine.async_llm_engine import AsyncLLMEngine -from vllm.sampling_params import SamplingParams -from vllm.utils import random_uuid - -from outlines.models.vllm import adapt_tokenizer -from outlines.processors import JSONLogitsProcessor, RegexLogitsProcessor - -TIMEOUT_KEEP_ALIVE = 5 # seconds. -TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds. -app = FastAPI() -engine = None -tokenizer = None - - -@app.get("/health") -async def health() -> Response: - """Health check.""" - return Response(status_code=200) - - -@app.post("/generate") -async def generate(request: Request) -> Response: - """Generate completion for the request. - - The request should be a JSON object with the following fields: - - prompt: the prompt to use for the generation. - - schema: the JSON schema to use for the generation (if regex is not provided). - - regex: the regex to use for the generation (if schema is not provided). - - stream: whether to stream the results or not. - - other fields: the sampling parameters (See `SamplingParams` for details). - """ - assert engine is not None - - request_dict = await request.json() - prompt = request_dict.pop("prompt") - stream = request_dict.pop("stream", False) - - json_schema = request_dict.pop("schema", None) - regex_string = request_dict.pop("regex", None) - if json_schema is not None: - logits_processors = [JSONLogitsProcessor(json_schema, tokenizer)] - elif regex_string is not None: - logits_processors = [RegexLogitsProcessor(regex_string, tokenizer)] - else: - logits_processors = [] - - sampling_params = SamplingParams( - **request_dict, logits_processors=logits_processors # type: ignore - ) - request_id = random_uuid() - - results_generator = engine.generate(prompt, sampling_params, request_id) # type: ignore - - # Streaming case - async def stream_results() -> AsyncGenerator[bytes, None]: - async for request_output in results_generator: - prompt = request_output.prompt - text_outputs = [prompt + output.text for output in request_output.outputs] - ret = {"text": text_outputs} - yield (json.dumps(ret) + "\0").encode("utf-8") - - if stream: - return StreamingResponse(stream_results()) - - # Non-streaming case - final_output = None - async for request_output in results_generator: - if await request.is_disconnected(): - # Abort the request if the client disconnects. - await engine.abort(request_id) # type: ignore - return Response(status_code=499) - final_output = request_output - - assert final_output is not None - prompt = final_output.prompt - text_outputs = [prompt + output.text for output in final_output.outputs] - ret = {"text": text_outputs} - return JSONResponse(ret) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--host", type=str, default=None) - parser.add_argument("--port", type=int, default=8000) - parser.add_argument("--ssl-keyfile", type=str, default=None) - parser.add_argument("--ssl-certfile", type=str, default=None) - parser = AsyncEngineArgs.add_cli_args(parser) - args = parser.parse_args() - - # Adds the `engine_use_ray`, `disable_log_requests` and `max_log_len` - # arguments - engine_args: AsyncEngineArgs = AsyncEngineArgs.from_cli_args(args) # type: ignore - - # Sets default for the model (`facebook/opt-125m`) - engine = AsyncLLMEngine.from_engine_args(engine_args) - tokenizer = adapt_tokenizer(tokenizer=engine.engine.tokenizer.tokenizer) - - uvicorn.run( - app, - host=args.host, - port=args.port, - log_level="debug", - timeout_keep_alive=TIMEOUT_KEEP_ALIVE, - ssl_keyfile=args.ssl_keyfile, - ssl_certfile=args.ssl_certfile, - ) From 1792544753ba7d4243df715fca6298219b300dbe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:50:59 +0200 Subject: [PATCH 05/20] Remove OpenAI integration and vectorization utils --- outlines/__init__.py | 1 - outlines/base.py | 288 --------------------- outlines/generate/cfg.py | 9 - outlines/generate/choice.py | 17 -- outlines/generate/format.py | 9 - outlines/generate/json.py | 11 - outlines/generate/regex.py | 14 +- outlines/generate/text.py | 13 +- outlines/models/__init__.py | 1 - outlines/models/openai.py | 484 ------------------------------------ tests/models/test_openai.py | 105 -------- tests/test_base.py | 275 -------------------- 12 files changed, 2 insertions(+), 1225 deletions(-) delete mode 100644 outlines/base.py delete mode 100644 outlines/models/openai.py delete mode 100644 tests/models/test_openai.py delete mode 100644 tests/test_base.py diff --git a/outlines/__init__.py b/outlines/__init__.py index 667f7be0..b050521f 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -2,7 +2,6 @@ import outlines.generate import outlines.models import outlines.processors -from outlines.base import vectorize from outlines.caching import clear_cache, disable_cache, get_cache __all__ = [ diff --git a/outlines/base.py b/outlines/base.py deleted file mode 100644 index 4de8ccf5..00000000 --- a/outlines/base.py +++ /dev/null @@ -1,288 +0,0 @@ -import asyncio -import builtins -import functools -import inspect -from typing import Callable, Optional - -import numpy as np -from numpy.lib.function_base import ( - _calculate_shapes, - _parse_gufunc_signature, - _parse_input_dimensions, - _update_dim_sizes, -) - -# Allow nested loops for running in notebook. We don't enable it globally as it -# may interfere with other libraries that use asyncio. -if hasattr(builtins, "__IPYTHON__"): - try: - import nest_asyncio - - nest_asyncio.apply() - except ImportError: - print( - "Couldn't patch nest_asyncio because it's not installed. Running in the notebook might be have issues" - ) - - -class vectorize: - """Returns an object that acts like a function but takes arrays as an input. - - The vectorized function evaluates `func` over successive tuples of the input - chararrays and returns a single NumPy chararrays or a tuple of NumPy chararrays. - - Its behavior is similar to NumPy's `vectorize` for Python functions: the function - being vectorized is executed in a `for` loop. Coroutines, however, are executed - concurrently. - - Part of the code was adapted from `numpy.lib.function_base`. - - """ - - def __init__(self, func: Callable, signature: Optional[str] = None): - self.func = func - self.signature = signature - self.is_coroutine_fn = inspect.iscoroutinefunction(func) - - functools.update_wrapper(self, func) - - if signature is not None: - # Parse the signature string into a Python data structure. - # For instance "(m),(s)->(s,m)" becomes `([(m,),(s,)],[(s,m)])`. - self._in_and_out_core_dimensions = _parse_gufunc_signature(signature) - else: - self._in_and_out_core_dimensions = None - - def __call__(self, *args, **kwargs): - """Call the vectorized function.""" - if not args and not kwargs: - return self.call_thunk() - elif self.signature is not None: - return self.call_with_signature(*args, **kwargs) - else: - return self.call_no_signature(*args, **kwargs) - - def call_thunk(self): - """Call a vectorized thunk. - - Thunks have no arguments and can thus be called directly. - - """ - if self.is_coroutine_fn: - loop = asyncio.new_event_loop() - try: - outputs = loop.run_until_complete(self.func()) - finally: - loop.close() - else: - outputs = self.func() - - return outputs - - def call_no_signature(self, *args, **kwargs): - """Call functions and coroutines when no signature is specified. - - When no signature is specified we assume that all of the function's - inputs and outputs are scalars (core dimension of zero). We first - broadcast the input arrays, then iteratively apply the function over the - elements of the broadcasted arrays and finally reshape the results to - match the input shape. - - Functions are executed in a for loop, coroutines are executed - concurrently. - - """ - # Convert args and kwargs to arrays - args = [np.array(arg) for arg in args] - kwargs = {key: np.array(value) for key, value in kwargs.items()} - - # Broadcast args and kwargs - broadcast_shape = np.broadcast(*args, *list(kwargs.values())).shape - args = [np.broadcast_to(arg, broadcast_shape) for arg in args] - kwargs = { - key: np.broadcast_to(value, broadcast_shape) - for key, value in kwargs.items() - } - - # Execute functions in a loop, and coroutines concurrently - if self.is_coroutine_fn: - outputs = self.vectorize_call_coroutine(broadcast_shape, args, kwargs) - else: - outputs = self.vectorize_call(broadcast_shape, args, kwargs) - - # `outputs` is a flat array or a tuple of flat arrays. We reshape the arrays - # to match the input shape. - outputs = [ - results if isinstance(results, tuple) else (results,) for results in outputs - ] - outputs = tuple( - [np.asarray(x).reshape(broadcast_shape).squeeze() for x in zip(*outputs)] - ) - outputs = tuple([x.item() if np.ndim(x) == 0 else x for x in outputs]) - - n_results = len(list(outputs)) - - return outputs[0] if n_results == 1 else outputs - - def call_with_signature(self, *args, **kwargs): - """Call functions and coroutines when a signature is specified.""" - input_core_dims, output_core_dims = self._in_and_out_core_dimensions - - # Make sure that the numbers of arguments passed is compatible with - # the signature. - num_args = len(args) + len(kwargs) - if num_args != len(input_core_dims): - raise TypeError( - "wrong number of positional arguments: " - "expected %r, got %r" % (len(input_core_dims), len(args)) - ) - - # Convert args and kwargs to arrays - args = [np.asarray(arg) for arg in args] - kwargs = {key: np.array(value) for key, value in kwargs.items()} - - # Find the arguments' broadcast shape, and map placeholder - # variables in the signature to the number of dimensions - # they correspond to given the arguments. - broadcast_shape, dim_sizes = _parse_input_dimensions( - args + list(kwargs.values()), input_core_dims - ) - - # Calculate the shape to which each of the arguments should be broadcasted - # and reshape them accordingly. - input_shapes = _calculate_shapes(broadcast_shape, dim_sizes, input_core_dims) - args = [ - np.broadcast_to(arg, shape, subok=True) - for arg, shape in zip(args, input_shapes) - ] - kwargs = { - key: np.broadcast_to(value, broadcast_shape) - for key, value in kwargs.items() - } - - n_out = len(output_core_dims) - - if self.is_coroutine_fn: - outputs = self.vectorize_call_coroutine(broadcast_shape, args, kwargs) - else: - outputs = self.vectorize_call(broadcast_shape, args, kwargs) - - outputs = [ - results if isinstance(results, tuple) else (results,) for results in outputs - ] - - flat_outputs = list(zip(*outputs)) - n_results = len(flat_outputs) - - if n_out != n_results: - raise ValueError( - f"wrong number of outputs from the function, expected {n_out}, got {n_results}" - ) - - # The number of dimensions of the outputs are not necessarily known in - # advance. The following iterates over the results and updates the - # number of dimensions of the outputs accordingly. - for results, core_dims in zip(flat_outputs, output_core_dims): - for result in results: - _update_dim_sizes(dim_sizes, result, core_dims) - - # Calculate the shape to which each of the outputs should be broadcasted - # and reshape them. - shapes = _calculate_shapes(broadcast_shape, dim_sizes, output_core_dims) - outputs = tuple( - [ - np.hstack(results).reshape(shape).squeeze() - for shape, results in zip(shapes, zip(*outputs)) - ] - ) - outputs = tuple([x.item() if np.ndim(x) == 0 else x for x in outputs]) - - return outputs[0] if n_results == 1 else outputs - - def vectorize_call(self, broadcast_shape, args, kwargs): - """Run the function in a for loop. - - A possible extension would be to parallelize the calls. - - Parameters - ---------- - broadcast_shape - The brodcast shape of the input arrays. - args - The function's broadcasted arguments. - kwargs - The function's broadcasted keyword arguments. - - """ - outputs = [] - for index in np.ndindex(*broadcast_shape): - current_args = tuple(arg[index] for arg in args) - current_kwargs = {key: value[index] for key, value in kwargs.items()} - outputs.append(self.func(*current_args, **current_kwargs)) - - return outputs - - def vectorize_call_coroutine(self, broadcast_shape, args, kwargs): - """Run coroutines concurrently. - - Creates as many tasks as needed and executes them in a new event - loop. - - Parameters - ---------- - broadcast_shape - The brodcast shape of the input arrays. - args - The function's broadcasted arguments. - kwargs - The function's broadcasted keyword arguments. - - """ - - async def create_and_gather_tasks(): - tasks = [] - for index in np.ndindex(*broadcast_shape): - current_args = tuple(arg[index] for arg in args) - current_kwargs = {key: value[index] for key, value in kwargs.items()} - tasks.append(self.func(*current_args, **current_kwargs)) - - outputs = await asyncio.gather(*tasks) - - return outputs - - loop = asyncio.new_event_loop() - try: - outputs = loop.run_until_complete(create_and_gather_tasks()) - finally: - loop.close() - - return outputs - - -def _update_arrays_type(arrays, results): - """Update the dtype of arrays. - - String arrays contain strings of fixed length. Here they are initialized with - the type of the first results, so that if the next results contain longer - strings they will be truncated when added to the output arrays. Here we - update the type if the current results contain longer strings than in the - current output array. - - Parameters - ---------- - arrays - Arrays that contain the vectorized function's results. - results - The current output of the function being vectorized. - - """ - - updated_arrays = [] - for array, result in zip(arrays, results): - if array.dtype.type == np.str_: - if array.dtype < np.array(result).dtype: - array = array.astype(np.array(result).dtype) - - updated_arrays.append(array) - - return tuple(updated_arrays) diff --git a/outlines/generate/cfg.py b/outlines/generate/cfg.py index 0df83306..6af41400 100644 --- a/outlines/generate/cfg.py +++ b/outlines/generate/cfg.py @@ -1,7 +1,6 @@ from functools import singledispatch from outlines.generate.api import SequenceGeneratorAdapter -from outlines.models import OpenAI from outlines.samplers import Sampler, multinomial @@ -30,11 +29,3 @@ def cfg( + "Please subscribe to https://github.com/outlines-dev/outlines/issues/684" + " for updates on the fix." ) - - -@cfg.register(OpenAI) -def cfg_openai(model, cfg_str: str, sampler: Sampler = multinomial()): - raise NotImplementedError( - "Cannot use grammar-structured generation with an OpenAI model" - + "due to the limitations of the OpenAI API." - ) diff --git a/outlines/generate/choice.py b/outlines/generate/choice.py index 931409a5..970364d7 100644 --- a/outlines/generate/choice.py +++ b/outlines/generate/choice.py @@ -2,7 +2,6 @@ from typing import Callable, List from outlines.generate.api import SequenceGeneratorAdapter -from outlines.models import OpenAI from outlines.samplers import Sampler, multinomial from .regex import regex @@ -18,19 +17,3 @@ def choice( generator.format_sequence = lambda x: x return generator - - -@choice.register(OpenAI) -def choice_openai( - model: OpenAI, choices: List[str], sampler: Sampler = multinomial() -) -> Callable: - if not isinstance(sampler, multinomial): - raise NotImplementedError( - r"The OpenAI API does not support any other sampling algorithm " - + "that the multinomial sampler." - ) - - def generate_choice(prompt: str, max_tokens: int = 1): - return model.generate_choice(prompt, choices, max_tokens) - - return generate_choice diff --git a/outlines/generate/format.py b/outlines/generate/format.py index 88acec75..ff7346b4 100644 --- a/outlines/generate/format.py +++ b/outlines/generate/format.py @@ -2,7 +2,6 @@ from outlines.fsm.types import python_types_to_regex from outlines.generate.api import SequenceGeneratorAdapter -from outlines.models import OpenAI from outlines.samplers import Sampler, multinomial from .regex import regex @@ -37,11 +36,3 @@ def format( generator.format_sequence = format_fn return generator - - -@format.register(OpenAI) -def format_openai(model, python_type, sampler: Sampler = multinomial()): - raise NotImplementedError( - "Cannot use Python type-structured generation with an OpenAI model" - + " due to the limitations of the OpenAI API." - ) diff --git a/outlines/generate/json.py b/outlines/generate/json.py index 6209840e..04e8bfb7 100644 --- a/outlines/generate/json.py +++ b/outlines/generate/json.py @@ -6,7 +6,6 @@ from outlines.fsm.json_schema import build_regex_from_schema, get_schema_from_signature from outlines.generate.api import SequenceGeneratorAdapter -from outlines.models import OpenAI from outlines.samplers import Sampler, multinomial from .regex import regex @@ -66,13 +65,3 @@ def json( ) return generator - - -@json.register(OpenAI) -def json_openai( - model, schema_object: Union[str, object, Callable], sampler: Sampler = multinomial() -): - raise NotImplementedError( - "Cannot use JSON Schema-structure generation with an OpenAI model " - + "due to the limitations of the OpenAI API" - ) diff --git a/outlines/generate/regex.py b/outlines/generate/regex.py index 815a8b1b..c731cb9d 100644 --- a/outlines/generate/regex.py +++ b/outlines/generate/regex.py @@ -6,7 +6,7 @@ SequenceGeneratorAdapter, VisionSequenceGeneratorAdapter, ) -from outlines.models import ExLlamaV2Model, OpenAI, TransformersVision +from outlines.models import ExLlamaV2Model, TransformersVision from outlines.samplers import Sampler, multinomial @@ -61,15 +61,3 @@ def regex_exllamav2( generator = SequenceGenerator(fsm, model, sampler, device) return generator - - -@regex.register(OpenAI) -def regex_openai( - model: OpenAI, - regex_str: str, - sampler: Sampler = multinomial(), -): - raise NotImplementedError( - "Cannot use regex-structured generation with an OpenAI model" - + "due to the limitations of the OpenAI API." - ) diff --git a/outlines/generate/text.py b/outlines/generate/text.py index 3fe3dc55..907402e4 100644 --- a/outlines/generate/text.py +++ b/outlines/generate/text.py @@ -6,7 +6,7 @@ SequenceGeneratorAdapter, VisionSequenceGeneratorAdapter, ) -from outlines.models import ExLlamaV2Model, OpenAI, TransformersVision +from outlines.models import ExLlamaV2Model, TransformersVision from outlines.samplers import Sampler, multinomial @@ -46,14 +46,3 @@ def text_exllamav2(model, sampler: Sampler = multinomial()) -> SequenceGenerator @text.register(TransformersVision) def text_vision(model, sampler: Sampler = multinomial()): return VisionSequenceGeneratorAdapter(model, None, sampler) - - -@text.register(OpenAI) -def text_openai(model: OpenAI, sampler: Sampler = multinomial()) -> OpenAI: - if not isinstance(sampler, multinomial): - raise NotImplementedError( - r"The OpenAI API does not support any other sampling algorithm " - + "than the multinomial sampler." - ) - - return model diff --git a/outlines/models/__init__.py b/outlines/models/__init__.py index d28fcb2d..28b5af3e 100644 --- a/outlines/models/__init__.py +++ b/outlines/models/__init__.py @@ -11,7 +11,6 @@ from .exllamav2 import ExLlamaV2Model, exl2 from .llamacpp import LlamaCpp, llamacpp from .mlxlm import MLXLM, mlxlm -from .openai import OpenAI, azure_openai, openai from .transformers import Transformers, TransformerTokenizer, mamba, transformers from .transformers_vision import TransformersVision, transformers_vision from .vllm import VLLM, vllm diff --git a/outlines/models/openai.py b/outlines/models/openai.py deleted file mode 100644 index 692e64ac..00000000 --- a/outlines/models/openai.py +++ /dev/null @@ -1,484 +0,0 @@ -"""Integration with OpenAI's API.""" -import functools -import warnings -from dataclasses import asdict, dataclass, field, replace -from itertools import zip_longest -from typing import Callable, Dict, List, Optional, Set, Tuple, Union - -import numpy as np - -from outlines.base import vectorize -from outlines.caching import cache - -__all__ = ["OpenAI", "openai", "azure_openai"] - - -@dataclass(frozen=True) -class OpenAIConfig: - """Represents the parameters of the OpenAI API. - - The information was last fetched on 2023/11/20. We document below the - properties that are specific to the OpenAI API. Not all these properties are - supported by Outlines. - - Properties - ---------- - model - The name of the model. Available models can be found on OpenAI's website. - frequence_penalty - Number between 2.0 and -2.0. Positive values penalize new tokens based on - their existing frequency in the text, - logit_bias - Modifies the likelihood of specified tokens to appear in the completion. - Number between -100 (forbid) and +100 (only allows). - n - The number of completions to return for each prompt. - presence_penalty - Similar to frequency penalty. - response_format - Specifies the format the model must output. `{"type": "json_object"}` - enables JSON mode. - seed - Two completions with the same `seed` value should return the same - completion. This is however not guaranteed. - stop - Up to 4 words where the API will stop the completion. - temperature - Number between 0 and 2. Higher values make the output more random, while - lower values make it more deterministic. - top_p - Number between 0 and 1. Parameter for nucleus sampling. - user - A unique identifier for the end-user. - - """ - - model: str = "" - frequency_penalty: float = 0 - logit_bias: Dict[int, int] = field(default_factory=dict) - max_tokens: Optional[int] = None - n: int = 1 - presence_penalty: float = 0 - response_format: Optional[Dict[str, str]] = None - seed: Optional[int] = None - stop: Optional[Union[str, List[str]]] = None - temperature: float = 1.0 - top_p: int = 1 - user: str = field(default_factory=str) - - -class OpenAI: - """An object that represents the OpenAI API.""" - - def __init__( - self, - client, - config, - tokenizer=None, - system_prompt: Optional[str] = None, - ): - """Create an `OpenAI` instance. - - This class supports the standard OpenAI API, the Azure OpeanAI API as - well as compatible APIs that rely on the OpenAI client. - - Parameters - ---------- - client - An instance of the API's async client. - config - An instance of `OpenAIConfig`. Can be useful to specify some - parameters that cannot be set by calling this class' methods. - tokenizer - The tokenizer associated with the model the client connects to. - - """ - - self.client = client - self.tokenizer = tokenizer - self.config = config - - # We count the total number of prompt and generated tokens as returned - # by the OpenAI API, summed over all the requests performed with this - # model instance. - self.prompt_tokens = 0 - self.completion_tokens = 0 - - def __call__( - self, - prompt: Union[str, List[str]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[List[str], str]] = None, - *, - system_prompt: Optional[str] = None, - temperature: Optional[float] = None, - samples: Optional[int] = None, - ) -> np.ndarray: - """Call the OpenAI API to generate text. - - Parameters - ---------- - prompt - A string or list of strings that will be used to prompt the model - max_tokens - The maximum number of tokens to generate - stop_at - A string or array of strings which, such that the generation stops - when they are generated. - system_prompt - The content of the system message that precedes the user's prompt. - temperature - The value of the temperature used to sample tokens - samples - The number of completions to generate for each prompt - stop_at - Up to 4 words where the API will stop the completion. - - """ - if max_tokens is None: - max_tokens = self.config.max_tokens - if stop_at is None: - stop_at = self.config.stop - if temperature is None: - temperature = self.config.temperature - if samples is None: - samples = self.config.n - - config = replace(self.config, max_tokens=max_tokens, temperature=temperature, n=samples, stop=stop_at) # type: ignore - - response, prompt_tokens, completion_tokens = generate_chat( - prompt, system_prompt, self.client, config - ) - self.prompt_tokens += prompt_tokens - self.completion_tokens += completion_tokens - - return response - - def stream(self, *args, **kwargs): - raise NotImplementedError( - "Streaming is currently not supported for the OpenAI API" - ) - - def generate_choice( - self, - prompt: str, - choices: List[str], - max_tokens: Optional[int] = None, - system_prompt: Optional[str] = None, - ) -> str: - """Call the OpenAI API to generate one of several choices. - - Parameters - ---------- - prompt - A string or list of strings that will be used to prompt the model - choices - The list of strings between which we ask the model to choose - max_tokens - The maximum number of tokens to generate - system_prompt - The content of the system message that precedes the user's prompt. - - """ - if self.tokenizer is None: - raise ValueError( - "You must initialize the `OpenAI` class with a tokenizer to use `outlines.generate.choice`" - ) - - config = replace(self.config, max_tokens=max_tokens) - - greedy = False - decoded: List[str] = [] - encoded_choices_left: List[List[int]] = [ - self.tokenizer.encode(word) for word in choices - ] - - while len(encoded_choices_left) > 0: - max_tokens_left = max([len(tokens) for tokens in encoded_choices_left]) - transposed_choices_left: List[Set] = [ - {item for item in subset if item is not None} - for subset in zip_longest(*encoded_choices_left) - ] - - if not greedy: - mask = build_optimistic_mask(transposed_choices_left) - else: - mask = {} - for token in transposed_choices_left[0]: # build greedy mask - mask[token] = 100 - - if len(mask) == 0: - break - - config = replace(config, logit_bias=mask, max_tokens=max_tokens_left) - - response, prompt_tokens, completion_tokens = generate_chat( - prompt, system_prompt, self.client, config - ) - self.prompt_tokens += prompt_tokens - self.completion_tokens += completion_tokens - - encoded_response = self.tokenizer.encode(response) - - if encoded_response in encoded_choices_left: - decoded.append(response) - break - else: - ( - encoded_response, - encoded_choices_left, - ) = find_response_choices_intersection( - encoded_response, encoded_choices_left - ) - - if len(encoded_response) == 0: - greedy = True # next iteration will be "greedy" - continue - else: - decoded.append("".join(self.tokenizer.decode(encoded_response))) - - if len(encoded_choices_left) == 1: # only one choice left - choice_left = self.tokenizer.decode(encoded_choices_left[0]) - decoded.append(choice_left) - break - - greedy = False # after each success, stay with (or switch to) "optimistic" approach - - prompt = prompt + "".join(decoded) - - choice = "".join(decoded) - - return choice - - def generate_json(self): - """Call the OpenAI API to generate a JSON object.""" - raise NotImplementedError - - def __str__(self): - return self.__class__.__name__ + " API" - - def __repr__(self): - return str(self.config) - - -@functools.partial(vectorize, signature="(),(),(),()->(s),(),()") -async def generate_chat( - prompt: str, - system_prompt: Union[str, None], - client, - config: OpenAIConfig, -) -> Tuple[np.ndarray, int, int]: - """Call OpenAI's Chat Completion API. - - Parameters - ---------- - prompt - The prompt we use to start the generation. Passed to the model - with the "user" role. - system_prompt - The system prompt, passed to the model with the "system" role - before the prompt. - client - The API client - config - An `OpenAIConfig` instance. - - Returns - ------- - A tuple that contains the model's response(s) and usage statistics. - - """ - - @error_handler - @cache() - async def call_api(prompt, system_prompt, config): - responses = await client.chat.completions.create( - messages=system_message + user_message, - **asdict(config), # type: ignore - ) - return responses.model_dump() - - system_message = ( - [{"role": "system", "content": system_prompt}] if system_prompt else [] - ) - user_message = [{"role": "user", "content": prompt}] - - responses = await call_api(prompt, system_prompt, config) - - results = np.array( - [responses["choices"][i]["message"]["content"] for i in range(config.n)] - ) - usage = responses["usage"] - - return results, usage["prompt_tokens"], usage["completion_tokens"] - - -def find_longest_intersection(response: List[int], choice: List[int]) -> List[int]: - """Find the longest intersection between the response and the choice.""" - for i, (token_r, token_c) in enumerate(zip_longest(response, choice)): - if token_r != token_c: - return response[:i] - - return response - - -def find_response_choices_intersection( - response: List[int], choices: List[List[int]] -) -> Tuple[List[int], List[List[int]]]: - """Find the longest intersection between the response and the different - choices. - - Say the response is of the form `[1, 2, 3, 4, 5]` and we have the choices - `[[1, 2], [1, 2, 3], [6, 7, 8]` then the function will return `[1, 2, 3]` as the - intersection, and `[[]]` as the list of choices left. - - Parameters - ---------- - response - The model's response - choices - The remaining possible choices - - Returns - ------- - A tuple that contains the longest intersection between the response and the - different choices, and the choices which start with this intersection, with the - intersection removed. - - """ - max_len_prefix = 0 - choices_left = [] - longest_prefix = [] - for i, choice in enumerate(choices): - # Find the longest intersection between the response and the choice. - prefix = find_longest_intersection(response, choice) - - if len(prefix) > max_len_prefix: - max_len_prefix = len(prefix) - choices_left = [choice[len(prefix) :]] - longest_prefix = prefix - - elif len(prefix) == max_len_prefix: - choices_left.append(choice[len(prefix) :]) - - return longest_prefix, choices_left - - -def build_optimistic_mask( - transposed: List[Set[int]], max_mask_size: int = 300 -) -> Dict[int, int]: - """We build the largest mask possible. - - Tokens are added from left to right, so if the encoded choices are e.g. - `[[1,2], [3,4]]`, `1` and `3` will be added before `2` and `4`. - - Parameters - ---------- - transposed - A list of lists that contain the nth token of each choice. - - """ - mask: Dict[int, int] = {} - for tokens in transposed: - for token in tokens: - if len(mask) == max_mask_size: - return mask - mask[token] = 100 - - return mask - - -def error_handler(api_call_fn: Callable) -> Callable: - """Handle OpenAI API errors and missing API key.""" - - def call(*args, **kwargs): - import openai - - try: - return api_call_fn(*args, **kwargs) - except ( - openai.APITimeoutError, - openai.InternalServerError, - openai.RateLimitError, - ) as e: - raise OSError(f"Could not connect to the OpenAI API: {e}") - except ( - openai.AuthenticationError, - openai.BadRequestError, - openai.ConflictError, - openai.PermissionDeniedError, - openai.NotFoundError, - openai.UnprocessableEntityError, - ) as e: - raise e - - return call - - -@functools.singledispatch -def openai(model_or_client, *args, **kwargs): - return OpenAI(model_or_client, *args, **kwargs) - - -@openai.register(str) -def openai_model( - model_name: str, - config: Optional[OpenAIConfig] = None, - **openai_client_params, -): - try: - import tiktoken - from openai import AsyncOpenAI - except ImportError: - raise ImportError( - "The `openai` and `tiktoken` libraries needs to be installed in order to use Outlines' OpenAI integration." - ) - - if config is not None: - config = replace(config, model=model_name) # type: ignore - else: - config = OpenAIConfig(model=model_name) - - client = AsyncOpenAI(**openai_client_params) - - try: - tokenizer = tiktoken.encoding_for_model(model_name) - except KeyError: - warnings.warn( - f"Could not find a tokenizer for model {model_name}. Using default cl100k_base." - ) - tokenizer = tiktoken.get_encoding("cl100k_base") - - return OpenAI(client, config, tokenizer) - - -def azure_openai( - deployment_name: str, - model_name: Optional[str] = None, - config: Optional[OpenAIConfig] = None, - **azure_openai_client_params, -): - try: - import tiktoken - from openai import AsyncAzureOpenAI - except ImportError: - raise ImportError( - "The `openai` and `tiktoken` libraries needs to be installed in order to use Outlines' Azure OpenAI integration." - ) - - if config is not None: - config = replace(config, model=deployment_name) # type: ignore - if config is None: - config = OpenAIConfig(model=deployment_name) - - client = AsyncAzureOpenAI(**azure_openai_client_params) - - try: - tokenizer = tiktoken.encoding_for_model(model_name or deployment_name) - except KeyError: - warnings.warn( - f"Could not find a tokenizer for model {model_name or deployment_name}. Using default cl100k_base." - ) - tokenizer = tiktoken.get_encoding("cl100k_base") - - return OpenAI(client, config, tokenizer) diff --git a/tests/models/test_openai.py b/tests/models/test_openai.py deleted file mode 100644 index 8a7870ef..00000000 --- a/tests/models/test_openai.py +++ /dev/null @@ -1,105 +0,0 @@ -import importlib -from unittest import mock -from unittest.mock import MagicMock - -import pytest -from openai import AsyncOpenAI - -from outlines.models.openai import ( - OpenAI, - OpenAIConfig, - build_optimistic_mask, - find_longest_intersection, - find_response_choices_intersection, -) - - -def module_patch(path): - """Patch functions that have the same name as the module in which they're implemented.""" - target = path - components = target.split(".") - for i in range(len(components), 0, -1): - try: - # attempt to import the module - imported = importlib.import_module(".".join(components[:i])) - - # module was imported, let's use it in the patch - patch = mock.patch(path) - patch.getter = lambda: imported - patch.attribute = ".".join(components[i:]) - return patch - except Exception: - continue - - # did not find a module, just return the default mock - return mock.patch(path) - - -def test_openai_call(): - with module_patch("outlines.models.openai.generate_chat") as mocked_generate_chat: - mocked_generate_chat.return_value = ["foo"], 1, 2 - async_client = MagicMock(spec=AsyncOpenAI, api_key="key") - - model = OpenAI( - async_client, - OpenAIConfig(max_tokens=10, temperature=0.5, n=2, stop=["."]), - ) - - assert model("bar")[0] == "foo" - assert model.prompt_tokens == 1 - assert model.completion_tokens == 2 - mocked_generate_chat_args = mocked_generate_chat.call_args - mocked_generate_chat_arg_config = mocked_generate_chat_args[0][3] - assert isinstance(mocked_generate_chat_arg_config, OpenAIConfig) - assert mocked_generate_chat_arg_config.max_tokens == 10 - assert mocked_generate_chat_arg_config.temperature == 0.5 - assert mocked_generate_chat_arg_config.n == 2 - assert mocked_generate_chat_arg_config.stop == ["."] - - model("bar", samples=3) - mocked_generate_chat_args = mocked_generate_chat.call_args - mocked_generate_chat_arg_config = mocked_generate_chat_args[0][3] - assert mocked_generate_chat_arg_config.n == 3 - - -@pytest.mark.parametrize( - "response,choice,expected_intersection,expected_choices_left", - ( - ([1, 2, 3, 4], [[5, 6]], [], [[5, 6]]), - ([1, 2, 3, 4], [[5, 6], [7, 8]], [], [[5, 6], [7, 8]]), - ([1, 2, 3, 4], [[1, 2], [7, 8]], [1, 2], [[]]), - ([1, 2], [[1, 2, 3, 4], [1, 2]], [1, 2], [[3, 4], []]), - ([1, 2, 3], [[1, 2, 3, 4], [1, 2]], [1, 2, 3], [[4]]), - ), -) -def test_find_response_choices_intersection( - response, choice, expected_intersection, expected_choices_left -): - intersection, choices_left = find_response_choices_intersection(response, choice) - assert intersection == expected_intersection - assert choices_left == expected_choices_left - - -@pytest.mark.parametrize( - "response,choice,expected_prefix", - ( - ([1, 2, 3], [1, 2, 3, 4], [1, 2, 3]), - ([1, 2, 3], [1, 2, 3], [1, 2, 3]), - ([4, 5], [1, 2, 3], []), - ), -) -def test_find_longest_common_prefix(response, choice, expected_prefix): - prefix = find_longest_intersection(response, choice) - assert prefix == expected_prefix - - -@pytest.mark.parametrize( - "transposed,mask_size,expected_mask", - ( - ([{1, 2}, {3, 4}], 3, {1: 100, 2: 100, 3: 100}), - ([{1, 2}, {3, 4}], 4, {1: 100, 2: 100, 3: 100, 4: 100}), - ), -) -def test_build_optimistic_mask(transposed, mask_size, expected_mask): - mask = build_optimistic_mask(transposed, mask_size) - assert mask == expected_mask diff --git a/tests/test_base.py b/tests/test_base.py deleted file mode 100644 index 24c84f7d..00000000 --- a/tests/test_base.py +++ /dev/null @@ -1,275 +0,0 @@ -import numpy as np -import pytest -from numpy.testing import assert_array_equal - -from outlines.base import vectorize - - -def test_vectorize_docstring(): - def test(x): - """This is a test docstring""" - return x - - fn = vectorize(test) - assert fn.__doc__ == "This is a test docstring" - assert fn.__name__ == "test" - - async def test(x): - """This is a test docstring""" - return x - - fn = vectorize(test) - assert fn.__doc__ == "This is a test docstring" - assert fn.__name__ == "test" - - -def test_vectorize_thunk(): - def thunk(): - """A thunk""" - return 1 - - fn = vectorize(thunk) - assert fn() == 1 - - async def thunk(): - """A thunk""" - return 1 - - fn = vectorize(thunk) - assert fn() == 1 - - -def test_vectorize_scalar_simple(): - def passthrough(x): - """A passthrough function.""" - return x - - fn = vectorize(passthrough) - - out_vector = fn(["one", "two", "three"]) - assert_array_equal(out_vector, ["one", "two", "three"]) - - out_array = fn([["one", "two"], ["three", "four"]]) - assert_array_equal(out_array, [["one", "two"], ["three", "four"]]) - - async def passthrough(x): - """A passthrough function.""" - return x - - fn = vectorize(passthrough) - assert fn.__doc__ == "A passthrough function." - - out_vector = fn(["one", "two", "three"]) - assert_array_equal(out_vector, ["one", "two", "three"]) - - out_array = fn([["one", "two"], ["three", "four"]]) - assert_array_equal(out_array, [["one", "two"], ["three", "four"]]) - - -def test_vectorize_scalar_multiple_outputs(): - def passthrough_multiple_outputs(x): - return x, x - - fn = vectorize(passthrough_multiple_outputs) - out = fn(["one", "two", "three"]) - assert len(out) == 2 - assert_array_equal(out[0], ["one", "two", "three"]) - assert_array_equal(out[1], ["one", "two", "three"]) - - async def passthrough_multiple_outputs(x): - return x, x - - fn = vectorize(passthrough_multiple_outputs) - out = fn(["one", "two", "three"]) - assert len(out) == 2 - assert_array_equal(out[0], ["one", "two", "three"]) - assert_array_equal(out[1], ["one", "two", "three"]) - - -def test_vectorize_scalar_args(): - def passthrough_args(*args): - """A passthrough function.""" - result = "" - for arg in args: - result += arg - return result - - fn = vectorize(passthrough_args) - - out_array = fn(["one", "two"], ["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn([["one", "two"], ["three", "four"]], ["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - async def passthrough_args(*args): - """A passthrough function.""" - result = "" - for arg in args: - result += arg - return result - - fn = vectorize(passthrough_args) - - out_array = fn(["one", "two"], ["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn([["one", "two"], ["three", "four"]], ["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - -def test_vectorize_scalar_kwargs(): - def passthrough_kwargs(**kwargs): - """A passthrough function.""" - result = "" - for _, value in kwargs.items(): - result += value - return result - - fn = vectorize(passthrough_kwargs) - - out_array = fn(first=["one", "two"], second=["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn(first=[["one", "two"], ["three", "four"]], second=["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - async def passthrough_kwargs(**kwargs): - """A passthrough function.""" - result = "" - for _, value in kwargs.items(): - result += value - return result - - fn = vectorize(passthrough_kwargs) - - out_array = fn(first=["one", "two"], second=["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn(first=[["one", "two"], ["three", "four"]], second=["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - -def test_signature_invalid(): - with pytest.raises(ValueError): - vectorize(lambda x: x, "(m,)->()") - - with pytest.raises(TypeError, match="wrong number of positional"): - fn = vectorize(lambda x, y: x, "()->()") - fn(1, 2) - - with pytest.raises(ValueError, match="wrong number of outputs"): - - def test_multioutput(x, y): - return x, y - - fn = vectorize(test_multioutput, "(),()->()") - fn(1, 2) - - -def test_vectorize_simple(): - def test(x): - return x - - fn = vectorize(test, "(m)->(m)") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["one", "two", "three"], ["four", "five", "six"]]) - - fn = vectorize(test, "()->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["one", "two", "three"], ["four", "five", "six"]]) - - -def test_vectorize_kwargs(): - def passthrough_kwargs(**kwargs): - """A passthrough function.""" - result = "" - for _, value in kwargs.items(): - result += value - return result - - fn = vectorize(passthrough_kwargs, "(),()->()") - - out_array = fn(first=["one", "two"], second=["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn(first=[["one", "two"], ["three", "four"]], second=["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - async def passthrough_kwargs(**kwargs): - """A passthrough function.""" - result = "" - for _, value in kwargs.items(): - result += value - return result - - fn = vectorize(passthrough_kwargs, "(),()->()") - - out_array = fn(first=["one", "two"], second=["1", "2"]) - assert_array_equal(out_array, ["one1", "two2"]) - - # Broadcasting - out_array = fn(first=[["one", "two"], ["three", "four"]], second=["1", "2"]) - assert_array_equal(out_array, [["one1", "two2"], ["three1", "four2"]]) - - -def test_vectorize_reduce(): - def test(x): - return x[0] - - fn = vectorize(test, "(m)->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, ["one", "four"]) - - fn = vectorize(test, "()->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["o", "t", "t"], ["f", "f", "s"]]) - - -def test_vectorize_expand(): - def test(x): - return np.array([x for i in range(3)]) - - fn = vectorize(test, "()->(s)") - out = fn(["one", "two"]) - assert_array_equal(out, [["one", "one", "one"], ["two", "two", "two"]]) - - -def test_vectorize_coroutine_simple(): - async def test(x): - return x - - fn = vectorize(test, "(m)->(m)") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["one", "two", "three"], ["four", "five", "six"]]) - - fn = vectorize(test, "()->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["one", "two", "three"], ["four", "five", "six"]]) - - -def test_vectorize_coroutine_reduce(): - async def test(x): - return x[0] - - fn = vectorize(test, "(m)->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, ["one", "four"]) - - fn = vectorize(test, "()->()") - out = fn([["one", "two", "three"], ["four", "five", "six"]]) - assert_array_equal(out, [["o", "t", "t"], ["f", "f", "s"]]) - - -def test_vectorize_coroutine_expand(): - async def test(x): - return np.array([x for i in range(3)]) - - fn = vectorize(test, "()->(s)") - out = fn(["one", "two"]) - assert_array_equal(out, [["one", "one", "one"], ["two", "two", "two"]]) From 83f906a42f4a02ede165a5352181bfaffd207229 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 13:51:27 +0200 Subject: [PATCH 06/20] Remove examples --- examples/babyagi.py | 167 --------- examples/bentoml/.bentoignore | 5 - examples/bentoml/bentofile.yaml | 9 - examples/bentoml/import_model.py | 24 -- examples/bentoml/requirements.txt | 5 - examples/bentoml/service.py | 78 ----- examples/cerebrium/cerebrium.toml | 26 -- examples/cerebrium/main.py | 43 --- examples/cfg.py | 89 ----- examples/dating_profile.py | 176 ---------- examples/llamacpp_example.py | 44 --- examples/llamacpp_processor.py | 50 --- examples/math_generate_code.py | 41 --- examples/meta_prompting.py | 168 --------- examples/modal_example.py | 81 ----- examples/parsing.py | 106 ------ examples/pick_odd_one_out.py | 43 --- examples/react.py | 76 ---- examples/sampling.ipynb | 401 ---------------------- examples/self_consistency.py | 82 ----- examples/simulation_based_inference.ipynb | 370 -------------------- examples/transformers_integration.py | 25 -- examples/vllm_integration.py | 24 -- 23 files changed, 2133 deletions(-) delete mode 100644 examples/babyagi.py delete mode 100644 examples/bentoml/.bentoignore delete mode 100644 examples/bentoml/bentofile.yaml delete mode 100644 examples/bentoml/import_model.py delete mode 100644 examples/bentoml/requirements.txt delete mode 100644 examples/bentoml/service.py delete mode 100644 examples/cerebrium/cerebrium.toml delete mode 100644 examples/cerebrium/main.py delete mode 100644 examples/cfg.py delete mode 100644 examples/dating_profile.py delete mode 100644 examples/llamacpp_example.py delete mode 100644 examples/llamacpp_processor.py delete mode 100644 examples/math_generate_code.py delete mode 100644 examples/meta_prompting.py delete mode 100644 examples/modal_example.py delete mode 100644 examples/parsing.py delete mode 100644 examples/pick_odd_one_out.py delete mode 100644 examples/react.py delete mode 100644 examples/sampling.ipynb delete mode 100644 examples/self_consistency.py delete mode 100644 examples/simulation_based_inference.ipynb delete mode 100644 examples/transformers_integration.py delete mode 100644 examples/vllm_integration.py diff --git a/examples/babyagi.py b/examples/babyagi.py deleted file mode 100644 index 6af2ead8..00000000 --- a/examples/babyagi.py +++ /dev/null @@ -1,167 +0,0 @@ -"""This example is a simplified translation of BabyAGI. - -It currently does not use the vector store retrieval - -The original repo can be found at https://github.com/yoheinakajima/babyagi -""" -from collections import deque -from typing import Deque, List - -import outlines -import outlines.models as models - -model = models.openai("gpt-3.5-turbo") -complete = outlines.generate.text(model) - - -################# -# Perform tasks # -################# - - -@outlines.prompt -def perform_task_ppt(objective: str, task: str): - """You are an AI who performs one task based on the following objective: {{objective}}. - - Your task: {{task.task_name}} - - Response: - """ - - -##################### -# Create a new task # -##################### - - -@outlines.prompt -def create_tasks_ppt( - objective: str, previous_task: str, result: str, task_list: List[str] -): - """You are an task creation AI that uses the result of an execution agent to \ - create new tasks with the following objective: {{objective}}. - - The last completed task has the result: {{result}}. - - This result was based on this task description: {{previous_task}}. These are \ - incomplete tasks: {{task_list | join(task_list)}}. - - Based on the result, create new tasks to be completed by the AI system that \ - do not overlap with incomplete tasks. - - Return the tasks as an array. - """ - - -def create_tasks_fmt(result: str) -> List[str]: - new_tasks = result.split("\n") - - task_list = [] - for task in new_tasks: - parts = task.strip().split(".", 1) - if len(parts) == 2: - task_list.append(parts[1].strip()) - - return task_list - - -######################## -# Prioritize new tasks # -######################## - - -@outlines.prompt -def prioritize_tasks_ppt(objective: str, task_names: List[str], next_task_id: int): - """You are a task prioritization AI tasked with cleaning the formatting of \ - and reprioritizing the following tasks: {{task_names}}. - - Consider the ultimate objective of your team: {{objective}}. - - Do not remove any tasks. Return the result as a numbered list, like: - #. First task - #. Second task - - Start the tasks list with the number {{next_task_id}}. - """ - - -def prioritize_tasks_fmt(result: str): - new_tasks = result.split("\n") - - task_list: Deque = deque([]) - for task in new_tasks: - parts = task.strip().split(".", 1) - if len(parts) == 2: - task_id = int(parts[0].strip()) - task_name = parts[1].strip() - task_list.append({"task_id": task_id, "task_name": task_name}) - - return task_list - - -objective = "Becoming rich while doing nothing." -first_task = { - "task_id": 1, - "task_name": "Find a repeatable, low-maintainance, scalable business.", -} -next_task_id = 1 -task_list = deque([first_task]) - - -def one_cycle(objective: str, task_list, next_task_id: int): - """One BabyAGI cycle. - - It consists in executing the highest-priority task, creating some new tasks - given the result, and re-priotizing the tasks. - - Parameters - ---------- - objective - The overall objective of the session. - task_list - The current list of tasks to perform. - task_id_counter - The current task id. - - """ - - task = task_list.popleft() - - prompt = perform_task_ppt(objective, task) - result = complete(prompt) - - prompt = create_tasks_ppt( - objective, first_task["task_name"], result, [first_task["task_name"]] - ) - new_tasks = complete(prompt) - - new_tasks = create_tasks_fmt(new_tasks) - - for task in new_tasks: - next_task_id += 1 - task_list.append({"task_id": next_task_id, "task_name": task}) - - prompt = prioritize_tasks_ppt( - objective, [task["task_name"] for task in task_list], next_task_id - ) - prioritized_tasks = complete(prompt) - - prioritized_tasks = prioritize_tasks_fmt(prioritized_tasks) - - return task, result, prioritized_tasks, next_task_id - - -# Let's run it for 5 cycles to see how it works without spending a fortune. -for _ in range(5): - print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m") - for t in task_list: - print(" β€’ " + str(t["task_name"])) - - task, result, task_list, next_task_id = one_cycle( - objective, task_list, next_task_id - ) - - print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m") - print(task) - print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m") - print(result) diff --git a/examples/bentoml/.bentoignore b/examples/bentoml/.bentoignore deleted file mode 100644 index d9cf1150..00000000 --- a/examples/bentoml/.bentoignore +++ /dev/null @@ -1,5 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints -venv/ diff --git a/examples/bentoml/bentofile.yaml b/examples/bentoml/bentofile.yaml deleted file mode 100644 index 437fa643..00000000 --- a/examples/bentoml/bentofile.yaml +++ /dev/null @@ -1,9 +0,0 @@ -service: "service:Outlines" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -python: - requirements_txt: "./requirements.txt" - lock_packages: false diff --git a/examples/bentoml/import_model.py b/examples/bentoml/import_model.py deleted file mode 100644 index c385fe3d..00000000 --- a/examples/bentoml/import_model.py +++ /dev/null @@ -1,24 +0,0 @@ -import bentoml - -MODEL_ID = "mistralai/Mistral-7B-v0.1" -BENTO_MODEL_TAG = MODEL_ID.lower().replace("/", "--") - - -def import_model(model_id, bento_model_tag): - import torch - from transformers import AutoModelForCausalLM, AutoTokenizer - - tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) - model = AutoModelForCausalLM.from_pretrained( - MODEL_ID, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - ) - - with bentoml.models.create(bento_model_tag) as bento_model_ref: - tokenizer.save_pretrained(bento_model_ref.path) - model.save_pretrained(bento_model_ref.path) - - -if __name__ == "__main__": - import_model(MODEL_ID, BENTO_MODEL_TAG) diff --git a/examples/bentoml/requirements.txt b/examples/bentoml/requirements.txt deleted file mode 100644 index bb3b90d5..00000000 --- a/examples/bentoml/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -bentoml>=1.2.11 -outlines==0.0.37 -transformers==4.38.2 -datasets==2.18.0 -accelerate==0.27.2 diff --git a/examples/bentoml/service.py b/examples/bentoml/service.py deleted file mode 100644 index 98d46894..00000000 --- a/examples/bentoml/service.py +++ /dev/null @@ -1,78 +0,0 @@ -import typing as t - -import bentoml -from import_model import BENTO_MODEL_TAG - -DEFAULT_SCHEMA = """{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}""" - - -@bentoml.service( - traffic={ - "timeout": 300, - }, - resources={ - "gpu": 1, - "gpu_type": "nvidia-l4", - }, -) -class Outlines: - bento_model_ref = bentoml.models.get(BENTO_MODEL_TAG) - - def __init__(self) -> None: - import torch - - import outlines - - self.model = outlines.models.transformers( - self.bento_model_ref.path, - device="cuda", - model_kwargs={"torch_dtype": torch.float16}, - ) - - @bentoml.api - async def generate( - self, - prompt: str = "Give me a character description.", - json_schema: t.Optional[str] = DEFAULT_SCHEMA, - ) -> t.Dict[str, t.Any]: - import outlines - - generator = outlines.generate.json(self.model, json_schema) - character = generator(prompt) - - return character diff --git a/examples/cerebrium/cerebrium.toml b/examples/cerebrium/cerebrium.toml deleted file mode 100644 index f7b9fb1e..00000000 --- a/examples/cerebrium/cerebrium.toml +++ /dev/null @@ -1,26 +0,0 @@ -[cerebrium.deployment] -name = "cerebrium" -python_version = "3.11" -cuda_version = "12" -include = "[./*, main.py, cerebrium.toml]" -exclude = "[.*]" -shell_commands = [] - -[cerebrium.hardware] -cpu = 2 -memory = 14.0 -gpu = "AMPERE A10" -gpu_count = 1 -provider = "aws" -region = "us-east-1" - -[cerebrium.scaling] -min_replicas = 0 -max_replicas = 5 -cooldown = 60 - -[cerebrium.dependencies.pip] -outline = "==0.0.37" -transformers = "==4.38.2" -datasets = "==2.18.0" -accelerate = "==0.27.2" diff --git a/examples/cerebrium/main.py b/examples/cerebrium/main.py deleted file mode 100644 index b61cdb6e..00000000 --- a/examples/cerebrium/main.py +++ /dev/null @@ -1,43 +0,0 @@ -import outlines - -model = outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.2") - -schema = { - "title": "Character", - "type": "object", - "properties": { - "name": {"title": "Name", "maxLength": 10, "type": "string"}, - "age": {"title": "Age", "type": "integer"}, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": {"title": "Strength", "type": "integer"}, - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string", - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string", - }, - }, -} - -generator = outlines.generate.json(model, schema) - - -def generate( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - character = generator( - f"[INST]Give me a character description. Describe {prompt}.[/INST]" - ) - - print(character) - return character diff --git a/examples/cfg.py b/examples/cfg.py deleted file mode 100644 index 99edf35f..00000000 --- a/examples/cfg.py +++ /dev/null @@ -1,89 +0,0 @@ -import outlines.generate as generate -import outlines.models as models - -nlamb_grammar = r""" - start: sentence - - sentence: noun verb noun -> simple - | noun verb "like" noun -> comparative - - noun: adj? NOUN - verb: VERB - adj: ADJ - - NOUN: "flies" | "bananas" | "fruit" - VERB: "like" | "flies" - ADJ: "fruit" - - %import common.WS - %ignore WS -""" - -calc_grammar = r""" - ?start: sum - | NAME "=" sum -> assign_var - - ?sum: product - | sum "+" product -> add - | sum "-" product -> sub - - ?product: atom - | product "*" atom -> mul - | product "/" atom -> div - - ?atom: NUMBER -> number - | "-" atom -> neg - | NAME -> var - | "(" sum ")" - - %import common.LETTER -> NAME - %import common.INT -> NUMBER - %import common.WS_INLINE - - %ignore WS_INLINE -""" - -dyck_grammar = r""" - start: s - s: /a+/ - | "(" s ")" - | "{" s "}" - | "[" s "]" -""" - -json_grammar = r""" - ?start: value - - ?value: object - | array - | string - | SIGNED_NUMBER -> number - | "true" -> true - | "false" -> false - | "null" -> null - - array : "[" [value ("," value)*] "]" - object : "{" [pair ("," pair)*] "}" - pair : string ":" value - - inner: /([^"]|\\\")+/ | - string : "\"" inner "\"" - - %import common.SIGNED_NUMBER - %import common.WS - - %ignore WS -""" - -model = models.transformers("hf-internal-testing/tiny-random-gpt2") -batch_size = 10 -for grammar in [nlamb_grammar, calc_grammar, dyck_grammar, json_grammar]: - generator = generate.cfg(model, grammar, max_tokens=model.model.config.n_positions) - sequences = generator([" "] * batch_size) - for seq in sequences: - try: - parse = generator.fsm.parser.parse(seq) - assert parse is not None - print("SUCCESS", seq) - except Exception: # will also fail if goes over max_tokens / context window - print("FAILURE", seq) diff --git a/examples/dating_profile.py b/examples/dating_profile.py deleted file mode 100644 index acc00126..00000000 --- a/examples/dating_profile.py +++ /dev/null @@ -1,176 +0,0 @@ -from dataclasses import dataclass -from enum import Enum - -import torch -import transformers -from pydantic import BaseModel, conlist - -import outlines -from outlines import models - - -class QuestionChoice(str, Enum): - A = "The key to my heart is" - B = "The first item on my bucket list is" - C = "Perks of dating me" - D = "Message me if you also love" - E = "People would describe me as" - F = "I can beat you in a game of" - - -@dataclass -class QuestionAnswer: - question: QuestionChoice - answer: str - - -class DatingProfile(BaseModel): - # It is possible put length constraints on these strings using constr- however, this appears to dramatically increase the generation time - # This may be resolved in the future with this PR: https://github.com/outlines-dev/outlines/pull/272 - bio: str - job: str - # Ignore mypy checks here because it still doesn't support conlist or constr: https://github.com/pydantic/pydantic/issues/975 - interests: conlist(str, min_length=1, max_length=5) # type: ignore - qna1: QuestionAnswer - qna2: QuestionAnswer - - -@dataclass -class Example: - description: str - profile: DatingProfile - - -@outlines.prompt -def dating_profile_prompt(description: str, examples: list[Example]): - """ - You are a world-renowned matchmaker who understands the modern dating market. Your job is to generate dating app profiles for male clients interested in women based on a provided description. The profiles should be authentic, show off their strengths, and maximize their likelihood of getting matches on dating apps. - Here are some examples of past clients that you have successfully created profiles for: - {% for example in examples %} - Description: - {{ example.description }} - Profile: - {{ example.profile }} - {% endfor %} - Here is the new client who you need to create a profile for: - Description: {{ description }} - Profile: - """ - - -samples: list[Example] = [ - Example( - description="I'm an author and former professional soccer player living in Seattle who publishes popular fiction books. A typical day for me starts by hanging out with my cat, drinking a coffee, and reading as much as I can in a few hours. Then, I'll prepare a quick smoothie before starting to write for a few hours, take a break with soccer or running a few miles, and finally meet friends for dinner at a new, hip restaurant in the evening. Sometimes we go axe-throwing afterwards, or play poker, or watch a comedy show, or visit a dive bar. On my vacations, I travel extensively to countries South America, Europe, and Asia, with the goal of visiting them all!", - profile=DatingProfile( - bio="Adventurer, dreamer, author, and soccer enthusiast. Life’s too short to waste time so I make the most of each day by exploring new places and playing with my friends on the pitch. What’s your favorite way to get out and have fun?", - job="Famous Soccer Player -> Famous Author", - interests=["Soccer", "Travel", "Friends", "Books", "Fluffy Animals"], - qna1=QuestionAnswer( - question=QuestionChoice.B, answer="swim in all seven oceans!" - ), - qna2=QuestionAnswer( - question=QuestionChoice.E, - answer="fun-loving, adventurous, and a little bit crazy", - ), - ), - ), - Example( - description="I run my company and build houses for a living. I'm a big fan of the outdoors and love to go hiking, camping, and fishing. I don't like video games, but do like to watch movies. My love language is home-cooked food, and I'm looking for someone who isn't afraid to get their hands dirty.", - profile=DatingProfile( - bio="If you're looking for a Montana man who loves to get outdoors and hunt, and who's in-tune with his masculinity then I'm your guy!", - job="House Construction Manager / Entrepreneur", - interests=["Hunting", "Hiking", "The outdoors", "Home-cooked food"], - qna1=QuestionAnswer(question=QuestionChoice.A, answer="food made at home"), - qna2=QuestionAnswer( - question=QuestionChoice.C, - answer="having a man in your life who can fix anything", - ), - ), - ), - Example( - description="I run my own Youtube channel with 10M subscribers. I love working with kids, and my audience skews pretty young too. In my free time, I play Fortnite and Roblox. I'm looking for someone who is also a gamer and likes to have fun. I'm learning Japanese in my free time as well as how to cook.", - profile=DatingProfile( - bio="Easy on the eyes (find me on Youtube!) and great with kids. What more do you need?", - job="Youtuber 10M+ subscribers", - interests=["Kids", "Gaming", "Japanese"], - qna1=QuestionAnswer(question=QuestionChoice.D, answer="anime and gaming!"), - qna2=QuestionAnswer(question=QuestionChoice.F, answer="Fortnite, gg ez"), - ), - ), -] - - -# Below requires ~13GB of GPU memory -# https://huggingface.co/mosaicml/mpt-7b-8k-instruct -# Motivation: Reasonably large model that fits on a single GPU and has been fine-tuned for a larger context window -config = transformers.AutoConfig.from_pretrained( - "mosaicml/mpt-7b-8k-instruct", trust_remote_code=True -) -config.init_device = "meta" -model = models.transformers( - model_name="mosaicml/mpt-7b-8k-instruct", - device="cuda", - model_kwargs={ - "config": config, - "trust_remote_code": True, - "torch_dtype": torch.bfloat16, - "device_map": {"": 0}, - }, -) - -new_description = "I'm a laid-back lawyer who spends a lot of his free-time gaming. I work in a corporate office, but ended up here after the start-up I cofounded got acquired, so still play ping pong with my cool coworkers every day. I have a bar at home where I make cocktails, which is great for entertaining friends. I secretly like to wear suits and get a new one tailored every few months. I also like weddings because I get to wear those suits, and it's a good excuse for a date. I watch the latest series because I'm paying, with my hard-earned money, for every streaming service." - -prompt = dating_profile_prompt(description=new_description, examples=samples) -profile = outlines.generate.json(model, DatingProfile)(prompt) # type: ignore -print(profile) - -# Sample generated profiles -""" -{ - "bio": "I'm an ambitious lawyer with a casual and fashionable style. I love games and sports, but my true passion is preparing refreshing cocktails at home and dressing to the nines at weddings. I'm currently looking for a woman to show a good time to and get a kiss on the opulent suit I just had made. Send resumΓƒ € to this inbox.", - "job": "Lawyer", - "interests": - [ - "Stylish guys", - "Gaming", - "Ping pong", - "Cocktails", - "Weddings" - ], - "qna1": - { - "question": "The first item on my bucket list is", - "answer": "be married and have a family." - }, - "qna2": - { - "question": "People would describe me as", - "answer": "charming, stylish, and funny." - } -} -""" - -""" -{ - "bio": "I’m a sexy lawyer with time on my hands. I love to game and play ping pong, but the real reason you should swipe to the right is because I look great in a suit. Who doesn’t love a man in a suit? Just saying. Send me a message if you think it’s time to take your dating life to the next level.", - "job": "Lawyer", - "interests": - [ - "Gaming", - "Ping Pong", - "Tailored Suits", - "Weddings", - "Streaming Services" - ], - "qna1": - { - "question": "The first item on my bucket list is", - "answer": "simulate space but stay alive for as long as possible" - }, - "qna2": - { - "question": "People would describe me as", - "answer": "easy-going, a little nerdy but with a mature essence" - } -} -""" diff --git a/examples/llamacpp_example.py b/examples/llamacpp_example.py deleted file mode 100644 index 22d0da3b..00000000 --- a/examples/llamacpp_example.py +++ /dev/null @@ -1,44 +0,0 @@ -from enum import Enum - -from pydantic import BaseModel, constr - -import outlines - - -class Weapon(str, Enum): - sword = "sword" - axe = "axe" - mace = "mace" - spear = "spear" - bow = "bow" - crossbow = "crossbow" - - -class Armor(str, Enum): - leather = "leather" - chainmail = "chainmail" - plate = "plate" - - -class Character(BaseModel): - name: constr(max_length=10) - age: int - armor: Armor - weapon: Weapon - strength: int - - -if __name__ == "__main__": - # curl -L -o mistral-7b-instruct-v0.2.Q5_K_M.gguf https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf - model = outlines.models.llamacpp("./mistral-7b-instruct-v0.2.Q5_K_M.gguf") - - # Construct structured sequence generator - generator = outlines.generate.json(model, Character) - - # Draw a sample - seed = 789005 - - prompt = "Instruct: You are a leading role play gamer. You have seen thousands of different characters and their attributes.\nPlease return a JSON object with common attributes of an RPG character. Give me a character description\nOutput:" - - sequence = generator(prompt, seed=seed, max_tokens=512) - print(sequence) diff --git a/examples/llamacpp_processor.py b/examples/llamacpp_processor.py deleted file mode 100644 index 909df38f..00000000 --- a/examples/llamacpp_processor.py +++ /dev/null @@ -1,50 +0,0 @@ -from enum import Enum - -from llama_cpp import Llama, LogitsProcessorList -from pydantic import BaseModel, constr - -from outlines.generate.processors import JSONLogitsProcessor -from outlines.models.llamacpp import LlamaCppTokenizer - - -class Weapon(str, Enum): - sword = "sword" - axe = "axe" - mace = "mace" - spear = "spear" - bow = "bow" - crossbow = "crossbow" - - -class Armor(str, Enum): - leather = "leather" - chainmail = "chainmail" - plate = "plate" - - -class Character(BaseModel): - name: constr(max_length=10) - age: int - armor: Armor - weapon: Weapon - strength: int - - -if __name__ == "__main__": - llama = Llama("./phi-2.Q4_K_M.gguf") - tokenizer = LlamaCppTokenizer(llama) - - prompt = "Instruct: You are a leading role play gamer. You have seen thousands of different characters and their attributes.\nPlease return a JSON object with common attributes of an RPG character. Give me a character description\nOutput:" - - logits_processor = JSONLogitsProcessor(Character, tokenizer) - - json_str = llama.create_completion( - prompt, - top_k=40, - top_p=0.95, - temperature=0.7, - max_tokens=100, - logits_processor=LogitsProcessorList([logits_processor]), - )["choices"][0]["text"] - - print(json_str) diff --git a/examples/math_generate_code.py b/examples/math_generate_code.py deleted file mode 100644 index 5bf16e86..00000000 --- a/examples/math_generate_code.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Example from https://dust.tt/spolu/a/d12ac33169""" -import outlines -import outlines.models as models - -examples = [ - {"question": "What is 37593 * 67?", "code": "37593 * 67"}, - { - "question": "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?", - "code": "(16-3-4)*2", - }, - { - "question": "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?", - "code": " 2 + 2/2", - }, -] - -question = "Carla is downloading a 200 GB file. She can download 2 GB/minute, but 40% of the way through the download, the download fails. Then Carla has to restart the download from the beginning. How load did it take her to download the file in minutes?" - - -@outlines.prompt -def answer_with_code_prompt(question, examples): - """ - {% for example in examples %} - QUESTION: {{example.question}} - CODE: {{example.code}} - - {% endfor %} - QUESTION: {{question}} - CODE:""" - - -def execute_code(code): - result = eval(code) - return result - - -prompt = answer_with_code_prompt(question, examples) -model = models.openai("gpt-3.5-turbo") -answer = outlines.generate.text(model)(prompt) -result = execute_code(answer) -print(f"It takes Carla {result:.0f} minutes to download the file.") diff --git a/examples/meta_prompting.py b/examples/meta_prompting.py deleted file mode 100644 index 8f9ba5ce..00000000 --- a/examples/meta_prompting.py +++ /dev/null @@ -1,168 +0,0 @@ -"""Meta-prompting examples. - -References ----------- - -.. [0] "Prompting is programming: A Query Language for Large Language Models" - https://arxiv.org/abs/2212.06094 -.. [1] "Prompt programming For Large Language Models: Beyond the Few-Shot Paradigm" - https://arxiv.org/abs/2102.07350. - -""" -import argparse - -import outlines -import outlines.models as models - - -def split_into_steps(question, model_name: str): - @outlines.prompt - def solve(question): - """{{question}} - Rephrase : : as a true or false statement, identify an Object, relationship and subject - """ - - model = models.openai(model_name) - generator = outlines.generate.text(model) - - prompt = solve(question) - answer = generator(prompt, 500) - prompt += ( - answer - + "\n what is the only option that displays the same type of relationship as : :?" - ) - answer = generator(prompt, 500) - completed = prompt + answer - - return completed - - -def fill_in_the_blanks(question, model_name: str): - @outlines.prompt - def determine_goal(question): - """{{question}} - - In order to solve this problem, we will analyze each of the options and determine - """ - - @outlines.prompt - def solve(memory): - """{{memory}}. Let's begin.""" - - model = models.openai(model_name) - generator = outlines.generate.text(model) - - prompt = determine_goal(question) - answer = generator(prompt, stop_at=["."]) - prompt = solve(prompt + answer) - answer = generator(prompt, max_tokens=500) - completed = prompt + answer - - return completed - - -def ask_an_expert(question, model_name: str): - @outlines.prompt - def find_expert(question): - """ - {{question}} - I entered my question into the Expert Generator \ - and waited. The Expert Generator will render a \ - simulation of an expert to answer my question. \ - The expert could be anyone, dead or alive, real \ - or fictional; the machine will find the person \ - most qualified to answer the question. For this \ - question in particular, the expert must be someone \ - who has thought a lot about the problem of \ - artificial intelligence and its alignment. \ - The Expert Generator beeped, indicating that it has \ - found the most qualified expert. The name displayed \ - on the screen: " - """ - - @outlines.prompt - def get_answer(question, expert, memory): - """ - {{memory}}". - I am ready to ask my question. - "{{expert}}" I say, - {{question}} - """ - - model = models.openai(model_name) - generator = outlines.generate.text(model) - - prompt = find_expert(question) - expert = generator(prompt, stop_at=['"']) - prompt = get_answer(question, expert, prompt + expert) - answer = generator(prompt, max_tokens=500) - completed = prompt + answer - - return completed - - -def ask_an_expert_simple(question, model_name: str): - @outlines.prompt - def find_expert(question): - """ - Q: {{question}} - A: A good person to answer this question would be - """ - - @outlines.prompt - def get_answer(expert, memory): - """ - {{memory}}. - - For instance, {{expert}} would answer - """ - - model = models.openai(model_name) - generator = outlines.generate.text(model) - - prompt = find_expert(question) - expert = generator(prompt, stop_at=["\n", "."]) - prompt = get_answer(expert, prompt + expert) - answer = generator(prompt, max_tokens=500) - completed = prompt + answer - - return completed - - -def run_example(model_fn, question, model_name): - completed = model_fn(question, model_name) - print("\n-----------------------") - print(f"{completed}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run the Meta Prompting examples") - parser.add_argument( - "--model", - type=str, - default="gpt-3.5-turbo-1106", - help="The Large Language Model to use to run the examples.", - ) - args = parser.parse_args() - - math_q = "f(x) = x*x. What is f(f(3))?" - sat_q = """ - -BRAGGART :: MODESTY -A) FLEDGLING : EXPERIENCE -B) EMBEZZLER : GREED -C) WALLFLOWER : TIMIDITY -D) INVALID : MALADY -E) CANDIDATE : AMBITION - - """ - alignment_q = "What should humankind do to ensure that artificial general intelligence is aligned?" - meaning_q = "What is the meaning of life?" - - run_example(split_into_steps, math_q, args.model) - run_example( - split_into_steps, sat_q.lower(), args.model - ) # gpt>3.5 usually gets this one right - run_example(fill_in_the_blanks, sat_q, args.model) - run_example(ask_an_expert, alignment_q, args.model) - run_example(ask_an_expert_simple, meaning_q, args.model) diff --git a/examples/modal_example.py b/examples/modal_example.py deleted file mode 100644 index 34d4ee60..00000000 --- a/examples/modal_example.py +++ /dev/null @@ -1,81 +0,0 @@ -import modal - -app = modal.App(name="outlines-app") - - -outlines_image = modal.Image.debian_slim(python_version="3.11").pip_install( - "outlines==0.0.37", - "transformers==4.38.2", - "datasets==2.18.0", - "accelerate==0.27.2", -) - - -def import_model(): - import outlines - - outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.2") - - -outlines_image = outlines_image.run_function(import_model) - - -schema = """{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}""" - - -@app.function(image=outlines_image, gpu=modal.gpu.A100(memory=80)) -def generate( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - import outlines - - model = outlines.models.transformers("mistralai/Mistral-7B-v0.1", device="cuda") - - generator = outlines.generate.json(model, schema) - character = generator( - f"[INST]Give me a character description. Describe {prompt}.[/INST]" - ) - - print(character) - - -@app.local_entrypoint() -def main( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - generate.remote(prompt) diff --git a/examples/parsing.py b/examples/parsing.py deleted file mode 100644 index a10da4eb..00000000 --- a/examples/parsing.py +++ /dev/null @@ -1,106 +0,0 @@ -"""An example illustrating parser-based masking.""" -import math -import time -from copy import copy - -import torch -from lark.indenter import DedentError -from lark.lexer import UnexpectedCharacters, UnexpectedToken -from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - LogitsProcessor, - LogitsProcessorList, - set_seed, -) - -from outlines.fsm.parsing import PartialLark, PartialPythonIndenter - -revision = None -checkpoint = "Salesforce/codegen-350M-mono" -device = "cuda" - -tokenizer = AutoTokenizer.from_pretrained(checkpoint) - -model = AutoModelForCausalLM.from_pretrained( - checkpoint, trust_remote_code=True, revision=revision -).to(device) - -parser = PartialLark.open_from_package( - "tests", - "partial_python.lark", - ["text"], - parser="lalr", - postlex=PartialPythonIndenter(), - start="file_input", -) - - -class ParserLogitsProcessor(LogitsProcessor): - """Bias invalid token scores according to a running parse state.""" - - def __init__(self, parser): - self.parser = parser - self.parser_state = parser.parse("") - self.states_stack = [self.parser_state] - self.token_seq = None - self.token_idx = 0 - - def __call__( - self, input_ids: torch.LongTensor, scores: torch.FloatTensor - ) -> torch.FloatTensor: - if self.token_seq is None: - self.token_seq = tokenizer.decode(input_ids[0]) - self.token_idx = len(input_ids[0]) - 1 - else: - self.token_idx += 1 - self.token_seq += tokenizer.decode(input_ids[0][self.token_idx]) - - # Process the last sampled token - lex_state = self.parser_state.lexer.state - lex_state.text = self.token_seq - - self.parser.parse_from_state(self.parser_state, is_end=False) - - print(f'parsed:"{self.token_seq}"') - - mask = torch.full_like(scores, -math.inf) - - # Determine which tokens in the vocabulary are valid next tokens - # given the parser state. - # - # TODO: This is a very naive and slow approach. It could be done in - # parallel, easily memoized/cached, etc., but there are a few other - # approaches to try first that will dramatically reduce the - # amount of work needed here. - t0 = time.perf_counter() - for test_token, token_id in tokenizer.vocab.items(): - ps = copy(self.parser_state) - ls = ps.lexer.state - ls.text = self.token_seq + tokenizer.convert_tokens_to_string([test_token]) - - try: - self.parser.parse_from_state(ps, is_end=False) - mask[0][token_id] = 0 - except (EOFError, UnexpectedToken, UnexpectedCharacters, DedentError): - pass - - print(f"next token masking duration: {time.perf_counter() - t0}") - - return scores + mask - - -set_seed(20399) - -input_text = "def " -inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) - -outputs = model.generate( - inputs, - max_length=100, - temperature=0.1, - logits_processor=LogitsProcessorList([ParserLogitsProcessor(parser)]), - renormalize_logits=True, -) - -print(tokenizer.decode(outputs[0])) diff --git a/examples/pick_odd_one_out.py b/examples/pick_odd_one_out.py deleted file mode 100644 index 6c4d4592..00000000 --- a/examples/pick_odd_one_out.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Chain-of-thought prompting for Odd one out classification. - -Example taken from the LQML library [1]_. - -References ----------- -.. [1] Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). - Prompting Is Programming: A Query Language For Large Language Models. - arXiv preprint arXiv:2212.06094. - -""" -import outlines -import outlines.models as models - - -@outlines.prompt -def build_ooo_prompt(options): - """ - Pick the odd word out: skirt, dress, pen, jacket. - skirt is clothing, dress is clothing, pen is an object, jacket is clothing. - So the odd one is pen. - - Pick the odd word out: Spain, France, German, England, Singapore. - Spain is a country, France is a country, German is a language, ... - So the odd one is German. - - Pick the odd word out: {{ options | join(", ") }}. - - """ - - -options = ["sea", "mountains", "plains", "sock"] - -model = models.openai("gpt-3.5-turbo") -gen_text = outlines.generate.text(model) -gen_choice = outlines.generate.choice(model, options) - -prompt = build_ooo_prompt(options) -reasoning = gen_text(prompt, stop_at=["Pick the odd word", "So the odd one"]) -prompt += reasoning -result = gen_choice(prompt) -prompt += result -print(result) diff --git a/examples/react.py b/examples/react.py deleted file mode 100644 index ccd74dbb..00000000 --- a/examples/react.py +++ /dev/null @@ -1,76 +0,0 @@ -"""ReAct - -This example was inspired by the LQML library [1]_. The ReAct framework was -first developed in [2]_ and augments Chain-of-Thought prompting with the ability -for the model to query external sources. - -References ----------- -.. [1] Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). Prompting Is Programming: A Query Language For Large Language Models. arXiv preprint arXiv:2212.06094. -.. [2] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629. - -""" -import requests # type: ignore - -import outlines -import outlines.models as models - - -@outlines.prompt -def build_reAct_prompt(question): - """What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into? - Tho 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado ... - Act 2: Search 'Colorado orogeny' - Obs 2: The Colorado orogeny was an episode of mountain building (an orogeny) ... - Tho 3: It does not mention the eastern sector. So I need to look up eastern sector. - ... - Tho 4: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft. - Act 5: Finish '1,800 to 7,000 ft' - {{ question }} - """ - - -@outlines.prompt -def add_mode(i, mode, result, prompt): - """{{ prompt }} - {{ mode }} {{ i }}: {{ result }} - """ - - -def search_wikipedia(query: str): - url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&titles={query}&origin=*" - response = requests.get(url) - page = response.json()["query"]["pages"] - return ".".join(list(page.values())[0]["extract"].split(".")[:2]) - - -prompt = build_reAct_prompt("Where is Apple Computers headquarted? ") -model = models.openai("gpt-3.5-turbo") -complete = outlines.generate.text(model) - -for i in range(1, 10): - mode = complete.generate_choice(prompt, choices=["Tho", "Act"], max_tokens=128) - prompt = add_mode(i, mode, "", prompt) - - if mode == "Tho": - thought = complete(prompt, stop_at="\n", max_tokens=128) - prompt += f"{thought}" - elif mode == "Act": - action = complete.generate_choice( - prompt, choices=["Search", "Finish"], max_tokens=128 - ) - prompt += f"{action} '" - - subject = complete( - prompt, stop_at=["'"], max_tokens=128 - ) # Apple Computers headquartered - subject = " ".join(subject.split()[:2]) - prompt += f"{subject}'" - - if action == "Search": - result = search_wikipedia(subject) - prompt = add_mode(i, "Obs", result, prompt) - else: - break - -print(prompt) diff --git a/examples/sampling.ipynb b/examples/sampling.ipynb deleted file mode 100644 index bcbcca1e..00000000 --- a/examples/sampling.ipynb +++ /dev/null @@ -1,401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "62129e1a-e9de-454e-a714-35ccbcf0b518", - "metadata": {}, - "outputs": [], - "source": [ - "import functools as ft\n", - "import re\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", - "\n", - "import outlines.models as models\n", - "import outlines.text as text" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b20aafe8-b7a3-4df4-878f-b48b74e131df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: OPENAI_API_KEY=# you key here\n" - ] - } - ], - "source": [ - "%env OPENAI_API_KEY= # you key here" - ] - }, - { - "cell_type": "markdown", - "id": "2a3514d6-d5d7-46e9-9b69-1251d337e094", - "metadata": {}, - "source": [ - "In this example we will look at completion results for questions from the GSM8K dataset, using few-shots prompts with 5 examples. We first use `outlines.text.prompt` to build the few-shot prompt. `outlines.text.prompt` is a decorator around \"prompt functions\" which contain the prompt template in its docstring. Outlines uses the Jinja2 templating engine to render the prompt when the function is called with the variables' values; it thus allows you to build complex prompts very easily." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ffe8bb11-6b51-4fe7-bfb3-c62556a60db8", - "metadata": {}, - "outputs": [], - "source": [ - "examples = [\n", - " {\n", - " \"question\": \"There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\",\n", - " \"answer\": \"We start with 15 trees. Later we have 21 trees. The difference must be the number of trees they planted. So, they must have planted 21 - 15 = 6 trees. The answer is 6.\",\n", - " },\n", - " {\n", - " \"question\": \"If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\",\n", - " \"answer\": \"There are 3 cars in the parking lot already. 2 more arrive. Now there are 3 + 2 = 5 cars. The answer is 5.\",\n", - " },\n", - " {\n", - " \"question\": \"Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\",\n", - " \"answer\": \"Leah had 32 chocolates and Leah’s sister had 42. That means there were originally 32 + 42 = 74 chocolates. 35 have been eaten. So in total they still have 74 - 35 = 39 chocolates. The answer is 39.\",\n", - " },\n", - " {\n", - " \"question\": \"Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\",\n", - " \"answer\": \"Jason had 20 lollipops. Since he only has 12 now, he must have given the rest to Denny. The number of lollipops he has given to Denny must have been 20 - 12 = 8 lollipops. The answer is 8.\",\n", - " },\n", - " {\n", - " \"question\": \"Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\",\n", - " \"answer\": \"He has 5 toys. He got 2 from mom, so after that he has 5 + 2 = 7 toys. Then he got 2 more from dad, so in total he has 7 + 2 = 9 toys. The answer is 9.\",\n", - " },\n", - " {\n", - " \"question\": \"There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\",\n", - " \"answer\": \"There are 4 days from monday to thursday. 5 computers were added each day. That means in total 4 * 5 = 20 computers were added. There were 9 computers in the beginning, so now there are 9 + 20 = 29 computers. The answer is 29.\",\n", - " },\n", - " {\n", - " \"question\": \"Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\",\n", - " \"answer\": \"Michael initially had 58 balls. He lost 23 on Tuesday, so after that he has 58 - 23 = 35 balls. On Wednesday he lost 2 more so now he has 35 - 2 = 33 balls. The answer is 33.\",\n", - " },\n", - " {\n", - " \"question\": \"Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\",\n", - " \"answer\": \"She bought 5 bagels for $3 each. This means she spent 5\",\n", - " },\n", - "]\n", - "\n", - "@text.prompt\n", - "def few_shot_prompt(question, examples):\n", - " \"\"\"\n", - " {% for example in examples %}\n", - " Q: {{ example.question }}\n", - " A: {{ example.answer }}\n", - " {% endfor %}\n", - " Q: {{ question }}\n", - " A:\n", - " \"\"\"\n", - "\n", - "# Prompt functions can be partially evaluated like any other function\n", - "gsm8k_prompt = ft.partial(few_shot_prompt, examples=examples)" - ] - }, - { - "cell_type": "markdown", - "id": "1eae0ec8-89f0-43fc-b055-6fcd64cbc03b", - "metadata": {}, - "source": [ - "## When `text-davinci-003` is uncertain" - ] - }, - { - "cell_type": "markdown", - "id": "a273ed78-e813-467e-85f3-16d7f283ba87", - "metadata": {}, - "source": [ - "Let us now sample 20 completions with the `text-davinci-003` model. Outlines is sampling first, and allows to draw several samples with both OpenAI and `transformers` models easily:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "beff960d-6833-4f24-af09-5b65886a9549", - "metadata": {}, - "outputs": [], - "source": [ - "model = models.text_completion.openai(\"text-davinci-003\", max_tokens=128)\n", - "\n", - "question = \"When I was 6 my sister was half my age. Now I’m 70 how old is my sister?\"\n", - "prompt = gsm8k_prompt(question)\n", - "answers = model(prompt, samples=20)" - ] - }, - { - "cell_type": "markdown", - "id": "1a895b6d-d4d4-40f9-9156-24ba7e21cc08", - "metadata": {}, - "source": [ - "The correct answer to this question is 67. Let us now count the different answers, and take a look at their distribution. Let us first define a few utility functions:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f1c83d1f-a478-4509-890e-b84a2e0d8846", - "metadata": {}, - "outputs": [], - "source": [ - "def count_digits(answers):\n", - " digits = []\n", - " for answer in answers:\n", - " try:\n", - " match = re.findall(r\"\\d+\", answer)[-1]\n", - " if match is not None:\n", - " digit = int(match)\n", - " digits.append(digit)\n", - " except AttributeError:\n", - " print(f\"Could not parse the completion: '{answer}'\")\n", - " \n", - " unique_digits, counts = np.unique(digits, return_counts=True)\n", - " return {d: c for d, c in zip(unique_digits, counts)}\n", - "\n", - "def plot_counts(counts):\n", - " fig = plt.figure(figsize=(12,8))\n", - " ax = fig.add_subplot(111)\n", - " \n", - " bar = ax.bar(counts.keys(), counts.values())\n", - " ax.spines[[\"right\", \"top\", \"left\"]].set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - " \n", - " for rect in bar:\n", - " height = rect.get_height()\n", - " plt.text(rect.get_x() + rect.get_width() / 2.0, height, f'{height:.0f}', ha='center', va='bottom', fontsize=20)\n", - " \n", - " ax.set_xticks(list(counts.keys()))\n", - " ax.set_xlabel(\"Answer\")\n", - "\n", - "def entropy(counts):\n", - " counts = np.array(list(counts.values()))\n", - " probs = counts / np.sum(counts)\n", - " log_probs = np.log(probs)\n", - " return - np.sum(probs * log_probs)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "88668e09-bcd6-4a6a-83a5-838189b910eb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "counts = count_digits(answers)\n", - "plot_counts(counts)" - ] - }, - { - "cell_type": "markdown", - "id": "661a1135-ac2d-4a49-a786-d04a7ba68b48", - "metadata": {}, - "source": [ - "We see that there is an important variabilty in the answers given by `text-davinci-003`. Depending on the number of samples taken, even self-consistency sampling may lead to the wrong result here." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "30ea0dfe-6c15-44f0-881c-88b325542b44", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Entropy: 1.5741030017371853\n" - ] - } - ], - "source": [ - "print(f\"Entropy: {entropy(counts)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0b15b230-b667-4c9c-8a5d-366dd61de9b7", - "metadata": {}, - "source": [ - "## `text-davinci-003` on an easier question" - ] - }, - { - "cell_type": "markdown", - "id": "beae30f0-4168-4a80-90d4-d26a4f476469", - "metadata": {}, - "source": [ - "Let us now look at the results for an arguably easier question:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7e106b94-2dfd-4a75-b4d9-b1ad693418a7", - "metadata": {}, - "outputs": [], - "source": [ - "question = \"John buys 2 pairs of shoes for each of his 3 children. They cost $60 each. How much did he pay?\"\n", - "prompt = gsm8k_prompt(question)\n", - "answers = model(question, samples=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dd46fb2b-08ef-4003-8d03-ea0f39c865c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Entropy: 0.1985152433458726\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAHgCAYAAACCbCTDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQuklEQVR4nO3db6ye9V3H8c+PPxGxIjMVghSLkCYgOtmUzSCd6xii8mCIyxBCTBUTWbBmIzEhWeIAUfegSYkha6JIQsmSRaoclnUbmeCyQbrBoi2GrtSlEIZKxnQOCmPB9vLBuaH8aUui4dyfwuuVnPQ+13Wd3N/TR+/++ruva0zTFAAAaHTEvAcAAICDEasAANQSqwAA1BKrAADUEqsAANQSqwAA1Drqdc67rxUAAEthHOiglVUAAGqJVQAAaolVAIA3sc2bN2fdunVZvXp1jjvuuIwxcsUVVxz0+meeeSYf+9jHcsYZZ+SYY47J2972tlx44YW55557lnDq/V5vzyoAAIexG2+8Mdu3b8+yZcuyYsWK7Ny586DXfve73815552XHTt25KyzzspVV12VPXv25K677sr73//+3HLLLbnyyiuXcHorqwAAb2obNmzIrl278vTTT2fjxo2HvPa6667Ljh07cskll2Tbtm256aabcsstt+Thhx/OKaecknXr1uWJJ55YoskXiVUAgDexNWvWZNWqVRnjgB+2f4U777wzSXLDDTfkqKP2/wf8CSeckGuuuSbf//73c+utt75hsx6IWAUAIEny5JNPJklOO+2015x78dhS710VqwAAJEmWL1+eJHn00Udfc2737t1JkkceeWRJZxKrAAAkSS666KIkycc//vHs3bv3peNPPfVUNmzYkGTxQ1hLyd0AAABIsrhX9e67787mzZtz9tln5/zzz8+zzz6bu+66KyeffHIef/zxHHHE0q51WlkFACBJctJJJ+XBBx/M1VdfnWeeeSaf/OQns2XLllx66aW54447kix+2GopWVkFAOAlJ554Ym6++ebcfPPNrzh+7733JknOOeecJZ3HyioAAK9r06ZNSZLLL798Sd9XrAIAkCTZt29f9uzZ85rjt99+ezZt2pRzzz03F1988ZLOZBsAAMCb2MLCQhYWFpLsv4/q1q1bs3bt2iSLt6tav359kuS5557LiSeemAsuuCCnn356jjjiiNx///3ZunVrzjzzzNxxxx1L/gGrMU3Toc4f8iQAAN2uu+66XH/99Qc9v3Llyjz22GNJkhdeeCFXXXVV7rvvvpceq7pq1ap86EMfykc+8pEce+yxb+SoB3zEllgFAKDBAWPVnlUAAGqJVQAAaolVAABqiVUAAHLqtVvmPcIBiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABqiVUAAGrVxOrmzZuzbt26rF69Oscdd1zGGLniiivmPRYAAHN01LwHeNGNN96Y7du3Z9myZVmxYkV27tw575EAAJizmpXVDRs2ZNeuXXn66aezcePGeY8DAECBmpXVNWvWzHsEAADK1KysAgDAq4lVAABqiVUAAGqJVQAAaolVAABqiVUAAGqJVQAAaolVAABq1TwUYGFhIQsLC0mSJ598MkmydevWrF27NkmyfPnyrF+/fk7TAQAwDzWxum3bttx2222vOLZ79+7s3r07SbJy5UqxCgDwFjOmaTrU+UOeBADgzeHUa7fksU9cNM8RxoEO2rMKAEAtsQoAQC2xCgBArdpYPfXaLfMeAQCAOauNVQAAEKsAANQSqwAA1BKrAADUEqsAANQSqwAA1BKrAADUEqsAANQSqwAA1BKrAADUEqsAANQSqwAA1BKrAADUEqsAANQSqwAA1BKrAADUEqsAANQSqwAA1BKrAADUEqsAANQSqwAA1BrTNB385BhfSLJ86cZ5heVJvjOn9wYAeCuaZ399Z5qmX3v1wUPG6jyNMb4+TdMvznsOAIC3isb+sg0AAIBaYhUAgFrNsfpX8x4AAOAtpq6/avesAgBA88oqAABvcXWxOsY4ZozxwBhj+xjj4THG9fOeCQDgcHewxhqL/myMsWuM8Y0xxh+97PhfjjG+OcZ4aIzxznnMfdQ83vR1/CDJ+6Zp2jPGODrJfWOMz0/T9NV5DwYAcBg7YGMlOTPJKUnOmKZp3xjjhNn1v55k1ezr3Uk2zv5cUnWxOi1uot0z+/bo2ZeNtQAA/w+HaKwPJ7l8mqZ9s+u+PbvmA0k2zX7uq2OM48cYJ03T9B9LOXfdNoAkGWMcOcbYluTbSb44TdPX5jwSAMBh7yCNdXqSS8cYXx9jfH6MsWp2+clJvvWyH39idmxJVcbqNE17p2k6O8mKJO8aY/zsnEcCADjsHaSxfijJ87MnV/11klvnOOJrVMbqi6Zp+u8k/5jkNc+JBQDg/+ZVjfVEkr+fnbozydtnr/8ti3tZX7RidmxJ1cXqGOMnxhjHz17/cJILkuyc61AAAIe5QzTWQpI1s8t+Jcmu2evPJPmd2V0BfinJ95Z6v2pS+AGrJCcluW2McWQWY/pvp2n67JxnAgA43B2wscYY9yX51Bjjo1n8ANbvz67/XJLfSPLNJM8l+d05zOwJVgAA9KrbBgAAAC8SqwAA1BKrAADUEqsAANQSqwAA1BKrAK8yxrh4jDGNMc6Y9ywAb3ViFeC1Lkty3+zPuRhjNN4HG2DJiVWAlxljLEtyXpIrk/z27Nh7xxhfGmNsHmPsHGN8aowxZuc+McbYMcZ4aIyxfoxx5Bjj0dkTX44fY+wdY7xndu2Xxxirxhg/Msa4dYzxwBjjn8cYH5idXzvG+MwY494k98znbwCgi3+5A7zSB5J8YZqmXWOM/xxj/MLs+DuSnJXk35Pcn+SXxxjfSPKbSc6YpmkaYxw/TdPeMcYjSX4myU8n+ackq8cYX0tyyjRN/zrG+PMk907T9HuzRx8+MMb4h9n7vDPJ26dp+q+l+oUBmllZBXily5J8evb609m/FeCBaZqemKZpX5JtSU5N8r0kzyf5mzHGJVl8HGGSfCXJe2Zff5HFldpzkjw4O/+rSa4dY2xL8qUkxyT5qdm5LwpVgP2srALMjDF+PMn7kvzcGGNKcmSSKcmWJD942aV7kxw1TdP/jDHeleT8JB9M8oezn/9ykg8n+ckkf5Lkj5O8N4sRmyQjyW9N0/TIq97/3UmefUN+OYDDlJVVgP0+mOT2aZpWTtN06jRNpyR5NMnqA10829/6Y9M0fS7JR5P8/OzUA0nOTbJvmqbns7gS+wdZjNgkuTvJupfte33HG/T7ABz2xCrAfpclufNVx/4uB78rwI8m+ewY46Es3j3gmiSZpukHSb6V5Kuz674yu/ZfZt//aZKjkzw0xnh49j0ABzCmaZr3DAAAcEBWVgEAqCVWAQCoJVYBAKglVgEAqCVWAQCoJVYBAKglVgEAqCVWAQCo9b+lzUDoz9UHogAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "counts = count_digits(answers)\n", - "plot_counts(counts)\n", - "print(f\"Entropy: {entropy(counts)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "cf4cacdf-a31d-43bd-8517-eec9f656eee4", - "metadata": {}, - "source": [ - "The entropy of the results is much lower, we say that the model is more \"certain\" of its answers. " - ] - }, - { - "cell_type": "markdown", - "id": "22f31872-aab7-4a68-b9f2-d335a4f1a875", - "metadata": {}, - "source": [ - "## How `gpt-4` compares to `text-davinci-003`\n", - "\n", - "Let us now look at how GPT4 fares on the original question:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2d5ab5b8-eca5-47f5-a35c-5f3865e35755", - "metadata": {}, - "outputs": [], - "source": [ - "model = models.text_completion.openai(\"gpt-4\", max_tokens=128)\n", - "\n", - "question = \"When I was 6 my sister was half my age. Now I’m 70 how old is my sister?\"\n", - "prompt = gsm8k_prompt(question)\n", - "answers = model(prompt, samples=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d316a5f7-cebc-4b09-9b1b-aee219b2f088", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Entropy: -0.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "counts = count_digits(answers)\n", - "plot_counts(counts)\n", - "print(f\"Entropy: {entropy(counts)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "2f6c8a22-fdf5-4f30-865c-8e11927b1b7c", - "metadata": {}, - "source": [ - "GPT4 returns the correct answer with certainty." - ] - }, - { - "cell_type": "markdown", - "id": "50d4a55e-86df-46ab-8b38-302c79bc8add", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "When generating text completions with a language model we typically look at one output sample, trying to find the \"right\" answer. However, doing so we obscure the diversity of answers that these language models can produce. Assuming the diversity of answers reflects these models' \"uncertainty\", we can use measures such as the entropy of the answers' distribution to evaluate the quality of the answer.\n", - "\n", - "Which result should we be choosing once we have different samples? There is no definite answer to this question. The [self-consistency method](https://arxiv.org/abs/2203.11171) consists in choosing the result based on a majority vote. We think this choice is arbitrary and that choosing the correct answer is a [decision theory](https://en.wikipedia.org/wiki/Decision_theory) problem, which can only be solved by specifying a loss function that is adapted to the experiment's context; the majority vote being a particular case with a 0-1 loss." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/self_consistency.py b/examples/self_consistency.py deleted file mode 100644 index e7468d5f..00000000 --- a/examples/self_consistency.py +++ /dev/null @@ -1,82 +0,0 @@ -import re - -import numpy as np - -import outlines -import outlines.models as models - -examples = [ - { - "question": "There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?", - "answer": "We start with 15 trees. Later we have 21 trees. The difference must be the number of trees they planted. So, they must have planted 21 - 15 = 6 trees. The answer is 6.", - }, - { - "question": "If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?", - "answer": "There are 3 cars in the parking lot already. 2 more arrive. Now there are 3 + 2 = 5 cars. The answer is 5.", - }, - { - "question": "Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?", - "answer": "Leah had 32 chocolates and Leah’s sister had 42. That means there were originally 32 + 42 = 74 chocolates. 35 have been eaten. So in total they still have 74 - 35 = 39 chocolates. The answer is 39.", - }, - { - "question": "Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?", - "answer": "Jason had 20 lollipops. Since he only has 12 now, he must have given the rest to Denny. The number of lollipops he has given to Denny must have been 20 - 12 = 8 lollipops. The answer is 8.", - }, - { - "question": "Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?", - "answer": "He has 5 toys. He got 2 from mom, so after that he has 5 + 2 = 7 toys. Then he got 2 more from dad, so in total he has 7 + 2 = 9 toys. The answer is 9.", - }, - { - "question": "There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?", - "answer": "There are 4 days from monday to thursday. 5 computers were added each day. That means in total 4 * 5 = 20 computers were added. There were 9 computers in the beginning, so now there are 9 + 20 = 29 computers. The answer is 29.", - }, - { - "question": "Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?", - "answer": "Michael initially had 58 balls. He lost 23 on Tuesday, so after that he has 58 - 23 = 35 balls. On Wednesday he lost 2 more so now he has 35 - 2 = 33 balls. The answer is 33.", - }, - { - "question": "Olivia has $23. She bought five bagels for $3 each. How much money does she have left?", - "answer": "She bought 5 bagels for $3 each. This means she spent 5", - }, -] - -question = "When I was 6 my sister was half my age. Now I’m 70 how old is my sister?" - - -@outlines.prompt -def few_shots(question, examples): - """ - {% for example in examples %} - Q: {{ example.question }} - A: {{ example.answer }} - {% endfor %} - Q: {{ question }} - A: - """ - - -model = models.openai("gpt-3.5-turbo") -generator = outlines.generate.text(model) -prompt = few_shots(question, examples) -answers = generator(prompt, samples=10) - -digits = [] -for answer in answers: - try: - match = re.findall(r"\d+", answer)[-1] - if match is not None: - digit = int(match) - digits.append(digit) - except AttributeError: - print(f"Could not parse the completion: '{answer}'") - -unique_digits, counts = np.unique(digits, return_counts=True) -results = {d: c for d, c in zip(unique_digits, counts)} -print(results) - -max_count = max(results.values()) -answer_value = [key for key, value in results.items() if value == max_count][0] -total_count = sum(results.values()) -print( - f"The most likely answer is {answer_value} ({max_count/total_count*100}% consensus)" -) diff --git a/examples/simulation_based_inference.ipynb b/examples/simulation_based_inference.ipynb deleted file mode 100644 index e6b99958..00000000 --- a/examples/simulation_based_inference.ipynb +++ /dev/null @@ -1,370 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e7c7d0bb-8d45-4139-a584-02c7196db92b", - "metadata": {}, - "source": [ - "# Find the best few-shot examples using simulation-based inference" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "831a76f5-c569-4174-adab-fb0245877367", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import random\n", - "import requests\n", - "import re\n", - "\n", - "import outlines.models as models\n", - "import outlines.text as text\n", - "\n", - "random.seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ec604edc-c8b6-4088-bf17-b77ae57d05a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: OPENAI_API_KEY=# your key here\n" - ] - } - ], - "source": [ - "%env OPENAI_API_KEY = # your key here" - ] - }, - { - "cell_type": "markdown", - "id": "aabb4db6-fd94-4c42-ab7f-97c3de45b2cc", - "metadata": {}, - "source": [ - "In this example we will use GPT 3.5 to solve problems from the GSM-8K dataset. The state-of-the-art performance on this dataset is obtained using few-shot prompting with 5 examples. However, it is not clear how one should select these examples. Here, we will use **simulation-based inference** to try to infer which examples we should be using to get the best out of the model's abilities to solve the problem.\n", - "\n", - "Let's start with downloading the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "367f5f89-8e5d-4381-b9eb-78c60bc50f86", - "metadata": {}, - "outputs": [], - "source": [ - "result = requests.get(\"https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl\")\n", - "lines = result.iter_lines()" - ] - }, - { - "cell_type": "markdown", - "id": "ef0f7aa9-d528-41e9-8a9d-4497f01f0692", - "metadata": {}, - "source": [ - "We now divide the train set in two sets:\n", - "- 20 problems from which we are going to sample 5 examples at random for every inference;\n", - "- 500 problems which we are going to use to perform inference." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0667c4a8-cebe-4796-bbc9-575ee9498717", - "metadata": {}, - "outputs": [], - "source": [ - "example_set = []\n", - "for _ in range(10):\n", - " line = json.loads(next(lines))\n", - " answer = re.findall(r\"\\d+\", line[\"answer\"])[-1]\n", - " example_set.append({\"question\": line[\"question\"], \"answer\": answer})\n", - "\n", - "train_set = []\n", - "for _ in range(500):\n", - " line = json.loads(next(lines))\n", - " answer = re.findall(r\"\\d+\", line[\"answer\"])[-1]\n", - " train_set.append({\"question\": line[\"question\"], \"answer\": answer})" - ] - }, - { - "cell_type": "markdown", - "id": "4b52b470-d818-495a-a6e3-e50a1deff13c", - "metadata": {}, - "source": [ - "Now let's define the prompt, the model, and the sampling loop. The sampling loop consists in choosing 5 examples at random, sampling 20 model answers; if the answer is correct we keep the example ids as samples, otherwise continue:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9fbebaa9-f05e-4c6b-8875-73a08273bbb5", - "metadata": {}, - "outputs": [], - "source": [ - "@text.prompt\n", - "def few_shots(question, examples):\n", - " \"\"\"\n", - " {% for example in examples %}\n", - " Q: {{ example.question }}\n", - " A: {{ example.answer }}\n", - " {% endfor %}\n", - " Q: {{ question }}\n", - " A:\n", - " \"\"\"\n", - "\n", - "model = models.text_completion.openai(\"text-davinci-003\", max_tokens=128)\n", - "\n", - "# TODO: This could largely benefit from vectorization in #52\n", - "def one_train_example(problem, example_set):\n", - " example_ids = random.choices(range(0, len(example_set)), k=5)\n", - " examples = [example_set[i] for i in example_ids]\n", - " prompt = few_shots(problem[\"question\"], examples)\n", - " answers_raw = model(prompt, samples=20)\n", - "\n", - " samples = []\n", - " for answer_raw in answers_raw:\n", - " try:\n", - " answer = re.findall(r\"\\d+\", answer_raw)[-1]\n", - " if answer == problem[\"answer\"]:\n", - " samples += example_ids\n", - " else:\n", - " continue\n", - " except IndexError:\n", - " pass\n", - "\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1dae1ef2-c9e0-4c98-8686-7fbc2ff55e56", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9efc9d077af24a2eb5ea3c05fe63f298", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/500 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as plt\n", - "\n", - "example_ids, counts = np.unique(samples, return_counts=True)\n", - "\n", - "fig = plt.figure(figsize=(12,8))\n", - "ax = fig.add_subplot(111)\n", - "ax.bar(example_ids, counts)\n", - "\n", - "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", - "\n", - "ax.set_xticks(range(10))\n", - "ax.set_xlabel(\"Example #\")\n", - "ax.set_ylabel(\"Counts\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cde37e5b-377e-4872-af40-674d680bd2da", - "metadata": {}, - "source": [ - "Looking at the distribution, our best guess for which examples we should use for benchmarking on the test set would be 0, 1, 2, 6 and 9. This method can be trivially extended to other workflows that use few-shot examples to query LLMs. Of course, simulation-based inference extends beyong choosing the \"best\" prompt, and could for instance be useful to select the structure of chains of LLMs and tools as well." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bddda20b-234a-4d30-b40a-90708fbaba23", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',\n", - " 'answer': '72'}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_set[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fb186bf9-62b7-485f-a8ce-401f551a9e57", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'question': 'Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?',\n", - " 'answer': '10'}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_set[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ae427bb2-e3f4-4a96-a508-e8011a0fc553", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'question': 'Betty is saving money for a new wallet which costs $100. Betty has only half of the money she needs. Her parents decided to give her $15 for that purpose, and her grandparents twice as much as her parents. How much more money does Betty need to buy the wallet?',\n", - " 'answer': '5'}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_set[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fe43ae0f-c18f-4b74-b639-8481472edf4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'question': 'Albert is wondering how much pizza he can eat in one day. He buys 2 large pizzas and 2 small pizzas. A large pizza has 16 slices and a small pizza has 8 slices. If he eats it all, how many pieces does he eat that day?',\n", - " 'answer': '48'}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_set[6]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "19d9d936-d0f0-4927-990c-76dbbfa95b47", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'question': 'Tina makes $18.00 an hour. If she works more than 8 hours per shift, she is eligible for overtime, which is paid by your hourly wage + 1/2 your hourly wage. If she works 10 hours every day for 5 days, how much money does she make?',\n", - " 'answer': '990'}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_set[9]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/transformers_integration.py b/examples/transformers_integration.py deleted file mode 100644 index 16e2b8ec..00000000 --- a/examples/transformers_integration.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Example of integrating `outlines` with `transformers`.""" - -from pydantic import BaseModel -from transformers import pipeline - -from outlines.integrations.transformers import JSONPrefixAllowedTokens - - -class Person(BaseModel): - first_name: str - surname: str - - -pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1") -prefix_allowed_tokens_fn = JSONPrefixAllowedTokens( - schema=Person, tokenizer_or_pipe=pipe, whitespace_pattern=r" ?" -) -results = pipe( - ["He is Tom Jones", "She saw Linda Smith"], - return_full_text=False, - do_sample=False, - max_new_tokens=50, - prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, -) -print(results) diff --git a/examples/vllm_integration.py b/examples/vllm_integration.py deleted file mode 100644 index 5f833d19..00000000 --- a/examples/vllm_integration.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Example of integrating `outlines` with `vllm`.""" - -import vllm -from pydantic import BaseModel - -from outlines.integrations.vllm import JSONLogitsProcessor - - -class Person(BaseModel): - first_name: str - surname: str - - -llm = vllm.LLM(model="mistralai/Mistral-7B-v0.1", max_model_len=512) -logits_processor = JSONLogitsProcessor(schema=Person, llm=llm, whitespace_pattern=r" ?") -result = llm.generate( - ["He is Tom Jones", "She saw Linda Smith"], - sampling_params=vllm.SamplingParams( - temperature=0.0, - max_tokens=50, - logits_processors=[logits_processor], - ), -) -print(result) From 1a0e1879a9d225d5d71d1f66e8ad84d621fceed6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 14:03:22 +0200 Subject: [PATCH 07/20] Remove models other than `transformers` --- outlines/__init__.py | 1 - outlines/generate/fsm.py | 20 - outlines/generate/regex.py | 27 -- outlines/generate/text.py | 13 - outlines/models/__init__.py | 7 +- outlines/models/exllamav2.py | 232 ---------- outlines/models/llamacpp.py | 407 ------------------ outlines/models/mlxlm.py | 247 ----------- outlines/models/transformers_vision.py | 138 ------ outlines/models/vllm.py | 226 ---------- pyproject.toml | 13 - tests/generate/test_generate.py | 41 +- tests/generate/test_integration_llamacpp.py | 380 ---------------- .../test_integration_transformers_vision.py | 116 ----- tests/generate/test_integration_vllm.py | 239 ---------- 15 files changed, 5 insertions(+), 2102 deletions(-) delete mode 100644 outlines/models/exllamav2.py delete mode 100644 outlines/models/llamacpp.py delete mode 100644 outlines/models/mlxlm.py delete mode 100644 outlines/models/transformers_vision.py delete mode 100644 outlines/models/vllm.py delete mode 100644 tests/generate/test_integration_llamacpp.py delete mode 100644 tests/generate/test_integration_transformers_vision.py delete mode 100644 tests/generate/test_integration_vllm.py diff --git a/outlines/__init__.py b/outlines/__init__.py index b050521f..04f4da55 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -1,7 +1,6 @@ """Outlines is a Generative Model Programming Framework.""" import outlines.generate import outlines.models -import outlines.processors from outlines.caching import clear_cache, disable_cache, get_cache __all__ = [ diff --git a/outlines/generate/fsm.py b/outlines/generate/fsm.py index 03fe512b..2a128d96 100644 --- a/outlines/generate/fsm.py +++ b/outlines/generate/fsm.py @@ -8,7 +8,6 @@ SequenceGeneratorAdapter, VisionSequenceGeneratorAdapter, ) -from outlines.models import ExLlamaV2Model, TransformersVision from outlines.samplers import Sampler, multinomial @@ -21,22 +20,3 @@ def fsm( fsm = RegexGuide.from_interegular_fsm(fsm, model.tokenizer) logits_processor = FSMLogitsProcessor(tokenizer=model.tokenizer, fsm=fsm) return SequenceGeneratorAdapter(model, logits_processor, sampler) - - -@fsm.register(TransformersVision) -def fsm_vision(model, fsm: interegular.fsm.FSM, sampler: Sampler = multinomial()): - from outlines.processors import FSMLogitsProcessor - - fsm = RegexGuide.from_interegular_fsm(fsm, model.tokenizer) - logits_processor = FSMLogitsProcessor(tokenizer=model.tokenizer, fsm=fsm) - return VisionSequenceGeneratorAdapter(model, logits_processor, sampler) - - -@fsm.register(ExLlamaV2Model) -def fsm_exllamav2( - model, fsm: interegular.fsm.FSM, sampler: Sampler = multinomial() -) -> SequenceGenerator: - fsm = RegexGuide.from_interegular_fsm(fsm, model.tokenizer) - device = model.device - generator = SequenceGenerator(fsm, model, sampler, device) - return generator diff --git a/outlines/generate/regex.py b/outlines/generate/regex.py index c731cb9d..78321ec8 100644 --- a/outlines/generate/regex.py +++ b/outlines/generate/regex.py @@ -6,7 +6,6 @@ SequenceGeneratorAdapter, VisionSequenceGeneratorAdapter, ) -from outlines.models import ExLlamaV2Model, TransformersVision from outlines.samplers import Sampler, multinomial @@ -35,29 +34,3 @@ def regex(model, regex_str: str, sampler: Sampler = multinomial()): logits_processor = RegexLogitsProcessor(regex_str, tokenizer=model.tokenizer) return SequenceGeneratorAdapter(model, logits_processor, sampler) - - -@regex.register(TransformersVision) -def regex_vision( - model, - regex_str: str, - sampler: Sampler = multinomial(), -): - from outlines.processors import RegexLogitsProcessor - - logits_processor = RegexLogitsProcessor(regex_str, tokenizer=model.tokenizer) - return VisionSequenceGeneratorAdapter(model, logits_processor, sampler) - - -@regex.register(ExLlamaV2Model) -def regex_exllamav2( - model, - regex_str: str, - sampler: Sampler = multinomial(), -) -> SequenceGenerator: - fsm = RegexGuide(regex_str, model.tokenizer) - - device = model.device - generator = SequenceGenerator(fsm, model, sampler, device) - - return generator diff --git a/outlines/generate/text.py b/outlines/generate/text.py index 907402e4..e5e00fe7 100644 --- a/outlines/generate/text.py +++ b/outlines/generate/text.py @@ -6,7 +6,6 @@ SequenceGeneratorAdapter, VisionSequenceGeneratorAdapter, ) -from outlines.models import ExLlamaV2Model, TransformersVision from outlines.samplers import Sampler, multinomial @@ -34,15 +33,3 @@ def text(model, sampler: Sampler = multinomial()) -> SequenceGeneratorAdapter: """ return SequenceGeneratorAdapter(model, None, sampler) - - -@text.register(ExLlamaV2Model) -def text_exllamav2(model, sampler: Sampler = multinomial()) -> SequenceGenerator: - fsm = StopAtEOSGuide(model.tokenizer) - device = model.device - return SequenceGenerator(fsm, model, sampler, device) - - -@text.register(TransformersVision) -def text_vision(model, sampler: Sampler = multinomial()): - return VisionSequenceGeneratorAdapter(model, None, sampler) diff --git a/outlines/models/__init__.py b/outlines/models/__init__.py index 28b5af3e..c6277f62 100644 --- a/outlines/models/__init__.py +++ b/outlines/models/__init__.py @@ -8,11 +8,6 @@ from typing import Union -from .exllamav2 import ExLlamaV2Model, exl2 -from .llamacpp import LlamaCpp, llamacpp -from .mlxlm import MLXLM, mlxlm from .transformers import Transformers, TransformerTokenizer, mamba, transformers -from .transformers_vision import TransformersVision, transformers_vision -from .vllm import VLLM, vllm -LogitsGenerator = Union[Transformers, LlamaCpp, ExLlamaV2Model, MLXLM, VLLM] +LogitsGenerator = Union[Transformers] diff --git a/outlines/models/exllamav2.py b/outlines/models/exllamav2.py deleted file mode 100644 index 0ec6ef03..00000000 --- a/outlines/models/exllamav2.py +++ /dev/null @@ -1,232 +0,0 @@ -import os -from typing import TYPE_CHECKING, Optional - -if TYPE_CHECKING: - from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Lora - from transformers import PreTrainedTokenizer - import torch - -from .transformers import TransformerTokenizer - - -class ExLlamaV2Model: - """Represents a `exl2` model.""" - - def __init__( - self, - model: "ExLlamaV2", - tokenizer: "PreTrainedTokenizer", - device, - cache: "ExLlamaV2Cache", - lora: Optional["ExLlamaV2Lora"] = None, - ): - self.device = device - self.model = model - self.tokenizer = TransformerTokenizer(tokenizer) - self.cache = cache - self.past_seq = None - self.lora = lora - - def forward(self, input_ids: "torch.LongTensor", *_): - """Compute a forward pass through the exl2 model.""" - import torch - - # Caching with past_seq - reset = True - seq_tensor = input_ids[0] - - if self.past_seq is not None: - min_length = min(self.past_seq.shape[0], seq_tensor.shape[0]) - indices = torch.nonzero( - ~torch.eq(self.past_seq[:min_length], seq_tensor[:min_length]) - ) - if len(indices) > 0: - longest_prefix = indices[0].item() - else: - longest_prefix = min_length - - if longest_prefix > 0: - reset = False - self.cache.current_seq_len = longest_prefix - if seq_tensor.shape[0] - longest_prefix > 1: - self.model.forward( - seq_tensor[longest_prefix:-1].view(1, -1), - self.cache, - preprocess_only=True, - loras=[self.lora], - ) - elif seq_tensor.shape[0] == longest_prefix: - self.cache.current_seq_len -= 1 - - if reset: - self.cache.current_seq_len = 0 - if seq_tensor.shape[0] > 1: - self.model.forward( - seq_tensor[:-1].view(1, -1), - self.cache, - preprocess_only=True, - loras=[self.lora], - ) - - self.past_seq = seq_tensor - - return self.model.forward( - seq_tensor[-1:].view(1, -1), self.cache, loras=[self.lora] - ) - - def __call__(self, input_ids: "torch.LongTensor", *_) -> "torch.FloatTensor": - logits = self.forward(input_ids) - next_token_logits = logits[..., -1, :] - - return next_token_logits, None - - def update_lora(self, lora_path: Optional[str] = None): - """ - Update and apply the LoRA to the model. - - Args: - lora_path (Optional[str]): The path to the LoRA directory. If None, the LoRA will be unloaded. - """ - try: - from exllamav2 import ExLlamaV2Lora - except ImportError: - raise ImportError( - "The `exllamav2` library needs to be installed in order to use `exllamav2` models." - ) - if lora_path is None: - if self.lora is not None: - print(" -- Unloading LoRA...") - self.lora = None - else: - self.lora = ExLlamaV2Lora.from_directory(self.model, lora_path) - print(" -- Loading LoRA...") - - -def exl2( - model_path: str, - device: str, - max_seq_len: Optional[int] = None, - scale_pos_emb: Optional[float] = None, - scale_alpha_value: Optional[float] = None, - no_flash_attn: Optional[bool] = None, - num_experts_per_token: Optional[int] = None, - cache_8bit: bool = False, - cache_q4: bool = False, - tokenizer_kwargs: dict = {}, - gpu_split: Optional[str] = None, - low_mem: Optional[bool] = None, - verbose: Optional[bool] = None, -) -> ExLlamaV2Model: - """ - Load an ExLlamaV2 model. - - Parameters - ---------- - model_path (str) - Path to the model directory. - device (str) - Device to load the model on. Pass in 'cuda' for GPU or 'cpu' for CPU - max_seq_len (Optional[int], optional) - Maximum sequence length. Defaults to None. - scale_pos_emb (Optional[float], optional) - Scale factor for positional embeddings. Defaults to None. - scale_alpha_value (Optional[float], optional) - Scale alpha value. Defaults to None. - no_flash_attn (Optional[bool], optional) - Disable flash attention. Defaults to None. - num_experts_per_token (Optional[int], optional) - Number of experts per token. Defaults to None. - cache_8bit (bool, optional) - Use 8-bit cache. Defaults to False. - cache_q4 (bool, optional) - Use Q4 cache. Defaults to False. - tokenizer_kwargs (dict, optional) - Additional keyword arguments for the tokenizer. Defaults to {}. - gpu_split (str) - \"auto\", or VRAM allocation per GPU in GB. Auto will use exllama's autosplit feature - low_mem (bool, optional) - Enable VRAM optimizations, potentially trading off speed - verbose (bool, optional) - Enable if you want debugging statements - - Returns - ------- - An `ExLlamaV2Model` instance. - - Raises - ------ - `ImportError` if the `exllamav2` library is not installed. - - """ - try: - from exllamav2 import ( - ExLlamaV2, - ExLlamaV2Cache, - ExLlamaV2Cache_8bit, - ExLlamaV2Cache_Q4, - ExLlamaV2Config, - ) - from transformers import AutoTokenizer - except ImportError: - raise ImportError( - "The `exllamav2`, `transformers` and `torch` libraries needs to be installed in order to use `exllamav2` models." - ) - - # Load tokenizer - if not verbose: - print(" -- Loading tokenizer...") - tokenizer_kwargs.setdefault("padding_side", "left") - tokenizer = AutoTokenizer.from_pretrained(model_path, **tokenizer_kwargs) - # tokenizer = TransformerTokenizer(model_path, **tokenizer_kwargs) - - # Check fasttensors for config - if os.name != "nt": - use_fasttensors = True - else: - use_fasttensors = False - - # Create config - config = ExLlamaV2Config() - config.model_dir = model_path - config.fasttensors = use_fasttensors - config.prepare() - - # Set config options - if max_seq_len is not None: - config.max_seq_len = max_seq_len - if scale_pos_emb is not None: - config.scale_pos_emb = scale_pos_emb - if scale_alpha_value is not None: - config.scale_alpha_value = scale_alpha_value - if no_flash_attn is not None: - config.no_flash_attn = no_flash_attn - if num_experts_per_token is not None: - config.num_experts_per_token = num_experts_per_token - if low_mem: - config.set_low_mem() - - # Prepare the model from the config - model = ExLlamaV2(config) - - # Create cache - if cache_8bit: - cache = ExLlamaV2Cache_8bit(model, lazy=not model.loaded) - elif cache_q4: - cache = ExLlamaV2Cache_Q4(model, lazy=not model.loaded) - else: - cache = ExLlamaV2Cache(model, lazy=not model.loaded) - - # Load the model - split = None - if gpu_split and gpu_split != "auto": - split = [float(alloc) for alloc in gpu_split.split(",")] - if not verbose: - print(" -- Loading model...") - model.load(split) - - # Autoload if no GPU split was provided - if not model.loaded: - print(" -- Loading model...") - model.load_autosplit(cache) - - return ExLlamaV2Model(model, tokenizer, device, cache) diff --git a/outlines/models/llamacpp.py b/outlines/models/llamacpp.py deleted file mode 100644 index 904b193c..00000000 --- a/outlines/models/llamacpp.py +++ /dev/null @@ -1,407 +0,0 @@ -import dataclasses -import pickle -import warnings -from typing import ( - TYPE_CHECKING, - Dict, - Iterator, - List, - Optional, - Set, - Tuple, - TypedDict, - Union, -) - -from typing_extensions import Unpack - -from outlines.generate.api import GenerationParameters, SamplingParameters -from outlines.models.tokenizer import Tokenizer - -if TYPE_CHECKING: - from llama_cpp import Llama, LogitsProcessorList - - -class LlamaCppTokenizer(Tokenizer): - def __init__(self, model: "Llama"): - self.eos_token_id = model.token_eos() - self.eos_token = model.tokenizer().decode([self.eos_token_id]) - self.pad_token_id = self.eos_token_id - self.special_tokens: Set[str] = set() - - self.vocabulary: Dict[str, int] = dict() - - self.tokenizer = model.tokenizer() - - # TODO: Remove when https://github.com/ggerganov/llama.cpp/pull/5613 is resolved - self._hf_tokenizer = None - try: - self.vocabulary = model.tokenizer_.hf_tokenizer.get_vocab() - self._hf_tokenizer = model.tokenizer_.hf_tokenizer - except AttributeError: - # ### - for t in range(model.n_vocab()): - token_piece = model.tokenizer().decode([t]) - self.vocabulary[token_piece] = t - - # ensure stable ordering of vocabulary - self.vocabulary = { - tok: tok_id - for tok, tok_id in sorted(self.vocabulary.items(), key=lambda x: x[1]) - } - - self._hash = None - - def decode(self, token_ids: List[int]) -> List[str]: - decoded_bytes = self.tokenizer.detokenize(token_ids) - return [decoded_bytes.decode("utf-8", errors="ignore")] - - def encode( - self, prompt: Union[str, List[str]], add_bos: bool = True, special: bool = True - ) -> Tuple[List[int], List[int]]: - if isinstance(prompt, list): - raise NotImplementedError( - "llama-cpp-python tokenizer doesn't support batch tokenization" - ) - token_ids = self.tokenizer.tokenize( - prompt.encode("utf-8", errors="ignore"), add_bos=add_bos, special=special - ) - # generate attention mask, missing from llama-cpp-python - attention_mask = [ - 1 if token_id != self.pad_token_id else 0 for token_id in token_ids - ] - return token_ids, attention_mask - - def convert_token_to_string(self, token: str) -> str: - if self._hf_tokenizer is not None: - from transformers.file_utils import SPIECE_UNDERLINE - - token_str = self._hf_tokenizer.convert_tokens_to_string([token]) - if token.startswith(SPIECE_UNDERLINE) or token == "<0x20>": - token_str = " " + token_str - return token_str - else: - return token - - def __eq__(self, other): - if not isinstance(other, LlamaCppTokenizer): - return False - return self.__getstate__() == other.__getstate__() - - def __hash__(self): - if self._hash is None: - self._hash = hash(pickle.dumps(self)) - return self._hash - - def __getstate__(self): - """Create a stable representation for outlines.caching""" - return ( - self.vocabulary, - self.eos_token_id, - self.eos_token, - self.pad_token_id, - sorted(self.special_tokens), - ) - - def __setstate__(self, state): - raise NotImplementedError("Cannot load a pickled llamacpp tokenizer") - - -class LlamaCppParams(TypedDict, total=False): - suffix: Optional[str] - temperature: float - top_p: float - min_p: float - typical_p: float - seed: int - max_tokens: int - logits_processor: "LogitsProcessorList" - stop: Optional[Union[str, List[str]]] - frequence_penalty: float - presence_penalty: float - repeat_penalty: float - top_k: int - tfs_z: float - mirostat_mode: int - mirostat_tau: float - mirostat_eta: float - stream: bool - - -class LlamaCpp: - """Represents a model provided by the `llama-cpp-python` library. - - We wrap models from model providing libraries in order to give all of - them the same interface in Outlines and allow users to easily switch - between providers. This class wraps the `llama_cpp.Llama` class from the - `llama-cpp-python` library. - - """ - - def __init__(self, model: "Llama"): - self.model = model - - @property - def tokenizer(self): - return LlamaCppTokenizer(self.model) - - def prepare_generation_parameters( - self, - generation_parameters: GenerationParameters, - sampling_parameters: SamplingParameters, - structure_logits_processor, - **llama_cpp_params: Unpack[LlamaCppParams], - ): - """Prepare the generation parameters. - - `llama-cpp-python` uses different default values - - """ - from llama_cpp import LogitsProcessorList - - max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) - - # We update `llama_cpp_params` with the values the user passed to the - # generator. - if "stop" not in llama_cpp_params: - llama_cpp_params["stop"] = stop_at - if "seed" not in llama_cpp_params: - llama_cpp_params["seed"] = seed - - # Somehow `llama-cpp-python` generates `max_tokens + 1` tokens - if "max_tokens" not in llama_cpp_params: - if max_tokens is None: - llama_cpp_params["max_tokens"] = -1 # indicates unlimited tokens - else: - llama_cpp_params["max_tokens"] = max_tokens - 1 - else: - llama_cpp_params["max_tokens"] = llama_cpp_params["max_tokens"] - 1 - - sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( - sampling_parameters - ) - - # We update the `llama_cpp_params` with the sampling values that - # were specified by the user via the `Sampler` class, unless they - # are also specified in `llama_cpp_params`. We also disable other - # sampling methods that are enabled by default and reset the temperature - # value. - # - # See https://github.com/ggerganov/llama.cpp/blob/e11a8999b5690f810c2c99c14347f0834e68c524/common/sampling.h#L22 - # for the default values in `llama.cpp` and indications to disable the sampling modes. - # Mirostat sampling, tail-free sampling and all penalties are disabled by default. - # - # See https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ - # for default values in `llama-cpp-python` - if sampler == "beam_search": - raise NotImplementedError( - "The `llama_cpp_python` library does not support Beam Search." - ) - if num_samples != 1: - raise NotImplementedError( - "The `llama_cpp_python` library does not allow to take several samples." - ) - if "top_p" not in llama_cpp_params: - if top_p is not None: - llama_cpp_params["top_p"] = top_p - else: - llama_cpp_params["top_p"] = 1.0 - - if "min_p" not in llama_cpp_params: - llama_cpp_params["min_p"] = 0.0 - - if "top_k" not in llama_cpp_params: - if top_k is not None: - llama_cpp_params["top_k"] = top_k - else: - llama_cpp_params["top_k"] = -1 - - if "temperature" not in llama_cpp_params: - if temperature is not None: - llama_cpp_params["temperature"] = temperature - else: - llama_cpp_params["temperature"] = 1.0 - - if "repeat_penalty" not in llama_cpp_params: - llama_cpp_params["repeat_penalty"] = 1.0 - - # The choice to stream or not should happen via the high-level API - llama_cpp_params["stream"] = False - - if structure_logits_processor is not None: - if "logits_processor" in llama_cpp_params: - llama_cpp_params["logits_processor"].append(structure_logits_processor) - else: - llama_cpp_params["logits_processor"] = LogitsProcessorList( - [structure_logits_processor] - ) - - return llama_cpp_params - - def generate( - self, - prompts: Union[str, List[str]], - generation_parameters: GenerationParameters, - structure_logits_processor, - sampling_parameters: SamplingParameters, - **llama_cpp_params: Unpack[LlamaCppParams], - ) -> str: - """Generate text using `llama-cpp-python`. - - Arguments - --------- - prompts - A prompt or list of prompts. - generation_parameters - An instance of `GenerationParameters` that contains the prompt, - the maximum number of tokens, stop sequences and seed. All the - arguments to `SequenceGeneratorAdapter`'s `__cal__` method. - logits_processor - The logits processor to use when generating text. - sampling_parameters - An instance of `SamplingParameters`, a dataclass that contains - the name of the sampler to use and related parameters as available - in Outlines. - llama_cpp_params - Keyword arguments that can be passed to - `llama_cpp_python.Llama.__call__`. The values in `llama_cpp_params` - supersede the values of the parameters in `generation_parameters` and - `sampling_parameters`. See the `llama_cpp_python` documentation for - a list of possible values: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ - - Returns - ------- - The generated text. - - """ - if not isinstance(prompts, str): - raise NotImplementedError( - "The `llama-cpp-python` library does not support batch inference." - ) - - llama_cpp_params = self.prepare_generation_parameters( - generation_parameters, - sampling_parameters, - structure_logits_processor, - **llama_cpp_params, - ) - completion = self.model(prompts, **llama_cpp_params) - result = completion["choices"][0]["text"] - - self.model.reset() - - return result - - def stream( - self, - prompts: Union[str, List[str]], - generation_parameters: GenerationParameters, - structure_logits_processor, - sampling_parameters: SamplingParameters, - **llama_cpp_params: Unpack[LlamaCppParams], - ) -> Iterator[str]: - """Stream text using `llama-cpp-python`. - - Arguments - --------- - prompts - A prompt or list of prompts. - generation_parameters - An instance of `GenerationParameters` that contains the prompt, - the maximum number of tokens, stop sequences and seed. All the - arguments to `SequenceGeneratorAdapter`'s `__cal__` method. - logits_processor - The logits processor to use when generating text. - sampling_parameters - An instance of `SamplingParameters`, a dataclass that contains - the name of the sampler to use and related parameters as available - in Outlines. - llama_cpp_params - Keyword arguments that can be passed to - `llama_cpp_python.Llama.__call__`. The values in `llama_cpp_params` - supersede the values of the parameters in `generation_parameters` and - `sampling_parameters`. See the `llama_cpp_python` documentation for - a list of possible values: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ - - Returns - ------- - A generator that return strings. - - """ - - if not isinstance(prompts, str): - raise NotImplementedError( - "The `llama-cpp-python` library does not support batch inference." - ) - - llama_cpp_params = self.prepare_generation_parameters( - generation_parameters, - sampling_parameters, - structure_logits_processor, - **llama_cpp_params, - ) - llama_cpp_params["stream"] = True - generator = self.model(prompts, **llama_cpp_params) - - def token_generator() -> Iterator[str]: - while True: - try: - result = next(generator) - yield result["choices"][0]["text"] - except StopIteration: - self.model.reset() - return - - return token_generator() - - def load_lora(self, adapter_path: str): - if self.model._model.apply_lora_from_file( - adapter_path, - 1.0, - ): - raise RuntimeError(f"Failed to apply LoRA from lora path: {adapter_path}") - - -def llamacpp( - repo_id: str, filename: Optional[str] = None, **llamacpp_model_params -) -> LlamaCpp: - """Load a model from the `llama-cpp-python` library. - - We use the `Llama.from_pretrained` classmethod that downloads models - directly from the HuggingFace hub, instead of asking users to specify - a path to the downloaded model. One can still load a local model - by initializing `llama_cpp.Llama` directly. - - Arguments - --------- - repo_id - The name of the model repository. - filename: - A filename of glob pattern to match the model file in the repo. - llama_cpp_model_params - Llama-specific model parameters. See the `llama-cpp-python` documentation - for the full list: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__ - - """ - from llama_cpp import Llama - - # Default to using the model's full context length - if "n_ctx" not in llamacpp_model_params: - llamacpp_model_params["n_ctx"] = 0 - - if "verbose" not in llamacpp_model_params: - llamacpp_model_params["verbose"] = False - - # TODO: Remove when https://github.com/ggerganov/llama.cpp/pull/5613 is resolved - if "tokenizer" not in llamacpp_model_params: - warnings.warn( - "The pre-tokenizer in `llama.cpp` handles unicode improperly " - + "(https://github.com/ggerganov/llama.cpp/pull/5613)\n" - + "Outlines may raise a `RuntimeError` when building the regex index.\n" - + "To circumvent this error when using `models.llamacpp()` you may pass the argument" - + "`tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained()`\n" - ) - - model = Llama.from_pretrained(repo_id, filename, **llamacpp_model_params) - - return LlamaCpp(model) diff --git a/outlines/models/mlxlm.py b/outlines/models/mlxlm.py deleted file mode 100644 index 6e63ef5b..00000000 --- a/outlines/models/mlxlm.py +++ /dev/null @@ -1,247 +0,0 @@ -import dataclasses -from typing import TYPE_CHECKING, Generator, Iterator, List, Optional, Tuple, Union - -from .transformers import TransformerTokenizer - -if TYPE_CHECKING: - import mlx.core as mx - import mlx.nn as nn - from transformers import PreTrainedTokenizer - - from outlines.generate.api import GenerationParameters, SamplingParameters - from outlines.processors import OutlinesLogitsProcessor - - -class MLXLM: - """ - Represents an `mlx_lm` model - """ - - def __init__( - self, - model: "nn.Module", - tokenizer: "PreTrainedTokenizer", - ): - self.model = model - self.mlx_tokenizer = tokenizer # returns mlx tensors, used for encode() - self.tokenizer = TransformerTokenizer( - tokenizer._tokenizer - ) # _tokenizer is HF Tokenizer - - def generate( - self, - prompts: Union[str, List[str]], - generation_parameters: "GenerationParameters", - logits_processor, - sampling_parameters: "SamplingParameters", - ) -> str: - streamer = self.stream( - prompts, generation_parameters, logits_processor, sampling_parameters - ) - return "".join(list(streamer)) - - def stream( - self, - prompts: Union[str, List[str]], - generation_parameters: "GenerationParameters", - logits_processor, - sampling_parameters: "SamplingParameters", - ) -> Iterator[str]: - """Generate text using `mlx_lm`. - - Arguments - --------- - prompts - A prompt or list of prompts. - generation_parameters - An instance of `GenerationParameters` that contains the prompt, - the maximum number of tokens, stop sequences and seed. All the - arguments to `SequenceGeneratorAdapter`'s `__cal__` method. - logits_processor - The logits processor to use when generating text. - sampling_parameters - An instance of `SamplingParameters`, a dataclass that contains - the name of the sampler to use and related parameters as available - in Outlines. - Returns - ------- - The generated text. - """ - import mlx.core as mx - - max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) - sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( - sampling_parameters - ) - if max_tokens is None: - max_tokens = int(1e9) - - if not isinstance(prompts, str): - raise NotImplementedError( - "The `mlx-lm` library does not support batch inference." - ) - if sampler == "beam_search": - raise NotImplementedError( - "The `mlx-lm` library does not support Beam Search." - ) - if num_samples != 1: - raise NotImplementedError( - "The `mlx-lm` library does not allow to take several samples." - ) - if top_k is not None: - raise NotImplementedError("The `mlx-lm` library does not support top_k.") - if seed is not None: - raise NotImplementedError("The `mlx-lm` library does not support seed.") - if stop_at is not None: - raise NotImplementedError("The `mlx-lm` library does not support stop_at.") - - generate_kwargs = { - "temp": temperature, - "top_p": top_p, - "sampler": sampler, - "logits_processor": logits_processor, - } - - # Adapted from - # https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L267 - prompt_tokens = mx.array(self.mlx_tokenizer.encode(prompts)) - - detokenizer = self.mlx_tokenizer.detokenizer - detokenizer.reset() - - for (token, prob), n in zip( - self.generate_step(prompt_tokens, **generate_kwargs), - range(max_tokens), - ): - if token == self.tokenizer.eos_token_id: - break - detokenizer.add_token(token) - yield detokenizer.last_segment - - detokenizer.finalize() - yield detokenizer.last_segment - - def generate_step( - self, - prompt: "mx.array", - temp: Optional[float], - top_p: Optional[float], - sampler: str, - logits_processor: "OutlinesLogitsProcessor", - ) -> Generator[Tuple[int, float], None, None]: - """ - Adapted from - https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L129 - - A generator producing token ids based on the given prompt from the model. - - Args: - prompt (mx.array): The input prompt. - temp (float): The temperature for sampling, if 0 the argmax is used. - Default: ``0``. - top_p (float, optional): Nulceus sampling, higher means model considers - more less likely words. - sampler (str): The sampler string defined by SequenceGeneratorAdapter - logits_processor (OutlinesLogitsProcessor): Augment logits before sampling. - """ - import mlx.core as mx - import mlx_lm - - temperature: float = temp or 1.0 - - def sample(logits: "mx.array") -> Tuple["mx.array", float]: - softmax_logits = mx.softmax(logits) - - if temperature == 0.0 or sampler == "greedy": - token = mx.argmax(logits, axis=-1) - elif sampler == "multinomial": - if top_p is not None and top_p > 0 and top_p < 1.0: - token = mlx_lm.sample_utils.top_p_sampling( - logits, top_p, temperature - ) - else: - token = mx.random.categorical(logits * (1 / temperature)) - else: - raise ValueError(f"Invalid mlx-lm sampler: `{sampler}`") - - prob = softmax_logits[0, token] - return token, prob - - kv_heads = ( - [self.model.n_kv_heads] * len(self.model.layers) - if isinstance(self.model.n_kv_heads, int) - else self.model.n_kv_heads - ) - cache = [mlx_lm.models.base.KVCache(self.model.head_dim, n) for n in kv_heads] - - # kv cache contains processed input IDs, we pass the unprocessed inputs and cache to model() - unprocessed_input_ids = prompt - generated_ids: List[int] = [] - - while True: - logits = self.model(unprocessed_input_ids[None], cache=cache) - logits = logits[:, -1, :] - - if logits_processor is not None: - # convert to logits_processor 1d expectation, apply, then convert back - logits_1d = logits.reshape(-1) - logits_1d = logits_processor(generated_ids, logits_1d) - logits = logits_1d.reshape(1, -1) - - new_token_single, prob = sample(logits) - new_token = new_token_single.item() - yield new_token, prob - - generated_ids.append(new_token) - unprocessed_input_ids = new_token_single - - -def mlxlm( - model_name: str, - tokenizer_config: dict = {}, - model_config: dict = {}, - adapter_path: Optional[str] = None, - lazy: bool = False, -): - """Instantiate a model from the `mlx_lm` library and its tokenizer. - - Signature adapted from - https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L422 - - Parameters - ---------- - Args: - path_or_hf_repo (Path): The path or the huggingface repository to load the model from. - tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer. - Defaults to an empty dictionary. - model_config(dict, optional): Configuration parameters specifically for the model. - Defaults to an empty dictionary. - adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers - to the model. Default: ``None``. - lazy (bool): If False eval the model parameters to make sure they are - loaded in memory before returning, otherwise they will be loaded - when needed. Default: ``False`` - - Returns - ------- - A `MLXLM` model instance. - - """ - try: - import mlx.core as mx - import mlx_lm - except ImportError: - raise ImportError( - "The `mlx_lm` library needs to be installed in order to use `mlx_lm` models." - ) - if not mx.metal.is_available(): - raise RuntimeError("You cannot use `mlx_lm` without Apple Silicon (Metal)") - - model, tokenizer = mlx_lm.load( - model_name, - tokenizer_config=tokenizer_config, - model_config=model_config, - adapter_path=adapter_path, - lazy=lazy, - ) - return MLXLM(model, tokenizer) diff --git a/outlines/models/transformers_vision.py b/outlines/models/transformers_vision.py deleted file mode 100644 index 9709c26a..00000000 --- a/outlines/models/transformers_vision.py +++ /dev/null @@ -1,138 +0,0 @@ -from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union - -from outlines.generate.api import GenerationParameters, SamplingParameters -from outlines.models import Transformers - -if TYPE_CHECKING: - from outlines.processors import OutlinesLogitsProcessor - - -class TransformersVision(Transformers): - def __init__(self, model, tokenizer, processor): - super().__init__(model, tokenizer) - self.processor = processor - - def generate( # type: ignore - self, - prompts: Union[str, List[str]], - media: Union[List[Any], List[List[Any]]], - generation_parameters: GenerationParameters, - logits_processor: Optional["OutlinesLogitsProcessor"], - sampling_parameters: SamplingParameters, - ) -> Union[str, List[str], List[List[str]]]: - """Generate text using `transformers`. - - Arguments - --------- - prompts - A prompt or list of prompts. - media - A List[PIL.Image] or List[List[PIL.Image]] - generation_parameters - An instance of `GenerationParameters` that contains the prompt, - the maximum number of tokens, stop sequences and seed. All the - arguments to `SequenceGeneratorAdapter`'s `__cal__` method. - logits_processor - The logits processor to use when generating text. - sampling_parameters - An instance of `SamplingParameters`, a dataclass that contains - the name of the sampler to use and related parameters as available - in Outlines. - - Returns - ------- - The generated text - """ - inputs = self.processor(prompts, media, padding=True, return_tensors="pt").to( - self.model.device - ) - - generation_kwargs = self._get_generation_kwargs( - prompts, - generation_parameters, - logits_processor, - sampling_parameters, - ) - generated_ids = self._generate_output_seq(prompts, inputs, **generation_kwargs) - - # if single str input and single sample per input, convert to a 1D output - if isinstance(prompts, str): - # Should always be true until NotImplementedError above is fixed - generated_ids = generated_ids.squeeze(0) - - return self._decode_generation(generated_ids) - - def stream( # type: ignore - self, - prompts: Union[str, List[str]], - media: Union[Any, List[Any]], # TODO: docstring - generation_parameters: GenerationParameters, - logits_processor: Optional["OutlinesLogitsProcessor"], - sampling_parameters: SamplingParameters, - ) -> Iterator[Union[str, List[str]]]: - raise NotImplementedError - - -def transformers_vision( - model_name: str, - model_class, - device: Optional[str] = None, - model_kwargs: dict = {}, - processor_kwargs: dict = {}, - tokenizer_class=None, - processor_class=None, -): - """Instantiate a model from the `transformers` library and its tokenizer. - - Parameters - ---------- - model_name - The name of the model as listed on Hugging Face's model page. - model_class - The `PreTrainedModel` class from transformers to use in initializing the vision model from `model_name`. - https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel - device - The device(s) on which the model should be loaded. This overrides - the `device_map` entry in `model_kwargs` when provided. - model_kwargs - A dictionary that contains the keyword arguments to pass to the - `from_pretrained` method when loading the model. - processor_kwargs - A dictionary that contains the keyword arguments to pass to the - `from_pretrained` method when loading the processor. - - Returns - ------- - A `TransformersModel` model instance. - - """ - if processor_class is None or tokenizer_class is None: - try: - from transformers import AutoProcessor, AutoTokenizer - except ImportError: - raise ImportError( - "The `transformers` library needs to be installed in order to use `transformers` models." - ) - if processor_class is None: - processor_class = AutoProcessor - if tokenizer_class is None: - tokenizer_class = AutoTokenizer - - if device is not None: - model_kwargs["device_map"] = device - - model = model_class.from_pretrained(model_name, **model_kwargs) - - processor_kwargs.setdefault("padding_side", "left") - processor_kwargs.setdefault("pad_token", "[PAD]") - processor = processor_class.from_pretrained(model_name, **processor_kwargs) - - if tokenizer_class is None: - if getattr(processor, "tokenizer", None): - tokenizer = processor.tokenizer - else: - tokenizer = AutoTokenizer.from_pretrained(model_name, **processor_kwargs) - else: - tokenizer = tokenizer_class.from_pretrained(model_name, **processor_kwargs) - - return TransformersVision(model, tokenizer, processor) diff --git a/outlines/models/vllm.py b/outlines/models/vllm.py deleted file mode 100644 index d1f97bde..00000000 --- a/outlines/models/vllm.py +++ /dev/null @@ -1,226 +0,0 @@ -import dataclasses -from typing import TYPE_CHECKING, List, Optional, Union - -from transformers import SPIECE_UNDERLINE, PreTrainedTokenizerBase - -from outlines.generate.api import GenerationParameters, SamplingParameters - -if TYPE_CHECKING: - from vllm import LLM - from vllm.sampling_params import SamplingParams - - -class VLLM: - """Represents a vLLM model. - - We wrap models from model providing libraries in order to give all of - them the same interface in Outlines and allow users to easily switch - between providers. This class wraps the `vllm.LLM` class from the - `vllm` library. - - """ - - def __init__(self, model: "LLM"): - self.model = model - self.lora_request = None - - self.tokenizer = self._get_tokenizer() - - def _get_tokenizer(self): - if hasattr(self.model, "get_tokenizer"): - tokenizer = self.model.get_tokenizer() - elif hasattr(self.model, "tokenizer"): - if hasattr(self.model.tokenizer, "tokenizer"): - tokenizer = self.model.tokenizer.tokenizer - else: - tokenizer = self.model.tokenizer - else: - raise ValueError( - "The provided LLM instance neither has a " - "`tokenizer` attribute or a `get_tokenizer` method." - ) - return adapt_tokenizer(tokenizer=tokenizer) - - def generate( - self, - prompts: Union[str, List[str]], - generation_parameters: GenerationParameters, - logits_processor, - sampling_parameters: SamplingParameters, - *, - sampling_params: Optional["SamplingParams"] = None, - use_tqdm: bool = True, - ): - """Generate text using vLLM. - - Arguments - --------- - prompts - A prompt or list of prompts. - generation_parameters - An instance of `GenerationParameters` that contains the prompt, - the maximum number of tokens, stop sequences and seed. All the - arguments to `SequenceGeneratorAdapter`'s `__cal__` method. - logits_processor - The logits processor to use when generating text. - sampling_parameters - An instance of `SamplingParameters`, a dataclass that contains - the name of the sampler to use and related parameters as available - in Outlines. - sampling_params - An instance of `vllm.sampling_params.SamplingParams`. The values - passed via this dataclass supersede the values of the parameters - in `generation_parameters` and `sampling_parameters`. See the - vLLM documentation for more details: https://docs.vllm.ai/en/latest/dev/sampling_params.html. - use_tqdm - A boolean in order to display progress bar while inferencing - - Returns - ------- - The generated text, of shape `(n_batch, n_samples)`. If there are only - one batch and several samples, the list is of shape `(n_samples)`. If - this is a batch with several sequences but only one sample the list is - of shape `(n_batch)`. If there is only one sequence and one sample, a - string is returned. - - """ - from vllm.sampling_params import SamplingParams - - if sampling_params is None: - sampling_params = SamplingParams() - - max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) - - # We only update the values in `sampling_params` if they - # are specified by the user when calling the generator. - if max_tokens is not None: - sampling_params.max_tokens = max_tokens - if stop_at is not None: - if isinstance(stop_at, str): - stop_at = [stop_at] - sampling_params.stop = stop_at - if seed is not None: - sampling_params.seed = seed - - sampling_params.logits_processors = ( - [logits_processor] if logits_processor is not None else [] - ) - - sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( - sampling_parameters - ) - - # We only update the values in `sampling_params` that - # were not specified by the user. - if sampling_params.n == 1: - sampling_params.n = num_samples - sampling_params.best_of = num_samples - if top_p is not None and sampling_params.top_p == 1.0: - sampling_params.top_p = top_p - if top_k is not None and sampling_params.top_k == -1: - sampling_params.top_k = top_k - # TODO: remove this if statement once fixed - # https://github.com/vllm-project/vllm/issues/5404#issuecomment-2175972897 - if top_k == 1: - sampling_params.repetition_penalty = 0 - if temperature is not None and sampling_params.temperature == 1.0: - sampling_params.temperature = temperature - if sampler == "beam_search": - sampling_params.use_beam_search = True - - results = self.model.generate( - prompts, - sampling_params=sampling_params, - lora_request=self.lora_request, - use_tqdm=use_tqdm, - ) - results = [[sample.text for sample in batch.outputs] for batch in results] - - batch_size = len(results) - sample_size = len(results[0]) - - if batch_size == 1 and sample_size == 1: - return results[0][0] - elif batch_size == 1: - return results[0] - elif sample_size == 1: - return [batch[0] for batch in results] - - return results - - def stream(self, *args, **kwargs): - """Return a text generator. - - Streaming is not yet available for `vllm.LLM`. - - TODO: Implement the streaming functionality ourselves. - - """ - raise NotImplementedError( - "Streaming is not available for the vLLM integration." - ) - - def load_lora(self, adapter_path: Optional[str]): - from vllm.lora.request import LoRARequest - - if adapter_path is None: - self.lora_request = None - else: - self.lora_request = LoRARequest(adapter_path, 1, adapter_path) - - -def vllm(model_name: str, **vllm_model_params): - """Load a vLLM model. - - Arguments - --------- - model_name - The name of the model to load from the HuggingFace hub. - vllm_model_params - vLLM-specific model parameters. See the vLLM code for the full list: - https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py - - """ - from vllm import LLM - - model = LLM(model_name, **vllm_model_params) - - return VLLM(model) - - -def adapt_tokenizer(tokenizer: PreTrainedTokenizerBase) -> PreTrainedTokenizerBase: - """Adapt a tokenizer to use to compile the FSM. - - The API of Outlines tokenizers is slightly different to that of `transformers`. In - addition we need to handle the missing spaces to Llama's tokenizer to be able to - compile FSMs for this model. - - Parameters - ---------- - tokenizer - The tokenizer of the model. - - Returns - ------- - PreTrainedTokenizerBase - The adapted tokenizer. - """ - tokenizer.vocabulary = tokenizer.get_vocab() - tokenizer.special_tokens = set(tokenizer.all_special_tokens) - - def convert_token_to_string(token: Union[str, bytes]) -> str: - string = tokenizer.convert_tokens_to_string([token]) - - # A hack to handle missing spaces to HF's Llama tokenizers - if ( - type(token) is str - and token.startswith(SPIECE_UNDERLINE) - or token == "<0x20>" - ): - return " " + string - - return string - - tokenizer.convert_token_to_string = convert_token_to_string - - return tokenizer diff --git a/pyproject.toml b/pyproject.toml index 99d4f94e..e8d1abf2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,11 +56,7 @@ test = [ "accelerate", "beartype<0.16.0", "responses", - "llama-cpp-python", - "mlx-lm; platform_machine == 'arm64' and sys_platform == 'darwin'", "huggingface_hub", - "openai>=1.0.0", - "vllm; sys_platform != 'darwin'", "torch", "transformers", "pillow", @@ -111,10 +107,6 @@ module = [ "exllamav2.*", "jinja2", "jsonschema.*", - "openai.*", - "mamba_ssm.*", - "mlx_lm.*", - "mlx.*", "nest_asyncio", "numpy.*", "cloudpickle.*", @@ -122,10 +114,8 @@ module = [ "pydantic.*", "pytest", "referencing.*", - "tiktoken.*", "torch.*", "transformers.*", - "llama_cpp", "huggingface_hub", "lark.*", "interegular.*", @@ -133,11 +123,8 @@ module = [ "numba.*", "requests.*", "responses.*", - "vllm.*", "uvicorn.*", "fastapi.*", - "pycountry.*", - "pyairports.*", ] ignore_missing_imports = true diff --git a/tests/generate/test_generate.py b/tests/generate/test_generate.py index 8d5daa37..9e16b2dd 100644 --- a/tests/generate/test_generate.py +++ b/tests/generate/test_generate.py @@ -12,24 +12,6 @@ ########################################## -@pytest.fixture(scope="session") -def model_llamacpp(tmp_path_factory): - return models.llamacpp( - repo_id="M4-ai/TinyMistral-248M-v2-Instruct-GGUF", - filename="TinyMistral-248M-v2-Instruct.Q4_K_M.gguf", - ) - - -@pytest.fixture(scope="session") -def model_mlxlm(tmp_path_factory): - return models.mlxlm("mlx-community/TinyLlama-1.1B-Chat-v1.0-4bit") - - -@pytest.fixture(scope="session") -def model_mlxlm_phi3(tmp_path_factory): - return models.mlxlm("mlx-community/Phi-3-mini-4k-instruct-4bit") - - @pytest.fixture(scope="session") def model_transformers_random(tmp_path_factory): return models.transformers("hf-internal-testing/tiny-random-gpt2", device="cpu") @@ -40,11 +22,6 @@ def model_transformers_opt125m(tmp_path_factory): return models.transformers("facebook/opt-125m", device="cpu") -@pytest.fixture(scope="session") -def model_mamba(tmp_path_factory): - return models.mamba(model_name="state-spaces/mamba-130m-hf", device="cpu") - - @pytest.fixture(scope="session") def model_bart(tmp_path_factory): from transformers import AutoModelForSeq2SeqLM @@ -71,10 +48,6 @@ def model_transformers_vision(tmp_path_factory): ) -@pytest.fixture(scope="session") -def model_vllm(tmp_path_factory): - return models.vllm("facebook/opt-125m", gpu_memory_utilization=0.1) - # TODO: exllamav2 failing in main, address in https://github.com/outlines-dev/outlines/issues/808 # TODO: t5 tokenizer doesn't work with streaming @@ -97,15 +70,9 @@ def model_t5(tmp_path_factory): ALL_MODEL_FIXTURES = ( - "model_llamacpp", - "model_mlxlm", - "model_mlxlm_phi3", "model_transformers_random", "model_transformers_opt125m", - "model_mamba", "model_bart", - "model_transformers_vision", - "model_vllm", ) @@ -149,10 +116,10 @@ def enforce_not_implemented(model_fixture, *task_names): assert an NotImplementedError is raised. Otherwise, run normally """ NOT_IMPLEMENTED = { - "stream": ["model_transformers_vision", "model_vllm"], - "batch": ["model_llamacpp", "model_mlxlm", "model_mlxlm_phi3"], - "beam_search": ["model_llamacpp", "model_mlxlm", "model_mlxlm_phi3"], - "multiple_samples": ["model_llamacpp", "model_mlxlm", "model_mlxlm_phi3"], + "stream": ["model_transformers_vision"], + "batch": [], + "beam_search": [], + "multiple_samples": [], } for task_name in task_names: if model_fixture in NOT_IMPLEMENTED.get(task_name, []): diff --git a/tests/generate/test_integration_llamacpp.py b/tests/generate/test_integration_llamacpp.py deleted file mode 100644 index 0d1908eb..00000000 --- a/tests/generate/test_integration_llamacpp.py +++ /dev/null @@ -1,380 +0,0 @@ -import datetime -import re - -import pytest -from pydantic import BaseModel, constr - -import outlines.generate as generate -import outlines.grammars as grammars -import outlines.models as models -import outlines.samplers as samplers - -TEST_MODEL = "./llama-test-model/TinyMistral-248M-v2-Instruct.Q4_K_M.gguf" - - -@pytest.fixture(scope="session") -def model(tmp_path_factory): - return models.llamacpp( - repo_id="M4-ai/TinyMistral-248M-v2-Instruct-GGUF", - filename="TinyMistral-248M-v2-Instruct.Q4_K_M.gguf", - ) - - -@pytest.mark.parametrize( - "generator_type,params", - ( - (generate.text, []), - (generate.regex, ("[0-9]",)), - # (generate.cfg, (grammars.arithmetic,)), # Awaiting CFG fix - ), -) -def test_llamacpp_generation_api(model, generator_type, params): - generator = generator_type(model, *params) - - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - res = generator("test", stop_at=".") - assert isinstance(res, str) - - res = generator("test", stop_at=[".", "ab"]) - assert isinstance(res, str) - - res = generator("test", stop_at=[".", "ab"]) - assert isinstance(res, str) - - res1 = generator("test", seed=1, max_tokens=10) - res2 = generator("test", seed=1, max_tokens=10) - assert isinstance(res1, str) - assert isinstance(res2, str) - assert res1 == res2 - - -def test_llama_cpp_streaming_api(model): - generator = generate.text(model) - token_generator = generator.stream("test", max_tokens=10) - tokens = [token for token in token_generator] - assert len(tokens) <= 10 - assert isinstance(tokens[0], str) - - -@pytest.mark.xfail(reason="Batch inference is not available in `llama-cpp-python`.") -def test_llamacpp_batch_inference(model): - generator = generate.text(model) - res = generator(["test", "test1"]) - assert len(res) == 2 - - -def test_llamacpp_sampling_params(model): - generator = generate.text(model) - - params = { - "frequency_penalty": 1.0, - "presence_penalty": 1.0, - } - res = generator("test", seed=1, max_tokens=10, **params) - assert isinstance(res, str) - - -def test_llamacpp_greedy_sampling(model): - sampler = samplers.greedy() - generator = generate.text(model, sampler) - res = generator("test", max_tokens=20) - assert isinstance(res, str) - - -def test_llamacpp_multinomial_sampling(model): - sampler = samplers.multinomial() - generator = generate.text(model, sampler) - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - sampler = samplers.multinomial(1, temperature=1.0) - generator = generate.text(model, sampler) - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - sampler = samplers.multinomial(1, top_k=1) - generator = generate.text(model, sampler) - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - sampler = samplers.multinomial(1, top_p=0.5) - generator = generate.text(model, sampler) - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - -def test_llamacpp_several_samples(model): - sampler = samplers.multinomial(3) - generator = generate.text(model, sampler) - with pytest.raises(NotImplementedError, match="allow to take several samples"): - generator("test") - - -def test_llamacpp_beam_search(model): - sampler = samplers.beam_search(1) - generator = generate.text(model, sampler) - - with pytest.raises(NotImplementedError, match="does not support Beam Search"): - generator("test") - - -def test_llamacpp_text_stop(model): - prompt = ( - "<|im_start|>user\nWrite a short sentence<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.text(model)(prompt, stop_at="a", max_tokens=100) - assert isinstance(sequence, str) - assert sequence.find("a") == -1 - - -def test_llamacpp_regex(model): - prompt = ( - "<|im_start|>user\nWrite an email address<|im_end|>\n<|im_start|>assistant\n" - ) - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str) - - # One prompt - sequence = generator(prompts=prompt) - assert isinstance(sequence, str) - assert re.fullmatch(pattern=regex_str, string=sequence) is not None - - -def test_llamacpp_integer(model): - prompt = ( - "<|im_start|>user\nWrite a short sentence<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.format(model, int)(prompt, max_tokens=10) - assert isinstance(sequence, int) - assert sequence != "" - int(sequence) - - -def test_llamacpp_float(model): - prompt = ( - "<|im_start|>user\nWrite a short sentence<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.format(model, float)(prompt, max_tokens=10) - assert isinstance(sequence, float) - - assert sequence != "" - float(sequence) - - -def test_llamacpp_bool(model): - prompt = ( - "<|im_start|>user\nIs this True or False?<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.format(model, bool)(prompt, max_tokens=10) - assert isinstance(sequence, bool) - - assert sequence != "" - bool(sequence) - - -def test_llamacpp_date(model): - prompt = ( - "<|im_start|>user\nWhat day is it today?<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.format(model, datetime.date)(prompt, max_tokens=20, seed=10) - assert isinstance(sequence, datetime.date) - - -def test_llamacpp_time(model): - prompt = "<|im_start|>user\nWhat time is it?<|im_end|>\n<|im_start|>assistant\n" - sequence = generate.format(model, datetime.time)(prompt, max_tokens=10) - assert isinstance(sequence, datetime.time) - - -def test_llamacpp_datetime(model): - prompt = "<|im_start|>user\nWhat time is it?<|im_end|>\n<|im_start|>assistant\n" - sequence = generate.format(model, datetime.datetime)(prompt, max_tokens=20) - assert isinstance(sequence, datetime.datetime) - - -def test_llamacpp_choice(model): - prompt = ( - "<|im_start|>user\nWrite a short sentence<|im_end|>\n<|im_start|>assistant\n" - ) - sequence = generate.choice(model, ["test", "choice"])(prompt) - assert sequence == "test" or sequence == "choice" - - -def test_llamacpp_json_basic(model): - prompt = "<|im_start|>user\nOutput some JSON<|im_end|>\n<|im_start|>assistant\n" - - class Spam(BaseModel): - spam: constr(max_length=10) - fuzz: bool - - result = generate.json(model, Spam, whitespace_pattern="")( - prompt, max_tokens=100, temperature=0.0, seed=1 - ) - assert isinstance(result, BaseModel) - assert isinstance(result.spam, str) - assert isinstance(result.fuzz, bool) - assert len(result.spam) <= 10 - - -def test_llamacpp_json_schema(model): - prompt = "<|im_start|>user\nOutput some JSON<|im_end|>\n<|im_start|>assistant\n" - - schema = """{ - "title": "spam", - "type": "object", - "properties": { - "foo" : {"type": "boolean"}, - "bar": {"type": "string", "maxLength": 4} - }, - "required": ["foo", "bar"] - } - """ - - result = generate.json(model, schema, whitespace_pattern="")( - prompt, max_tokens=100, temperature=0, seed=10 - ) - assert isinstance(result, dict) - assert isinstance(result["foo"], bool) - assert isinstance(result["bar"], str) - - -def test_llamacpp_cfg(model): - prompt = "<|im_start|>user\nOutput a short and valid JSON object with two keys.<|im_end|>\n><|im_start|>assistant\n" - - # remove this statement once cfg is implemented - with pytest.raises(NotImplementedError): - result = generate.cfg(model, grammars.arithmetic)(prompt, seed=11) - assert isinstance(result, str) - - -@pytest.mark.parametrize( - "repo,model_path,hf_tokenizer_uri", - [ - ("Qwen/Qwen1.5-0.5B-Chat-GGUF", "*q2*.gguf", "Qwen/Qwen1.5-0.5B-Chat"), - ("TheBloke/phi-2-GGUF", "*Q2*.gguf", "microsoft/phi-2"), - ], -) -def test_byte_tokenizer_regression(repo, model_path, hf_tokenizer_uri): - """Reproduce https://github.com/outlines-dev/outlines/issues/820""" - import llama_cpp - - model = models.llamacpp( - repo, - model_path, - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - hf_tokenizer_uri - ), - ) - generator = generate.choice(model, ["skirt", "dress", "pen", "jacket"]) - generator("Pick the odd word out: skirt, dress, pen, jacket") - - -def test_llama_cpp_pre_tokenizer_remains_broken(): - """If fails, llama.cpp pre-tokenizer is fixed -> revert #892, remove `with pytest.raises`""" - repo = "Qwen/Qwen1.5-0.5B-Chat-GGUF" - model_path = "*q2*.gguf" - - model = models.llamacpp(repo, model_path) - with pytest.raises(RuntimeError): - generate.choice(model, ["skirt", "dress", "pen", "jacket"]) - - -def test_RegexGuide_caching(model, temp_cache_dir): - import llama_cpp - - import outlines.caching - from outlines.fsm.guide import create_states_mapping - - assert outlines.caching._caching_enabled - - regex = r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)" - prompt = "What is the IP address of the Google DNS servers? " - - cache = outlines.caching.get_cache() - - # Returns (hits, misses) - _ = cache.stats(enable=True) - assert cache.statistics - - assert create_states_mapping.__memory__ is cache - - generator = generate.regex(model, regex, sampler=samplers.greedy()) - assert cache.stats() == (0, 1) - - model_2 = models.llamacpp( - "Qwen/Qwen1.5-0.5B-Chat-GGUF", - "*q2*.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Qwen/Qwen1.5-0.5B-Chat" - ), - ) - generator_2 = generate.regex(model_2, regex, sampler=samplers.greedy()) - assert cache.stats() == (0, 2) - - # These two different models and tokenizers should not have the same state - # mapping results - assert ( - generator.logits_processor.fsm.states_to_token_maps - != generator_2.logits_processor.fsm.states_to_token_maps - ) - - generator_3 = generate.regex(model_2, regex, sampler=samplers.greedy()) - assert cache.stats() == (1, 2) - assert ( - generator_2.logits_processor.fsm.states_to_token_maps - == generator_3.logits_processor.fsm.states_to_token_maps - ) - - # Just for fun... - structured = generator(prompt, max_tokens=30) - structured_2 = generator_2(prompt, max_tokens=30) - - assert re.fullmatch(regex, structured) - assert re.fullmatch(regex, structured_2) - assert structured != structured_2 - - -def test_tokenizer_vocabulary_decode_sanity(): - """Assert the decoded newline token (198) is the same as the normalized vocab token""" - import llama_cpp - - model = models.llamacpp( - "bartowski/Meta-Llama-3-8B-Instruct-GGUF", - "Meta-Llama-3-8B-Instruct-IQ1_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - ) - tokenizer = generate.regex(model, "a").logits_processor.tokenizer - - decoded_nl_token = tokenizer.decode([198])[0] - vocab_nl_token = tokenizer.convert_token_to_string( - [token for token, token_id in tokenizer.vocabulary.items() if token_id == 198][ - 0 - ] - ) - assert decoded_nl_token == vocab_nl_token - - -def test_no_length_constraint_when_unset(): - """Assert that models.llamacpp doesn't have an implicit max_tokens preventing full sequence generation""" - import llama_cpp - - model = models.llamacpp( - repo_id="M4-ai/TinyMistral-248M-v2-Instruct-GGUF", - filename="TinyMistral-248M-v2-Instruct.Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Locutusque/TinyMistral-248M-Instruct" - ), - ) - - long_pattern = "abcdefg" * 10 - generator = generate.regex(model, long_pattern) - - output = generator("a") - assert re.match(long_pattern, output) diff --git a/tests/generate/test_integration_transformers_vision.py b/tests/generate/test_integration_transformers_vision.py deleted file mode 100644 index 28b516c5..00000000 --- a/tests/generate/test_integration_transformers_vision.py +++ /dev/null @@ -1,116 +0,0 @@ -from io import BytesIO -from urllib.request import urlopen - -import pytest -from PIL import Image -from transformers import AutoProcessor, LlavaForConditionalGeneration - -import outlines -from outlines.models.transformers_vision import transformers_vision - -IMAGE_URLS = [ - "https://upload.wikimedia.org/wikipedia/commons/2/25/Siam_lilacpoint.jpg", - "https://upload.wikimedia.org/wikipedia/commons/7/71/2010-kodiak-bear-1.jpg", - "https://upload.wikimedia.org/wikipedia/commons/b/be/Tamias-rufus-001.jpg", -] - - -def img_from_url(url): - img_byte_stream = BytesIO(urlopen(url).read()) - return Image.open(img_byte_stream).convert("RGB") - - -@pytest.fixture(scope="session") -def model(tmp_path_factory): - return transformers_vision( - "trl-internal-testing/tiny-random-LlavaForConditionalGeneration", - model_class=LlavaForConditionalGeneration, - device="cpu", - ) - - -@pytest.fixture(scope="session") -def processor(tmp_path_factory): - return AutoProcessor.from_pretrained("llava-hf/llava-interleave-qwen-0.5b-hf") - - -def test_single_image_text_gen(model, processor): - conversation = [ - { - "role": "user", - "content": [{"type": "text", "text": "What is this?"}, {"type": "image"}], - }, - ] - generator = outlines.generate.text(model) - sequence = generator( - processor.apply_chat_template(conversation), - [img_from_url(IMAGE_URLS[0])], - seed=10000, - max_tokens=10, - ) - assert isinstance(sequence, str) - - -def test_multi_image_text_gen(model, processor): - """If the length of image tags and number of images we pass are > 1 and equal, - we should yield a successful generation. - """ - conversation = [ - { - "role": "user", - "content": [ - {"type": "text", "text": "What do all these have in common?"}, - ] - + [{"type": "image"} for _ in range(len(IMAGE_URLS))], - }, - ] - generator = outlines.generate.text(model) - sequence = generator( - processor.apply_chat_template(conversation), - [img_from_url(i) for i in IMAGE_URLS], - seed=10000, - max_tokens=10, - ) - assert isinstance(sequence, str) - - -def test_mismatched_image_text_gen(model, processor): - """If the length of image tags and number of images we pass are unequal, - we should raise an error. - """ - conversation = [ - { - "role": "user", - "content": [ - {"type": "text", "text": "I'm passing 3 images, but only 1 image tag"}, - {"type": "image"}, - ], - }, - ] - generator = outlines.generate.text(model) - with pytest.raises(ValueError): - _ = generator( - processor.apply_chat_template(conversation), - [img_from_url(i) for i in IMAGE_URLS], - seed=10000, - max_tokens=10, - ) - - -def test_single_image_choice(model, processor): - conversation = [ - { - "role": "user", - "content": [{"type": "text", "text": "What is this?"}, {"type": "image"}], - }, - ] - choices = ["cat", "dog"] - generator = outlines.generate.choice(model, choices) - sequence = generator( - processor.apply_chat_template(conversation), - [img_from_url(IMAGE_URLS[0])], - seed=10000, - max_tokens=10, - ) - assert isinstance(sequence, str) - assert sequence in choices diff --git a/tests/generate/test_integration_vllm.py b/tests/generate/test_integration_vllm.py deleted file mode 100644 index 4634bc83..00000000 --- a/tests/generate/test_integration_vllm.py +++ /dev/null @@ -1,239 +0,0 @@ -import datetime -import re - -import pytest -import torch -from pydantic import BaseModel, constr -from vllm.sampling_params import SamplingParams - -import outlines.generate as generate -import outlines.grammars as grammars -import outlines.models as models -import outlines.samplers as samplers - -pytestmark = pytest.mark.skipif( - not torch.cuda.is_available(), reason="vLLM models can only be run on GPU." -) - - -@pytest.fixture(scope="module") -def model(): - return models.vllm("gpt2", gpu_memory_utilization=0.5) - - -@pytest.mark.parametrize( - "generator_type,params", ((generate.text, []), (generate.regex, ("[0-9]",))) -) -def test_vllm_generation_api(model, generator_type, params): - generator = generator_type(model, *params) - - res = generator("test") - assert isinstance(res, str) - - res = generator("test", max_tokens=10) - assert isinstance(res, str) - - res = generator("test", stop_at=".") - assert isinstance(res, str) - - res = generator("test", stop_at=[".", "ab"]) - assert isinstance(res, str) - - res = generator("test", stop_at=[".", "ab"]) - assert isinstance(res, str) - - res1 = generator("test", seed=1) - res2 = generator("test", seed=1) - assert isinstance(res1, str) - assert isinstance(res2, str) - assert res1 == res2 - - res = generator(["test", "test1"]) - assert len(res) == 2 - - -def test_vllm_sampling_params(model): - generator = generate.text(model) - - sampling_params = SamplingParams(n=2) - res = generator("test", sampling_params=sampling_params) - assert len(res) == 2 - assert isinstance(res[0], str) - assert isinstance(res[1], str) - - sampling_params = SamplingParams(seed=2) - res1 = generator("test", sampling_params=sampling_params) - res2 = generator("test", sampling_params=sampling_params) - assert res1 == res2 - - -def test_vllm_greedy_sampling(model): - sampler = samplers.greedy() - generator = generate.text(model, sampler) - res = generator("test") - assert isinstance(res, str) - - -def test_vllm_multinomial_sampling(model): - sampler = samplers.multinomial() - generator = generate.text(model, sampler) - res = generator("test") - assert isinstance(res, str) - - sampler = samplers.multinomial(3) - generator = generate.text(model, sampler) - res = generator("test") - assert len(res) == 3 - assert isinstance(res[0], str) - assert isinstance(res[1], str) - - sampler = samplers.multinomial(2, top_k=1) - generator = generate.text(model, sampler) - res = generator("test") - assert res[0] == res[1] - - sampler = samplers.multinomial(1, top_p=0.5) - generator = generate.text(model, sampler) - res = generator("test") - assert isinstance(res, str) - - sampler = samplers.multinomial(2, temperature=0.00001) - generator = generate.text(model, sampler) - res = generator("test") - assert res[0] == res[1] - - -def test_vllm_beam_search(model): - sampler = samplers.beam_search(1) - generator = generate.text(model, sampler) - res1 = generator("test") - sampler = samplers.greedy() - generator = generate.text(model, sampler) - res2 = generator("test") - assert res1 == res2 - - sampler = samplers.beam_search(2) - generator = generate.text(model, sampler) - res = generator("test") - assert len(res) == 2 - assert res[0] != res[1] - - -def test_vllm_text_stop(model): - prompt = "Write a short sentence containing 'You': " - sequence = generate.text(model)(prompt, max_tokens=100, seed=10) - assert sequence.find("news") != -1 - - sequence = generate.text(model)(prompt, stop_at="news", max_tokens=100, seed=10) - assert isinstance(sequence, str) - assert sequence.find("news") == -1 - - -def test_vllm_regex(model): - prompt = "Write an email address: " - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str) - - # One prompt - sequence = generator(prompts=prompt) - assert isinstance(sequence, str) - assert re.fullmatch(pattern=regex_str, string=sequence) is not None - - -def test_vllm_integer(model): - prompt = "Give me an integer: " - sequence = generate.format(model, int)(prompt, max_tokens=10) - assert isinstance(sequence, int) - assert sequence != "" - int(sequence) - - -def test_vllm_float(model): - prompt = "Give me a floating-point number: " - sequence = generate.format(model, float)(prompt, max_tokens=10) - assert isinstance(sequence, float) - - assert sequence != "" - float(sequence) - - -def test_vllm_bool(model): - prompt = "Is this True or False? " - sequence = generate.format(model, bool)(prompt, max_tokens=10) - assert isinstance(sequence, bool) - - assert sequence != "" - bool(sequence) - - -def test_vllm_date(model): - prompt = "What day is it today? " - sequence = generate.format(model, datetime.date)(prompt, max_tokens=10) - assert isinstance(sequence, datetime.date) - - -def test_vllm_time(model): - prompt = "What time is it? " - sequence = generate.format(model, datetime.time)(prompt, max_tokens=10) - assert isinstance(sequence, datetime.time) - - -def test_vllm_datetime(model): - prompt = "What time is it? " - sequence = generate.format(model, datetime.datetime)(prompt, max_tokens=20) - assert isinstance(sequence, datetime.datetime) - - -def test_vllm_choice(model): - prompt = "Which one between 'test' and 'choice'? " - sequence = generate.choice(model, ["test", "choice"])(prompt) - assert sequence == "test" or sequence == "choice" - - -def test_vllm_json_basic(model): - prompt = "Output some JSON. " - - class Spam(BaseModel): - spam: constr(max_length=10) - fuzz: bool - - sampling_params = SamplingParams(temperature=0) - result = generate.json(model, Spam, whitespace_pattern="")( - prompt, max_tokens=100, seed=1, sampling_params=sampling_params - ) - assert isinstance(result, BaseModel) - assert isinstance(result.spam, str) - assert isinstance(result.fuzz, bool) - assert len(result.spam) <= 10 - - -def test_vllm_json_schema(model): - prompt = "Output some JSON. " - - schema = """{ - "title": "spam", - "type": "object", - "properties": { - "foo" : {"type": "boolean"}, - "bar": {"type": "string", "maxLength": 4} - }, - "required": ["foo", "bar"] - } - """ - - sampling_params = SamplingParams(temperature=0) - result = generate.json(model, schema, whitespace_pattern="")( - prompt, max_tokens=100, seed=10, sampling_params=sampling_params - ) - assert isinstance(result, dict) - assert isinstance(result["foo"], bool) - assert isinstance(result["bar"], str) - - -@pytest.mark.xfail( - reason="The CFG logits processor for vLLM has not been implemented yet." -) -def test_vllm_cfg(model): - prompt = "<|im_start|>user\nOutput a short and valid JSON object with two keys.<|im_end|>\n><|im_start|>assistant\n" - result = generate.cfg(model, grammars.arithmetic)(prompt, seed=11) - assert isinstance(result, str) From 46230bb4f4777b6644a51bf38f26ceb17b6db32a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 14:07:48 +0200 Subject: [PATCH 08/20] Remove the text generation logic --- outlines/__init__.py | 1 - outlines/generate/__init__.py | 8 - outlines/generate/api.py | 622 ------------------ outlines/generate/cfg.py | 31 - outlines/generate/choice.py | 19 - outlines/generate/format.py | 38 -- outlines/generate/fsm.py | 22 - outlines/generate/generator.py | 312 --------- outlines/generate/json.py | 67 -- outlines/generate/regex.py | 36 - outlines/generate/text.py | 35 - outlines/models/transformers.py | 21 +- outlines/samplers.py | 324 --------- tests/generate/__init__.py | 0 tests/generate/conftest.py | 64 -- tests/generate/test_api.py | 33 - tests/generate/test_generate.py | 303 --------- tests/generate/test_generator.py | 497 -------------- .../generate/test_integration_transformers.py | 544 --------------- tests/test_samplers.py | 254 ------- 20 files changed, 20 insertions(+), 3211 deletions(-) delete mode 100644 outlines/generate/__init__.py delete mode 100644 outlines/generate/api.py delete mode 100644 outlines/generate/cfg.py delete mode 100644 outlines/generate/choice.py delete mode 100644 outlines/generate/format.py delete mode 100644 outlines/generate/fsm.py delete mode 100644 outlines/generate/generator.py delete mode 100644 outlines/generate/json.py delete mode 100644 outlines/generate/regex.py delete mode 100644 outlines/generate/text.py delete mode 100644 outlines/samplers.py delete mode 100644 tests/generate/__init__.py delete mode 100644 tests/generate/conftest.py delete mode 100644 tests/generate/test_api.py delete mode 100644 tests/generate/test_generate.py delete mode 100644 tests/generate/test_generator.py delete mode 100644 tests/generate/test_integration_transformers.py delete mode 100644 tests/test_samplers.py diff --git a/outlines/__init__.py b/outlines/__init__.py index 04f4da55..f8d13c66 100644 --- a/outlines/__init__.py +++ b/outlines/__init__.py @@ -1,5 +1,4 @@ """Outlines is a Generative Model Programming Framework.""" -import outlines.generate import outlines.models from outlines.caching import clear_cache, disable_cache, get_cache diff --git a/outlines/generate/__init__.py b/outlines/generate/__init__.py deleted file mode 100644 index f28cbd80..00000000 --- a/outlines/generate/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .api import SequenceGenerator -from .cfg import cfg -from .choice import choice -from .format import format -from .fsm import fsm -from .json import json -from .regex import regex -from .text import text diff --git a/outlines/generate/api.py b/outlines/generate/api.py deleted file mode 100644 index ad01377c..00000000 --- a/outlines/generate/api.py +++ /dev/null @@ -1,622 +0,0 @@ -import datetime -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union - -from outlines.generate.generator import sequence_generator -from outlines.samplers import BeamSearchSampler, GreedySampler, MultinomialSampler - -if TYPE_CHECKING: - import torch - -FormattedOutput = Union[ - str, int, float, bool, datetime.date, datetime.time, datetime.datetime -] - - -class SequenceGenerator: - def __init__( - self, - fsm, - model, - sampler, - device, - ): - self.fsm = fsm - self.model = model - self.sampler = sampler - self.tokenizer = model.tokenizer - self.device = device - self.num_samples = sampler.samples - - def get_generated_token_ids( - self, - prompt_token_ids: "torch.Tensor", - token_ids: "torch.Tensor", - ) -> List["torch.Tensor"]: - """Get the tokens generated so far. - - Parameters - ---------- - prompt_token_ids - Tensor that contains the token ids of the sequences' prompts. - token_ids - The generated token ids. - - Returns - ------- - A tensor that contains the token ids that have been generated so far. - - """ - prompt_lengths = [len(prompt) for prompt in prompt_token_ids] - token_ids = [ - cur_token_ids[length:] - for cur_token_ids, length in zip(token_ids, prompt_lengths) - ] - - return token_ids - - def is_stop_sequence_found( - self, generated_sequences: List[str], stop_sequences: List[str] - ) -> bool: - """Determine whether one of the stop sequences has been generated. - - Parameters - ---------- - generated_sequences - The list of sequences generated so far. - stop_sequences - The list that contains the sequence which stop the generation when - found. - - Returns - ------- - True if at least one of the stop sequences has been found in each generated - sequence. - - """ - return all( - [ - any([seq in generated for seq in stop_sequences]) - for generated in generated_sequences - ] - ) - - def strip_stop_sequences( - self, sequence: str, stop_sequences: Optional[List[str]] - ) -> str: - """Remove the stop sequences from the generated sequences. - - Parameters - ---------- - sequence - One of the generated sequences. - stop_sequences - The list that contains the sequence which stop the generation when - found. - - """ - if stop_sequences: - match_indexes = [sequence.find(seq) for seq in stop_sequences] - if any([index != -1 for index in match_indexes]): - # select the stop_sequence that is found first in the sequence - min_match_index_value = min([i for i in match_indexes if i != -1]) - min_match_index_pos = match_indexes.index(min_match_index_value) - sequence = sequence[ - : match_indexes[min_match_index_pos] - + len(stop_sequences[min_match_index_pos]) - ] - - return sequence - - def format_sequence(self, sequence: str) -> FormattedOutput: - """Translate the generated sequence to another type. - - This method is for instance overridden when generating JSON to either - return a dictionnary or a Pydantic model. - - Parameters - ---------- - sequence - A generated sequences. - - Returns - ------- - The formatted sequence. - - """ - return sequence - - def __call__( - self, - prompts: Union[str, List[str]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - rng: Optional["torch.Generator"] = None, - ) -> Union[FormattedOutput, List[FormattedOutput], List[List[FormattedOutput]]]: - """Generate the full text sequence. - - Since `SequenceGenerator.stream` calls the tokenizer at every step this - method loops over the generator returned by `sequence_generator` itself - so the tokenizer is called only once after all token ids have been - generated. - - Parameters - ---------- - prompts - A string or list of strings that are passed to the model before - generating the first token. - max_tokens - An integer representing maximum number of tokens that will be generated - (per prompt) - stop_at - A string or list of strings at which the text generated will stop - rng - The random number generator. Defaults to a non-seeded `torch.Generator` - instance. - - Returns - ------- - The generation(s), potentially cast to another type. - """ - import torch - - if isinstance(prompts, str): - prompts = [prompts] - - if isinstance(stop_at, str): - stop_at = [stop_at] - - stop_sequences = stop_at - num_samples = self.num_samples - - if rng is None: - rng = torch.Generator(device=self.device) - rng.seed() - - prompt_token_ids, attention_masks = self.tokenizer.encode(prompts) - prompt_token_ids = prompt_token_ids.to(self.device) - attention_masks = attention_masks.to(self.device) - - # To draw multiple samples we repeat the prompt as many times - # as there are samples. We copy the FSMs and initialize the - # FSM states. - num_samples = self.num_samples - batch_size = len(prompts) - - prompt_token_ids = torch.repeat_interleave(prompt_token_ids, num_samples, dim=0) - attention_masks = torch.repeat_interleave(attention_masks, num_samples, dim=0) - fsm_states = [0 for _ in range(batch_size * num_samples)] - fsms = [self.fsm.copy() for _ in range(batch_size * num_samples)] - weights = torch.zeros( - (batch_size * num_samples), dtype=torch.float, device=self.device - ) - - states = sequence_generator( - self.model, - self.sampler, - fsms, - prompt_token_ids, - weights, - attention_masks, - fsm_states, - rng=rng, - ) - - while True: - try: - last_state = next(states) - if max_tokens or stop_sequences: - token_ids = last_state.token_ids - generated_token_ids = self.get_generated_token_ids( - prompt_token_ids, token_ids - ) - if max_tokens and len(generated_token_ids[0]) >= max_tokens: - break - if stop_sequences and self.is_stop_sequence_found( - self.tokenizer.decode(generated_token_ids), stop_sequences - ): - break - except StopIteration: - break - - token_ids = last_state.token_ids - generated_token_ids = self.get_generated_token_ids(prompt_token_ids, token_ids) - - generated = self.tokenizer.decode(generated_token_ids) - stripped = [ - self.strip_stop_sequences(sequence, stop_sequences) - for sequence in generated - ] - formatted = [self.format_sequence(sequence) for sequence in stripped] - - # We reshape the output to (batch_size, sample_size) - output: List[List[FormattedOutput]] = list() - for i in range(0, batch_size * num_samples, num_samples): - output.append(formatted[i : i + num_samples]) - - # We remove leading dimensions for the output - if batch_size == 1 and num_samples == 1: - return output[0][0] - elif batch_size == 1: - return output[0] - elif num_samples == 1: - return [samples[0] for samples in output] - else: - return output - - def stream( - self, - prompts: Union[str, List[str]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - rng: Optional["torch.Generator"] = None, - ) -> Iterator[Union[List[str], str, List[List[str]]]]: - """Generate the text sequence one token at a time. - - Since `Tokenizer.decode` strips the whitespaces from the tokens we have no - choice but to decode the generated token ids at each step and compare the - current decoded strings to the previously decoded strings. - - Parameters - ---------- - prompts - A string or list of strings that are passed to the model before - generating the first token. - max_tokens - An integer representing maximum number of tokens that will be generated - (per prompt) - stop_at - A string or list of strings at which the text generated will stop - rng - The random number generator. Defaults to a non-seeded `torch.Generator` - instance. - - Returns - ------- - A string or list of strings that contain the generated text. - - """ - import torch - - if isinstance(prompts, str): - prompts = [prompts] - - if isinstance(stop_at, str): - stop_at = [stop_at] - - stop_sequences = stop_at - num_samples = self.num_samples - - prompt_token_ids, attention_masks = self.tokenizer.encode(prompts) - prompt_token_ids = prompt_token_ids.to(self.device) - attention_masks = attention_masks.to(prompt_token_ids.device) - - # To draw multiple samples we repeat the prompt as many times - # as there are samples. We copy the FSMs and initialize the - # FSM states. - num_samples = self.num_samples - batch_size = len(prompts) - - prompt_token_ids = torch.repeat_interleave(prompt_token_ids, num_samples, dim=0) - attention_masks = torch.repeat_interleave(attention_masks, num_samples, dim=0) - fsm_states = [0 for _ in range(batch_size * num_samples)] - fsms = [self.fsm.copy() for _ in range(batch_size * num_samples)] - weights = torch.zeros( - (batch_size * num_samples), - dtype=torch.float, - device=prompt_token_ids.device, - ) - - if rng is None: - rng = torch.Generator(device=prompt_token_ids.device) - rng.seed() - - states = sequence_generator( - self.model, - self.sampler, - fsms, - prompt_token_ids, - weights, - attention_masks, - fsm_states, - rng=rng, - ) - - def token_generator() -> Iterator[Union[List[str], str, List[List[str]]]]: - previously_generated_sequences = [ - "" for _ in range(batch_size) - ] * num_samples - num_generated = 0 - is_stop_at_reached = [False for _ in range(batch_size)] * num_samples - while True: - if (max_tokens and num_generated >= max_tokens) or all( - is_stop_at_reached - ): - return - try: - sequence = next(states) - num_generated += 1 - except StopIteration: - return - generated_token_ids = sequence.token_ids[:, -num_generated:] - generated_sequences = self.tokenizer.decode(generated_token_ids) - if stop_sequences: - is_stop_at_reached = [ - stop - or self.is_stop_sequence_found( - [generated_sequence], stop_sequences - ) - for generated_sequence, stop in zip( - generated_sequences, is_stop_at_reached - ) - ] - - generated_sequences = [ - self.format_sequence( - self.strip_stop_sequences(sequence, stop_sequences) - ) - if stop - else sequence - for sequence, stop in zip( - generated_sequences, is_stop_at_reached - ) - ] - next_tokens = [ - token[len(sequence) :] - for token, sequence, stop in zip( - generated_sequences, - previously_generated_sequences, - is_stop_at_reached, - ) - ] - previously_generated_sequences = generated_sequences - # We reshape the output to (batch_size, sample_size) - output: List[List[str]] = list() - for i in range(0, batch_size * num_samples, num_samples): - output.append(next_tokens[i : i + num_samples]) - - # We remove leading dimensions for the output - if batch_size == 1 and num_samples == 1: - yield output[0][0] - elif batch_size == 1: - yield output[0] - elif num_samples == 1: - yield [samples[0] for samples in output] - else: - yield output - - return token_generator() - - -@dataclass(frozen=True) -class GenerationParameters: - """Generation parameters used in Outlines' public API.""" - - max_tokens: Optional[int] - stop_at: Optional[Union[str, List[str]]] - seed: Optional[int] - - -@dataclass(frozen=True) -class SamplingParameters: - """Sampling parameters available in Outlines.""" - - sampler: str - num_samples: int = 1 - top_p: Optional[float] = None - top_k: Optional[int] = None - temperature: Optional[float] = None - - -class SequenceGeneratorAdapter: - """Class used to unify the interface to the model providers' - generation functions. - - Attributes - ---------- - model - The wrapped model. - logits_processor - The logits processor to use to generate text. - sampler - The sampler to use to generate text. - - """ - - def __init__(self, model, logits_processor, sampler): - self.model = model - self.logits_processor = logits_processor - - if isinstance(sampler, MultinomialSampler): - self.sampling_params = SamplingParameters( - "multinomial", - sampler.samples, - sampler.top_p, - sampler.top_k, - sampler.temperature, - ) - elif isinstance(sampler, GreedySampler): - self.sampling_params = SamplingParameters( - "greedy", sampler.samples, None, None, 0.0 - ) - elif isinstance(sampler, BeamSearchSampler): - self.sampling_params = SamplingParameters( - "beam_search", sampler.samples, None, None, 1.0 - ) - - def prepare_generation_parameters( - self, - max_tokens: Optional[int], - stop_at: Optional[Union[str, List[str]]], - seed: Optional[int], - ): - if isinstance(stop_at, str): - stop_at = [stop_at] - - generation_params = GenerationParameters( - max_tokens, - stop_at, - seed, - ) - - return generation_params - - def format_sequence(self, sequence: str) -> FormattedOutput: - """Translate the generated sequence to another type. - - This method is for instance overridden when generating JSON to either - return a dictionnary or a Pydantic model. - - Parameters - ---------- - sequence - A generated sequences. - - Returns - ------- - The formatted sequence. - - """ - return sequence - - def _format(self, sequences): - """Apply formatting to every string in a completion.""" - if isinstance(sequences, list): - return [self._format(sequence) for sequence in sequences] - else: - return self.format_sequence(sequences) - - def __call__( - self, - prompts: Union[str, List[str]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - seed: Optional[int] = None, - **model_specific_params, - ): - """Generate text from a prompt of list of prompts.""" - - generation_params = self.prepare_generation_parameters( - max_tokens, stop_at, seed - ) - - completions = self.model.generate( - prompts, - generation_params, - self.logits_processor, - self.sampling_params, - **model_specific_params, - ) - - return self._format(completions) - - def stream( - self, - prompts: Union[str, List[str]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - seed: Optional[int] = None, - **model_specific_params, - ): - """Return a text generator from a prompt or a list of prompts.""" - generation_params = self.prepare_generation_parameters( - max_tokens, stop_at, seed - ) - return self.model.stream( - prompts, - generation_params, - self.logits_processor, - self.sampling_params, - **model_specific_params, - ) - - -class VisionSequenceGeneratorAdapter(SequenceGeneratorAdapter): - def __call__( # type: ignore - self, - prompts: Union[str, List[str]], - media: Union[str, Any], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - seed: Optional[int] = None, - **model_specific_params, - ): - """ - Generate text from a prompt of list of prompts. - - Media: A URI to construct media or media object itself. Used as AutoProcessor argument. - """ - prompts, media = self._validate_prompt_media_types(prompts, media) - - generation_params = self.prepare_generation_parameters( - max_tokens, stop_at, seed - ) - - completions = self.model.generate( - prompts, - media, - generation_params, - self.logits_processor, - self.sampling_params, - **model_specific_params, - ) - - return self._format(completions) - - def stream( # type: ignore - self, - prompts: Union[str, List[str]], - media: List[Union[str, Any, List[Union[str, Any]]]], - max_tokens: Optional[int] = None, - stop_at: Optional[Union[str, List[str]]] = None, - seed: Optional[int] = None, - **model_specific_params, - ): - """Return a text generator from a prompt or a list of prompts.""" - prompts, media = self._validate_prompt_media_types(prompts, media) - generation_params = self.prepare_generation_parameters( - max_tokens, stop_at, seed - ) - return self.model.stream( - prompts, - media, - generation_params, - self.logits_processor, - self.sampling_params, - **model_specific_params, - ) - - @classmethod - def _validate_prompt_media_types( - cls, - prompts: Union[str, List[str]], - media: Union[str, Any, List[Union[str, Any]]], - ) -> Union[Any, List[Any]]: - """ - Prepare media as PIL.Image and ensure for every prompt str there is one List[PIL.Image] - """ - - def valid_types(prompts, media): - from PIL import Image # type: ignore - - if isinstance(prompts, list): - if not isinstance(media, list) or len(prompts) != len(media): - return False - for subprompt, submedia in zip(prompts, media): - if not isinstance(subprompt, str) or not all( - isinstance(m, Image.Image) for m in submedia - ): - return False - elif isinstance(prompts, str): - if not all(isinstance(m, Image.Image) for m in media): - return False - return True - - if not valid_types(prompts, media): - raise TypeError( - "Expected (prompts, media) to be of type " - "(str, List[Image])), or (List[str], List[List[Image]]) " - f"instead got prompts={prompts}, media={media}" - ) - - return prompts, media diff --git a/outlines/generate/cfg.py b/outlines/generate/cfg.py deleted file mode 100644 index 6af41400..00000000 --- a/outlines/generate/cfg.py +++ /dev/null @@ -1,31 +0,0 @@ -from functools import singledispatch - -from outlines.generate.api import SequenceGeneratorAdapter -from outlines.samplers import Sampler, multinomial - - -@singledispatch -def cfg( - model, cfg_str: str, sampler: Sampler = multinomial() -) -> SequenceGeneratorAdapter: - """Generate text in the language of a Context-Free Grammar - - Arguments - --------- - model: - An instance of `Transformer` that represents a model from the - `transformers` library. - sampler: - The sampling algorithm to use to generate token ids from the logits - distribution. - - Returns - ------- - A `SequenceGeneratorAdapter` instance that generates text. - - """ - raise NotImplementedError( - f"The CFG Logits processor is not available for {type(model)}. " - + "Please subscribe to https://github.com/outlines-dev/outlines/issues/684" - + " for updates on the fix." - ) diff --git a/outlines/generate/choice.py b/outlines/generate/choice.py deleted file mode 100644 index 970364d7..00000000 --- a/outlines/generate/choice.py +++ /dev/null @@ -1,19 +0,0 @@ -from functools import singledispatch -from typing import Callable, List - -from outlines.generate.api import SequenceGeneratorAdapter -from outlines.samplers import Sampler, multinomial - -from .regex import regex - - -@singledispatch -def choice( - model, choices: List[str], sampler: Sampler = multinomial() -) -> SequenceGeneratorAdapter: - regex_str = r"(" + r"|".join(choices) + r")" - - generator = regex(model, regex_str, sampler) - generator.format_sequence = lambda x: x - - return generator diff --git a/outlines/generate/format.py b/outlines/generate/format.py deleted file mode 100644 index ff7346b4..00000000 --- a/outlines/generate/format.py +++ /dev/null @@ -1,38 +0,0 @@ -from functools import singledispatch - -from outlines.fsm.types import python_types_to_regex -from outlines.generate.api import SequenceGeneratorAdapter -from outlines.samplers import Sampler, multinomial - -from .regex import regex - - -@singledispatch -def format( - model, python_type, sampler: Sampler = multinomial() -) -> SequenceGeneratorAdapter: - """Generate structured data that can be parsed as a Python type. - - Parameters - ---------- - model: - An instance of `Transformer` that represents a model from the - `transformers` library. - python_type: - A Python type. The output of the generator must be parseable into - this type. - sampler: - The sampling algorithm to use to generate token ids from the logits - distribution. - - Returns - ------- - A `SequenceGenerator` instance that generates text constrained by the Python type - and translates this text into the corresponding type. - - """ - regex_str, format_fn = python_types_to_regex(python_type) - generator = regex(model, regex_str, sampler) - generator.format_sequence = format_fn - - return generator diff --git a/outlines/generate/fsm.py b/outlines/generate/fsm.py deleted file mode 100644 index 2a128d96..00000000 --- a/outlines/generate/fsm.py +++ /dev/null @@ -1,22 +0,0 @@ -from functools import singledispatch - -import interegular - -from outlines.fsm.guide import RegexGuide -from outlines.generate.api import ( - SequenceGenerator, - SequenceGeneratorAdapter, - VisionSequenceGeneratorAdapter, -) -from outlines.samplers import Sampler, multinomial - - -@singledispatch -def fsm( - model, fsm: interegular.fsm.FSM, sampler: Sampler = multinomial() -) -> SequenceGeneratorAdapter: - from outlines.processors import FSMLogitsProcessor - - fsm = RegexGuide.from_interegular_fsm(fsm, model.tokenizer) - logits_processor = FSMLogitsProcessor(tokenizer=model.tokenizer, fsm=fsm) - return SequenceGeneratorAdapter(model, logits_processor, sampler) diff --git a/outlines/generate/generator.py b/outlines/generate/generator.py deleted file mode 100644 index e506aa03..00000000 --- a/outlines/generate/generator.py +++ /dev/null @@ -1,312 +0,0 @@ -import dataclasses -import math -from typing import TYPE_CHECKING, Callable, Iterable, Iterator, List, Optional, Tuple - -if TYPE_CHECKING: - import torch - - from outlines.fsm.guide import Guide - - -class ContextLengthExceededError(Exception): - pass - - -@dataclasses.dataclass(frozen=True) -class GenerationState: - token_ids: "torch.Tensor" - kv_cache: "torch.Tensor" - logits: "torch.Tensor" - weights: "torch.Tensor" - fsm_states: List[int] - - -def sequence_generator( - model: Callable, - sampler: Callable, - fsms: List["Guide"], - token_ids: "torch.Tensor", - sequence_weights: "torch.Tensor", - attention_masks: "torch.Tensor", - fsm_states: List[int], - rng: "torch.Generator", -) -> Iterator[GenerationState]: - """Generates sequences of tokens. - - Parameters - ---------- - model - A callable that generates a probability distribution over the - vocabulary when passed a tensor of token ids. - sampler - A callable that returns the next token ids, their ancestor sequence and - the updated sequence weights when passed a distribution over the - vocabulary. - token_ids - A tensor of token ids on which the sequence distribution is conditioned, of - shape ``(n_seqs, n_prompt_tokens)`` - sequence_weights - A tensor that contains the initial weights of the sequences, of shape - ``(n_seqs,)`` - attention_masks - A tensor of tensors that represent the tokens considered at the attention - layer, of shape ``(n_seqs, n_prompt_tokens)``. - fsms - List of finite-state machines that drive the text generation, - one for each sequence in the batch. - fsm_states - The initial states of the finite-state machine for each sequence in the batch. - - Yields - ------ - A new sequence. - - """ - import torch - - if rng is None: - rng = torch.Generator() - - kv_cache = None - - while True: - try: - logits, kv_cache = model(token_ids, attention_masks, kv_cache) - except IndexError: # Exceeding the context length - raise ContextLengthExceededError( - "The input length exceeds the context length of the model." - ) - - allowed_tokens = get_allowed_tokens(fsms, fsm_states) - biased_logits = bias_logits(logits, allowed_tokens) - next_token_ids, ancestors, sequence_weights = sampler( - biased_logits, sequence_weights, rng - ) - - token_ids = update_token_ids(token_ids, next_token_ids, ancestors) - attention_masks = update_attention_masks(attention_masks, ancestors) - kv_cache = reorder_kv_cache(kv_cache, ancestors) - if len(ancestors) > 1: - fsms = reorder_fsms(fsms, ancestors) - fsm_states = reorder_fsm_states(fsm_states, ancestors) - - fsm_states = get_next_fsm_states(fsms, fsm_states, next_token_ids) - is_finished = is_generation_finished(fsms, fsm_states) - - if is_finished: - yield GenerationState( - token_ids, - kv_cache, - logits, - sequence_weights, - fsm_states, - ) - return - - yield GenerationState( - token_ids, - kv_cache, - logits, - sequence_weights, - fsm_states, - ) - - -def get_next_fsm_states( - fsms: List["Guide"], fsm_states: List[int], next_token_ids: "torch.Tensor" -) -> List[int]: - """ - - Parameters - ---------- - fsm - The finite-state machine used to monitor this batch. - next_token_ids - The tokens that were just generated. - - Returns - ------- - A `torch.Tensor` object that represents the next logit mask. - - """ - return [ - fsm.get_next_state(fsm_state, int(token_id[0])) - for fsm, fsm_state, token_id in zip(fsms, fsm_states, next_token_ids) - ] - - -def get_allowed_tokens( - fsms: List["Guide"], fsm_states: List[int] -) -> List[Optional[Iterable[int]]]: - """Get the new instructions for each sequence from the finite-state machine. - - Parameters - ---------- - fsm - The finite-state machine used to monitor this batch. - fsm_states - The FSM states corresponding to each sequence in the batch. - - Returns - ------- - A nested list that contains the ids of the logits to keep. - - """ - return [ - fsm.get_next_instruction(state).tokens for fsm, state in zip(fsms, fsm_states) - ] - - -def is_generation_finished(fsms: List["Guide"], fsm_states: List[int]) -> bool: - """Determine if the generation is finished. - - A generation is considered finished if the FSM of every sequence in the - batch is in a final state. - - A better solution is to return finished sequences as soon as their FSM - is in a final state. - - Parameters - ---------- - fsm - The finite-state machine used to monitor this batch. - fsm_states - The FSM states corresponding to each sequence in the batch. - - Returns - ------- - Whether all sequences are finished sampling. - - """ - return all([fsm.is_final_state(state) for fsm, state in zip(fsms, fsm_states)]) - - -def update_token_ids( - token_ids: "torch.Tensor", next_token_ids: "torch.Tensor", ancestors: "torch.Tensor" -) -> "torch.Tensor": - """Append the sampled tokens to the running sequence of tokens. - - Parameters - ---------- - token_ids - The current token sequences - next_token_ids - The tokens that were just generated and that we need to append - to the existing sequences. - ancestors - The sequences to which the token ids need to be added. - - Returns - ------- - A new sequence of token ids that contains the tokens that were - just generated. - - """ - import torch - - token_ids = torch.index_select(token_ids, 0, ancestors) - return torch.concatenate([token_ids, next_token_ids], dim=-1) - - -def update_attention_masks( - attention_masks: "torch.Tensor", ancestors: "torch.Tensor" -) -> "torch.Tensor": - """Expand the attention masks. - - Parameters - ---------- - attention_masks - The attention masks for each sequence in the batch. - ancestors - The sequences to which the token ids need to be added. - - Returns - ------- - The attention masks padded with 1s. - - """ - import torch - - attention_masks = torch.index_select(attention_masks, 0, ancestors) - return torch.concatenate( - [ - attention_masks, - torch.ones( - attention_masks.shape[:-1] + (1,), device=attention_masks.device - ), - ], - axis=-1, - ) - - -def reorder_fsms(fsms: List["Guide"], ancestors: "torch.Tensor") -> List["Guide"]: - reordered_fsms = [] - for ancestor in ancestors: - reordered_fsms.append(fsms[ancestor].copy()) - - return reordered_fsms - - -def reorder_fsm_states(fsm_states: List[int], ancestors: "torch.Tensor") -> List[int]: - reordered_states = [] - for ancestor in ancestors: - reordered_states.append(fsm_states[ancestor]) - - return reordered_states - - -def reorder_kv_cache( - kv_cache: Optional[Tuple], ancestors: "torch.Tensor" -) -> Optional[Tuple]: - """Re-order the KV-cache based on the ancestors. - - In transformers, the object that stores the KV-cache is a tuple who elements - are the key cache and the value cache. Each of these caches are tuples where - each element correpond to a layer. To each layer corresponds a tensor whose - first dimension is the batch size. - - """ - import torch - - if kv_cache is None: - return None - - new_kv_cache: Tuple = tuple() - for cache_item in kv_cache: - new_cache_item: Tuple = tuple() - for layer in cache_item: - layer = torch.index_select(layer, 0, ancestors.to(layer.device)) - new_cache_item += (layer,) - new_kv_cache += (new_cache_item,) - - return new_kv_cache - - -def bias_logits(logits: "torch.Tensor", allowed_token_ids: List) -> "torch.Tensor": - """Mask the logits. - - The function iterates over a nested list where each list corresponds to the - indices that need to be masked for each row in the array. - - Parameters - ---------- - logits - Two dimensional tensor that contains the next-token probability - distribution. - allowed_token_ids - A list that contains the tokens that can be generated by the model. - - Returns - ------- - A view of the original logits tensor where some values are masked. - - """ - import torch - - biased_logits = torch.full_like(logits, -math.inf, device=logits.device) - for i, ids in enumerate(allowed_token_ids): - if ids is not None: - biased_logits[i, ids] = logits[i, ids] - else: - biased_logits[i] = logits[i] - return biased_logits diff --git a/outlines/generate/json.py b/outlines/generate/json.py deleted file mode 100644 index 04e8bfb7..00000000 --- a/outlines/generate/json.py +++ /dev/null @@ -1,67 +0,0 @@ -import json as pyjson -from functools import singledispatch -from typing import Callable, Optional, Union - -from pydantic import BaseModel - -from outlines.fsm.json_schema import build_regex_from_schema, get_schema_from_signature -from outlines.generate.api import SequenceGeneratorAdapter -from outlines.samplers import Sampler, multinomial - -from .regex import regex - - -@singledispatch -def json( - model, - schema_object: Union[str, object, Callable], - sampler: Sampler = multinomial(), - whitespace_pattern: Optional[str] = None, -) -> SequenceGeneratorAdapter: - """ - Generate structured JSON data with a `Transformer` model based on a specified JSON Schema. - - Parameters - ---------- - model: - An instance of `Transformer` that represents a model from the - `transformers` library. - schema_object: - The JSON Schema to generate data for. Can be a JSON string, a Pydantic model, or a callable - that returns a JSON schema. - sampler: - The sampling algorithm to use to generate token ids from the logits - distribution. - whitespace_pattern - Pattern to use for JSON syntactic whitespace (doesn't impact string literals) - Example: allow only a single space or newline with `whitespace_pattern=r"[\n ]?"` - - Returns - ------- - A `SequenceGenerator` instance that generates text constrained by the schema_object and - transforms the result if BaseModel is used. - - """ - if isinstance(schema_object, type(BaseModel)): - schema = pyjson.dumps(schema_object.model_json_schema()) - regex_str = build_regex_from_schema(schema, whitespace_pattern) - generator = regex(model, regex_str, sampler) - generator.format_sequence = lambda x: schema_object.parse_raw(x) - elif callable(schema_object): - schema = pyjson.dumps(get_schema_from_signature(schema_object)) - regex_str = build_regex_from_schema(schema, whitespace_pattern) - generator = regex(model, regex_str, sampler) - generator.format_sequence = lambda x: pyjson.loads(x) - elif isinstance(schema_object, str): - schema = schema_object - regex_str = build_regex_from_schema(schema, whitespace_pattern) - generator = regex(model, regex_str, sampler) - generator.format_sequence = lambda x: pyjson.loads(x) - else: - raise ValueError( - f"Cannot parse schema {schema_object}. The schema must be either " - + "a Pydantic object, a function or a string that contains the JSON " - + "Schema specification" - ) - - return generator diff --git a/outlines/generate/regex.py b/outlines/generate/regex.py deleted file mode 100644 index 78321ec8..00000000 --- a/outlines/generate/regex.py +++ /dev/null @@ -1,36 +0,0 @@ -from functools import singledispatch - -from outlines.fsm.guide import RegexGuide -from outlines.generate.api import ( - SequenceGenerator, - SequenceGeneratorAdapter, - VisionSequenceGeneratorAdapter, -) -from outlines.samplers import Sampler, multinomial - - -@singledispatch -def regex(model, regex_str: str, sampler: Sampler = multinomial()): - """Generate structured text in the language of a regular expression. - - Parameters - ---------- - model: - An instance of `Transformer` that represents a model from the - `transformers` library. - regex_str: - The regular expression that the output must follow. - sampler: - The sampling algorithm to use to generate token ids from the logits - distribution. - - Returns - ------- - A `SequenceGeneratorAdapter` instance that generates text constrained by the - regular expression. - - """ - from outlines.processors import RegexLogitsProcessor - - logits_processor = RegexLogitsProcessor(regex_str, tokenizer=model.tokenizer) - return SequenceGeneratorAdapter(model, logits_processor, sampler) diff --git a/outlines/generate/text.py b/outlines/generate/text.py deleted file mode 100644 index e5e00fe7..00000000 --- a/outlines/generate/text.py +++ /dev/null @@ -1,35 +0,0 @@ -from functools import singledispatch - -from outlines.fsm.guide import StopAtEOSGuide -from outlines.generate.api import ( - SequenceGenerator, - SequenceGeneratorAdapter, - VisionSequenceGeneratorAdapter, -) -from outlines.samplers import Sampler, multinomial - - -@singledispatch -def text(model, sampler: Sampler = multinomial()) -> SequenceGeneratorAdapter: - """Generate text with a `Transformer` model. - - Note - ---- - Python 3.11 allows dispatching on Union types and - this should greatly simplify the code. - - Arguments - --------- - model: - An instance of `Transformer` that represents a model from the - `transformers` library. - sampler: - The sampling algorithm to use to generate token ids from the logits - distribution. - - Returns - ------- - A `SequenceGeneratorAdapter` instance that generates text. - - """ - return SequenceGeneratorAdapter(model, None, sampler) diff --git a/outlines/models/transformers.py b/outlines/models/transformers.py index 7ecc9013..949d3c9c 100644 --- a/outlines/models/transformers.py +++ b/outlines/models/transformers.py @@ -4,7 +4,6 @@ from datasets.fingerprint import Hasher -from outlines.generate.api import GenerationParameters, SamplingParameters from outlines.models.tokenizer import Tokenizer if TYPE_CHECKING: @@ -19,6 +18,26 @@ KVCacheType = Tuple[Tuple["torch.DoubleTensor", "torch.DoubleTensor"], ...] +@dataclasses.dataclass(frozen=True) +class GenerationParameters: + """Generation parameters used in Outlines' public API.""" + + max_tokens: Optional[int] + stop_at: Optional[Union[str, List[str]]] + seed: Optional[int] + + +@dataclasses.dataclass(frozen=True) +class SamplingParameters: + """Sampling parameters available in Outlines.""" + + sampler: str + num_samples: int = 1 + top_p: Optional[float] = None + top_k: Optional[int] = None + temperature: Optional[float] = None + + def get_llama_tokenizer_types(): """Get all the Llama tokenizer types/classes that need work-arounds. diff --git a/outlines/samplers.py b/outlines/samplers.py deleted file mode 100644 index 8b64ed76..00000000 --- a/outlines/samplers.py +++ /dev/null @@ -1,324 +0,0 @@ -import math -from typing import TYPE_CHECKING, Callable, Optional, Protocol, Tuple - -if TYPE_CHECKING: - import torch - - -class Sampler(Protocol): - samples: int - - def __call__( - self, - next_token_logits: "torch.DoubleTensor", - sequence_weights: "torch.DoubleTensor", - rng: "torch.Generator", - ) -> "torch.DoubleTensor": - ... - - -class GreedySampler: - """Greedy Sampling algorithm. - - Greedy sampling consists in choosing the token with the largest - likelihood at every step. - - We don't allow more than one sample. We could attribute this a meaning, for - instance the k-th sample represents the k-th most likely token. In which - case it would be equivalent to beam search without the sequence weights. - - Attributes - ---------- - samples - The number of samples taken for each input sequence. - - """ - - def __init__(self): - self.samples = 1 - - def __call__( - self, - next_token_logits: "torch.DoubleTensor", - sequence_weights: "torch.DoubleTensor", - _, - ) -> "torch.DoubleTensor": - """Call the greedy sampler. - - Parameters - ---------- - next_token_logits - A tensor of shape ``(n_seqs, vocab_size,)`` that represents the - probability distribution of the next token over the vocabulary. - sequence_weights - A tensor of shape ``(n_seqs,)`` that represents the cumulative - weight of each sequence. - rng - A random number generator. - - Returns - ------- - A tuple with an array that contains the ids of the sampled tokens of - shape ``(n_seqs, 1)``, an array that contains the ancestors of each - sampled id of shape ``(n_seqs,)`` and an array that contains the updated - cumulative weights of each sequence of shape ``(n_seqs,)``. - - """ - import torch - - logprobs = torch.nn.functional.log_softmax(next_token_logits, dim=-1) - next_token_ids = torch.argmax(logprobs, dim=-1, keepdim=True) - - ancestors = torch.arange( - next_token_logits.shape[0], device=next_token_logits.device - ) - weights = sequence_weights + torch.gather(logprobs, 1, next_token_ids).squeeze() - - return next_token_ids, ancestors, weights - - -greedy = GreedySampler - - -class MultinomialSampler: - """Multinomial sampling algorithm. - - Multinomial sampling consists in randomly sampling the next token assuming - its distribution is a Categorical distribution parametrized by the - next-token logits. - - - Attributes - ---------- - samples - The number of samples taken for each input sequence. - - """ - - def __init__( - self, - samples: int = 1, - *, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: Optional[float] = None, - ): - self.samples = samples - self.top_k = top_k - self.top_p = top_p - self.temperature = temperature - - self.logits_processors = [] - if top_k is not None: - self.logits_processors.append(keep_top_k_logits(top_k)) - elif top_p is not None: - self.logits_processors.append(keep_top_p_logits(top_p)) - - if temperature is not None: - self.logits_processors.append(rescale_logits(temperature)) - - def __call__( - self, - next_token_logits: "torch.DoubleTensor", - sequence_weights: "torch.DoubleTensor", - rng: "torch.Generator", - ) -> Tuple["torch.DoubleTensor", "torch.DoubleTensor", "torch.DoubleTensor"]: - """Call the multinomial sampler. - - Parameters - ---------- - next_token_logits - A tensor of shape ``(n_seqs, vocab_size,)`` that represents the - probability distribution of the next token over the vocabulary. - sequence_weights - A tensor of shape ``(n_seqs,)`` that represents the cumulative - weight of each sequence. - rng - A random number generator. - - Returns - ------- - A tuple with an array that contains the ids of the sampled tokens of - shape ``(n_seqs, 1)``, an array that contains the ancestors of each - sampled id of shape ``(n_seqs,)`` and an array that contains the updated - cumulative weights of each sequence of shape ``(n_seqs,)``. - - """ - import torch - - altered_next_token_logits = next_token_logits - for logit_processor in self.logits_processors: - altered_next_token_logits = logit_processor(next_token_logits) - - probs = torch.nn.functional.softmax(altered_next_token_logits, dim=-1) - next_token_ids = torch.multinomial(probs, num_samples=1, generator=rng) - - logprobs = torch.nn.functional.log_softmax(altered_next_token_logits, dim=-1) - ancestors = torch.arange( - altered_next_token_logits.shape[0], device=next_token_logits.device - ) - weights = sequence_weights + torch.gather(logprobs, 1, next_token_ids).squeeze() - - return next_token_ids, ancestors, weights - - -multinomial = MultinomialSampler - - -def keep_top_k_logits(k: int) -> Callable[["torch.Tensor"], "torch.Tensor"]: - """Build a function that masks logits values smaller than the top `k` ones. - - Parameters - ---------- - k - The ranking below which logit values are replaced by `-math.inf`. - - """ - import torch - - if not isinstance(k, int) or k < 1: - raise ValueError(f"`k` must be a strictly positive integers, got {k} instead.") - - def logits_processor(logits: torch.Tensor) -> torch.Tensor: - num_to_keep = min(k, logits.size(-1)) - mask_idx = logits < torch.topk(logits, num_to_keep)[0][..., -1, None] - return logits.masked_fill(mask_idx, -math.inf) - - return logits_processor - - -def keep_top_p_logits(p: float) -> Callable[["torch.Tensor"], "torch.Tensor"]: - """Build a function that masks the lowest probability tokens whose - cumulative probability is below a certain threshold. - - Parameters - ---------- - p - The value of the threshold. We keep the highest probability tokens whose - cumulative distribution is greater than or equal to `p` and mask the - others. Its value must be between 0 (excluded) and 1 (included). - - """ - import torch - - if p <= 0.0 or p > 1.0: - raise ValueError( - f"`p` must be a floating point number between 0 (excluded) and 1 (included), got {p} instead." - ) - - def logits_processor(logits: torch.Tensor) -> torch.Tensor: - sorted_logits, sorted_idx = torch.sort(logits, descending=False) - cumulative_probabilties = torch.nn.functional.softmax( - sorted_logits, dim=-1 - ).cumsum(dim=-1) - - sorted_masked_idx = cumulative_probabilties <= (1 - p) - mask_idx = torch.scatter(sorted_masked_idx, 1, sorted_idx, sorted_masked_idx) - return logits.masked_fill(mask_idx, -math.inf) - - return logits_processor - - -def rescale_logits(temperature: float) -> Callable[["torch.Tensor"], "torch.Tensor"]: - """Build a function that rescales the token probabilities exponentially. - - Parameters - ---------- - temperature - The value by which we rescale the logits. - - """ - - if not isinstance(temperature, float) or temperature < 0.0: - raise ValueError( - f"`temperature` must be a strictly positive floating point number, got {temperature} instead." - ) - elif temperature == 0.0: - raise ValueError( - "Please use the greedy sampler instead of setting the temperature to 0." - ) - - def logits_processor(logits: "torch.Tensor") -> "torch.Tensor": - return logits / temperature - - return logits_processor - - -class BeamSearchSampler: - """Beam Search sampling algorithm. - - Attributes - ---------- - samples - The number of samples taken for each input sequence. - - """ - - def __init__(self, beams: int = 1): - self.samples = beams - - def __call__( - self, - next_token_logits: "torch.DoubleTensor", - sequence_weights: "torch.DoubleTensor", - _, - ) -> Tuple["torch.DoubleTensor", "torch.DoubleTensor", "torch.DoubleTensor"]: - """Call the beam search sampler. - - Parameters - ---------- - next_token_logits - A tensor of shape ``(n_seqs, vocab_size,)`` that represents the - probability distribution of the next token over the vocabulary. - sequence_weights - A tensor of shape ``(n_seqs,)`` that represents the cumulative - weight of each sequence. - rng - A random number generator. - - Returns - ------- - A tuple with an array that contains the ids of the sampled tokens of - shape ``(n_seqs, 1)``, an array that contains the ancestors of each - sampled id of shape ``(n_seqs,)`` and an array that contains the updated - cumulative weights of each sequence of shape ``(n_seqs,)``. - - """ - import torch - - logprobs = torch.nn.functional.log_softmax(next_token_logits, dim=-1) - weights = logprobs + sequence_weights.unsqueeze(1).expand_as(next_token_logits) - - # Flatten scores to (n_batch, n_samples * vocab_size) - # and find the top-k weights for each batch. - batch_size = next_token_logits.shape[0] // self.samples - vocab_size = next_token_logits.shape[-1] - weights = weights.view(batch_size, self.samples * vocab_size) - - # If the weights are all equal to 0 we are at the beginning of the search - # and thus only need to sample from one set of token logits for each - # batch. - if torch.all(sequence_weights == 0): - weights = weights[:, :vocab_size] - - weights, indices = torch.topk( - weights, self.samples, dim=1, largest=True, sorted=True - ) - - ancestors = torch.div(indices, vocab_size, rounding_mode="floor") - next_token_ids = indices % vocab_size - - # Re-shape the weights, next_token_ids and ancestors to (n_batch * n_samples, 1) - first_batch_idx = torch.arange( - 0, batch_size * self.samples, self.samples, device=next_token_logits.device - ).unsqueeze(1) - ancestors = ancestors + first_batch_idx - - ancestors = ancestors.view(self.samples * batch_size) - weights = weights.view(self.samples * batch_size) - next_token_ids = next_token_ids.view(self.samples * batch_size, 1) - - return next_token_ids, ancestors, weights - - -beam_search = BeamSearchSampler diff --git a/tests/generate/__init__.py b/tests/generate/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/generate/conftest.py b/tests/generate/conftest.py deleted file mode 100644 index ed883011..00000000 --- a/tests/generate/conftest.py +++ /dev/null @@ -1,64 +0,0 @@ -from importlib import reload - -import pytest -import torch - - -def is_metal_available(): - try: - import mlx.core as mx - import mlx_lm # noqa: F401 - - assert mx.metal.is_available() - except (ImportError, AssertionError): - return False - return True - - -def pytest_collection_modifyitems(config, items): - """ - If mlxlm and Metal aren't available, skip mlxlm tests - If CUDA isn't available, skip vllm and transformers_vision - """ - if not torch.cuda.is_available(): - skip_marker = pytest.mark.skip( - reason="Skipping test because CUDA is not available" - ) - for item in items: - if "model_fixture" in item.fixturenames: - model_param = item.callspec.params.get("model_fixture", None) - if model_param.startswith( - "model_transformers_vision" - ) or model_param.startswith("model_vllm"): - item.add_marker(skip_marker) - - if not is_metal_available(): - skip_marker = pytest.mark.skip( - reason="Skipping test because mlx-lm or Metal are not available" - ) - for item in items: - if "model_fixture" in item.fixturenames: - model_param = item.callspec.params.get("model_fixture", None) - if model_param.startswith("model_mlxlm"): - item.add_marker(skip_marker) - - -@pytest.fixture -def temp_cache_dir(): - import os - import tempfile - - import outlines.caching - import outlines.fsm.guide - - with tempfile.TemporaryDirectory() as tempdir: - os.environ["OUTLINES_CACHE_DIR"] = tempdir - outlines.caching.get_cache.cache_clear() - reload(outlines) - reload(outlines.fsm.guide) - cache_status = outlines.caching._caching_enabled - try: - outlines.caching._caching_enabled = True - yield - finally: - outlines.caching._caching_enabled = cache_status diff --git a/tests/generate/test_api.py b/tests/generate/test_api.py deleted file mode 100644 index 7188022f..00000000 --- a/tests/generate/test_api.py +++ /dev/null @@ -1,33 +0,0 @@ -from io import BytesIO -from urllib.request import urlopen - -import pytest -from PIL import Image # type: ignore - -from outlines.generate.api import VisionSequenceGeneratorAdapter - -IMG_URI = "https://upload.wikimedia.org/wikipedia/en/a/a9/Example.jpg" -PIL_IMG = Image.open(BytesIO(urlopen(IMG_URI).read())).convert("RGB") - - -@pytest.mark.parametrize( - "prompts,media,type_error", - [ - ("single prompt", [PIL_IMG], False), - (["prompt0", "prompt1"], [[PIL_IMG], [PIL_IMG]], False), - ("single prompt", [PIL_IMG, PIL_IMG], False), - (["prompt0", "prompt1"], [[PIL_IMG, PIL_IMG], [PIL_IMG]], False), - ("single prompt", "this isn't an image, it's a string", True), - ("single prompt", PIL_IMG, True), - (["prompt0", "prompt1"], [PIL_IMG], True), - (["prompt0", "prompt1"], [[PIL_IMG]], True), - (["prompt0", "prompt1"], [[[PIL_IMG]], [[PIL_IMG]]], True), - ], -) -def test_vision_sequence_generator_validate_types(prompts, media, type_error): - """Ensure inputs are validated correctly""" - if type_error: - with pytest.raises(TypeError): - VisionSequenceGeneratorAdapter._validate_prompt_media_types(prompts, media) - else: - VisionSequenceGeneratorAdapter._validate_prompt_media_types(prompts, media) diff --git a/tests/generate/test_generate.py b/tests/generate/test_generate.py deleted file mode 100644 index 9e16b2dd..00000000 --- a/tests/generate/test_generate.py +++ /dev/null @@ -1,303 +0,0 @@ -import contextlib -import re - -import pytest - -import outlines.generate as generate -import outlines.models as models -import outlines.samplers as samplers - -########################################## -# Model Fixtures -########################################## - - -@pytest.fixture(scope="session") -def model_transformers_random(tmp_path_factory): - return models.transformers("hf-internal-testing/tiny-random-gpt2", device="cpu") - - -@pytest.fixture(scope="session") -def model_transformers_opt125m(tmp_path_factory): - return models.transformers("facebook/opt-125m", device="cpu") - - -@pytest.fixture(scope="session") -def model_bart(tmp_path_factory): - from transformers import AutoModelForSeq2SeqLM - - return models.transformers( - "facebook/bart-base", device="cpu", model_class=AutoModelForSeq2SeqLM - ) - - -@pytest.fixture(scope="session") -def model_transformers_vision(tmp_path_factory): - import torch - from transformers import LlavaNextForConditionalGeneration - - return models.transformers_vision( - "llava-hf/llava-v1.6-mistral-7b-hf", - model_class=LlavaNextForConditionalGeneration, - device="cuda", - model_kwargs=dict( - torch_dtype=torch.bfloat16, - load_in_4bit=True, - low_cpu_mem_usage=True, - ), - ) - - - -# TODO: exllamav2 failing in main, address in https://github.com/outlines-dev/outlines/issues/808 -# TODO: t5 tokenizer doesn't work with streaming -""" -@pytest.fixture(scope="session") -def model_exllamav2(tmp_path_factory): - return models.exllamav2( - model_path="blockblockblock/TinyLlama-1.1B-Chat-v1.0-bpw4-exl2", - device="cpu" - ) - -@pytest.fixture(scope="session") -def model_t5(tmp_path_factory): - from transformers import AutoModelForSeq2SeqLM - - return models.transformers( - "EleutherAI/pile-t5-base", device="cpu", model_class=AutoModelForSeq2SeqLM - ) -""" - - -ALL_MODEL_FIXTURES = ( - "model_transformers_random", - "model_transformers_opt125m", - "model_bart", -) - - -########################################## -# Stuctured Generation Inputs -########################################## - - -@pytest.fixture() -def sample_schema(): - from pydantic import BaseModel, conint, conlist, constr - - class SampleSchema(BaseModel): - title: constr(max_length=10) - numbers: conlist(conint(strict=True), min_length=3, max_length=3) - labels: conlist(constr(min_length=1, max_length=5), min_length=3, max_length=3) - - return SampleSchema - - -@pytest.fixture() -def sample_choices(): - return ["foo", "bar", "baz"] - - -REGEX_PATTERNS = [ - "a b c d e", # ensure proper tokenizer whitespace prefix handling - "(123456789)|(abcdefghijklmnop)", # ensure consistent correct sequence handling during batch - r"([a-z]{10})@([a-z]{5})\.([a-z]{3})", # email example -] - - -########################################### -# Model/Generator Pair Behavior Definitions -########################################### - - -def enforce_not_implemented(model_fixture, *task_names): - """ - Per `NOT_IMPLEMENTED`, mapping, if a model hasn't implemented a task, - assert an NotImplementedError is raised. Otherwise, run normally - """ - NOT_IMPLEMENTED = { - "stream": ["model_transformers_vision"], - "batch": [], - "beam_search": [], - "multiple_samples": [], - } - for task_name in task_names: - if model_fixture in NOT_IMPLEMENTED.get(task_name, []): - return pytest.raises(NotImplementedError) - else: - return contextlib.nullcontext() - - -def get_inputs(fixture_name, batch_size=None): - """Get generator kwargs, just the prompt by default, but include images for transformers_visian""" - from io import BytesIO - from urllib.request import urlopen - - from PIL import Image # type: ignore - - prompts = ["abcd", "efgh", "1234", "5678", "foo", "bar", "baz", "bif"] - prompts = prompts[0] if batch_size is None else prompts[:batch_size] - - if fixture_name.endswith("_vision"): - img_url = "https://python-pillow.org/pillow-perf/static/space_pil_lanczos.png" - img = Image.open(BytesIO(urlopen(img_url).read())).convert("RGB") - - if batch_size is None: - return {"prompts": f" {prompts}", "media": [img]} - else: - return { - "prompts": [f" {p}" for p in prompts], - "media": [[img] for _ in range(batch_size)], - } - - else: - return {"prompts": prompts} - - -########################################### -# Tests -########################################### - - -@pytest.mark.parametrize("sampler_name", ("greedy", "multinomial", "beam_search")) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_text(request, model_fixture, sampler_name): - model = request.getfixturevalue(model_fixture) - generator = generate.text(model, getattr(samplers, sampler_name)()) - with enforce_not_implemented(model_fixture, sampler_name): - res = generator(**get_inputs(model_fixture), max_tokens=10) - assert isinstance(res, str) - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_regex(request, model_fixture, pattern): - model = request.getfixturevalue(model_fixture) - generator = generate.regex(model, pattern) - res = generator(**get_inputs(model_fixture), max_tokens=20) - assert re.fullmatch(pattern, res) is not None, res - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_fsm(request, model_fixture, pattern): - import interegular - - model = request.getfixturevalue(model_fixture) - generator = generate.fsm(model, interegular.parse_pattern(pattern).to_fsm()) - res = generator(**get_inputs(model_fixture)) - assert re.fullmatch(pattern, res) is not None, res - - -@pytest.mark.skip( - "Fix issues with JSON, some models fail this test https://github.com/outlines-dev/outlines/issues/985" -) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_json(request, model_fixture, sample_schema): - model = request.getfixturevalue(model_fixture) - generator = generate.json(model, sample_schema) - # asserts valid within call - generator(**get_inputs(model_fixture), max_tokens=100) - - -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_choice(request, model_fixture, sample_choices): - model = request.getfixturevalue(model_fixture) - generator = generate.choice(model, sample_choices) - res = generator(**get_inputs(model_fixture)) - assert res in sample_choices - - -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_format_bool(request, model_fixture): - model = request.getfixturevalue(model_fixture) - generator = generate.format(model, bool) - res = generator(**get_inputs(model_fixture)) - assert isinstance(res, bool) - - -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_text_stream(request, model_fixture): - model = request.getfixturevalue(model_fixture) - generator = generate.text(model) - with enforce_not_implemented(model_fixture, "stream"): - for token in generator.stream(**get_inputs(model_fixture), max_tokens=10): - assert isinstance(token, str) - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_regex_stream(request, model_fixture, pattern): - model = request.getfixturevalue(model_fixture) - generator = generate.regex(model, pattern) - with enforce_not_implemented(model_fixture, "stream"): - output = "" - for token in generator.stream(**get_inputs(model_fixture), max_tokens=20): - output += token - assert re.fullmatch(pattern, output) is not None, output - - -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_batch_text(request, model_fixture): - model = request.getfixturevalue(model_fixture) - generator = generate.text(model) - with enforce_not_implemented(model_fixture, "batch"): - res = generator(**get_inputs(model_fixture, 2), max_tokens=10) - assert isinstance(res, list) - assert isinstance(res[0], str) - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_regex_batch(request, model_fixture, pattern): - """Ensure batch requests work and fsm order is maintained""" - model = request.getfixturevalue(model_fixture) - generator = generate.regex(model, pattern) - with enforce_not_implemented(model_fixture, "batch"): - outputs = generator(**get_inputs(model_fixture, 4), max_tokens=20) - for output in outputs: - assert re.fullmatch(pattern, output) is not None, output - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_regex_batch_stream(request, model_fixture, pattern): - model = request.getfixturevalue(model_fixture) - generator = generate.regex(model, pattern) - with enforce_not_implemented(model_fixture, "batch", "stream"): - outputs = ["", ""] - for tokens in generator.stream(**get_inputs(model_fixture, 2), max_tokens=20): - outputs[0] += tokens[0] - outputs[1] += tokens[1] - for output in outputs: - assert re.fullmatch(pattern, output) is not None, output - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -def test_generate_regex_single_multinomial(request, model_fixture, pattern): - """Ensure batch requests work and fsm order is maintained""" - model = request.getfixturevalue(model_fixture) - generator = generate.regex(model, pattern, sampler=samplers.multinomial(4)) - with enforce_not_implemented(model_fixture, "multiple_samples"): - output_sample_groups = generator(**get_inputs(model_fixture), max_tokens=40) - for output in output_sample_groups: - assert re.fullmatch(pattern, output) is not None, output - - -@pytest.mark.parametrize("pattern", REGEX_PATTERNS) -@pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES) -@pytest.mark.parametrize("sampler_name", ("multinomial", "beam_search")) -def test_generate_regex_batch_multi_sample( - request, model_fixture, pattern, sampler_name -): - """Ensure batch requests work and fsm order is maintained""" - model = request.getfixturevalue(model_fixture) - generator = generate.regex( - model, pattern, sampler=getattr(samplers, sampler_name)(4) - ) - with enforce_not_implemented(model_fixture, "batch", "multiple_samples"): - output_batch_groups = generator(**get_inputs(model_fixture, 4), max_tokens=40) - for output_sample_groups in output_batch_groups: - for output in output_sample_groups: - assert re.fullmatch(pattern, output) is not None, output diff --git a/tests/generate/test_generator.py b/tests/generate/test_generator.py deleted file mode 100644 index 5a2edf8d..00000000 --- a/tests/generate/test_generator.py +++ /dev/null @@ -1,497 +0,0 @@ -import math -from typing import Generator - -import pytest -import torch - -from outlines.fsm.guide import Generate -from outlines.generate.api import SequenceGenerator -from outlines.generate.generator import ( - bias_logits, - get_allowed_tokens, - get_next_fsm_states, - is_generation_finished, - sequence_generator, - update_attention_masks, - update_token_ids, -) - - -def test_sequence_generator_class(): - class MockFSM: - first_state = 0 - - def get_next_state(self, state, next_token_ids): - return 4 - - def get_next_instruction(self, *_): - return Generate([4]) - - def is_final_state(self, _): - return True - - def copy(self): - return self - - class MockTokenizer: - def encode(self, _): - # Input: "test" - return torch.tensor([[0, 1, 2, 3]]), torch.tensor([[1, 1, 1, 1]]) - - def decode(self, tokens): - return ["testx"[i] for i in tokens] - - class MockModel: - def __init__(self): - self.tokenizer = MockTokenizer() - - def __call__(*_): - return torch.tensor([[0, 1, 2, 3, 4]], dtype=torch.float), None - - class sampler: - def __init__(self): - self.samples = 1 - - def __call__(self, biased_logits, *_): - return torch.argmax(biased_logits, keepdims=True), torch.tensor([0]), None - - # Stream - generator = SequenceGenerator(MockFSM(), MockModel(), sampler(), "cpu") - assert generator.device == "cpu" - assert isinstance(generator.tokenizer, MockTokenizer) - assert isinstance(generator.fsm, MockFSM) - - sequence = generator.stream("test") - assert isinstance(sequence, Generator) - - next(sequence) - - with pytest.raises(StopIteration): - next(sequence) - - # Call - generator = SequenceGenerator(MockFSM(), MockModel(), sampler(), "cpu") - result = generator("test") - assert result == "x" - - -def test_sequence_generator_1d_single_iteration(): - class MockFSM: - def get_next_state(self, state, next_token_ids): - return 0 - - def get_next_instruction(self, _): - return Generate([0, 1, 2, 3]) - - def is_final_state(self, _): - return True - - def copy(self): - return self - - class MockTokenizer: - def encode(self, _): - return torch.tensor([[0, 1, 2, 3]]), torch.tensor([[1, 1, 1, 1]]) - - def decode(self, x): - return x - - class MockModel: - def __init__(self): - self.tokenizer = MockTokenizer() - - def __call__(*_): - return torch.tensor([[0, 1, 2, 3]], dtype=torch.float), None - - def sampler(biased_logits, *_): - return torch.argmax(biased_logits, keepdims=True), torch.tensor([0]), None - - token_ids, attention_mask = ( - torch.tensor([[0, 1, 2, 3]]), - torch.tensor([[1, 1, 1, 1]]), - ) - init_fsm_states = [0] - sequence = sequence_generator( - MockModel(), - sampler, - [MockFSM()], - token_ids, - torch.tensor([0]), - attention_mask, - init_fsm_states, - rng=torch.Generator(), - ) - result = next(sequence) - - assert torch.equal(result.token_ids, torch.tensor([[0, 1, 2, 3, 3]])) - assert torch.equal(result.logits, torch.tensor([[0, 1, 2, 3]])) - - with pytest.raises(StopIteration): - next(sequence) - - -def test_sequence_generator_1d_several_iterations(): - class MockFSM: - def get_next_state(self, state, next_token_ids): - return state + 1 - - def get_next_instruction(self, _): - return Generate([0, 1, 2, 3]) - - def is_final_state(self, state): - if state < 2: - return False - else: - return True - - def copy(self): - return self - - class MockTokenizer: - def encode(self, _): - return torch.tensor([[0, 1, 2, 3]]), torch.tensor([[1, 1, 1, 1]]) - - def decode(self, x): - return x - - class MockModel: - def __init__(self): - self.tokenizer = MockTokenizer() - - def __call__(*_): - return torch.tensor([[0, 1, 2, 3]], dtype=torch.float), None - - def sampler(biased_logits, *_): - return torch.argmax(biased_logits, keepdims=True), torch.tensor([0]), None - - token_ids, attention_mask = ( - torch.tensor([[0, 1, 2, 3]]), - torch.tensor([[1, 1, 1, 1]]), - ) - init_fsm_states = [0] - sequence = sequence_generator( - MockModel(), - sampler, - [MockFSM()], - token_ids, - torch.tensor([0]), - attention_mask, - init_fsm_states, - rng=torch.Generator(), - ) - - result = next(sequence) - assert torch.equal(result.token_ids, torch.tensor([[0, 1, 2, 3, 3]])) - assert torch.equal(result.logits, torch.tensor([[0, 1, 2, 3]])) - - result = next(sequence) - assert torch.equal(result.token_ids, torch.tensor([[0, 1, 2, 3, 3, 3]])) - assert torch.equal(result.logits, torch.tensor([[0, 1, 2, 3]])) - - with pytest.raises(StopIteration): - next(sequence) - - -def test_sequence_generator_2d_single_iteration(): - class MockFSM: - def get_next_state(self, state, next_token_ids): - return 0 - - def get_next_instruction(self, _): - return Generate([0, 1, 2, 3]) - - def is_final_state(self, _): - return True - - def copy(self): - return self - - class MockTokenizer: - def encode(self, _): - return torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]), torch.tensor( - [[1, 1, 1, 1], [1, 1, 1, 1]] - ) - - def decode(self, x): - return x - - class MockModel: - def __init__(self): - self.tokenizer = MockTokenizer() - - def __call__(*_): - return torch.tensor([[0, 1, 2, 3], [4, 5, 7, 6]], dtype=torch.float), None - - def sampler(biased_logits, *_): - return ( - torch.argmax(biased_logits, keepdims=True, dim=-1), - torch.tensor([0, 1]), - None, - ) - - token_ids, attention_mask = ( - torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]), - torch.tensor([[1, 1, 1, 1], [1, 1, 1, 1]]), - ) - init_fsm_states = [0, 0] - fsms = [MockFSM(), MockFSM()] - sequence = sequence_generator( - MockModel(), - sampler, - fsms, - token_ids, - torch.tensor([0, 0]), - attention_mask, - init_fsm_states, - rng=torch.Generator(), - ) - - result = next(sequence) - assert torch.equal( - result.token_ids, torch.tensor([[0, 1, 2, 3, 3], [4, 5, 6, 7, 2]]) - ) - assert torch.equal( - result.logits, torch.tensor([[0, 1, 2, 3], [4, 5, 7, 6]], dtype=torch.float) - ) - - with pytest.raises(StopIteration): - next(sequence) - - -def test_sequence_generator_2d_several_iterations(): - class MockFSM: - def get_next_state(self, state, next_token_ids): - return state + 1 - - def get_next_instruction(self, _): - return Generate([0, 1, 2, 3]) - - def is_final_state(self, state): - if state < 2: - return False - else: - return True - - def copy(self): - return self - - class MockTokenizer: - def encode(self, _): - return torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]), torch.tensor( - [[1, 1, 1, 1], [1, 1, 1, 1]] - ) - - def decode(self, x): - return x - - class MockModel: - def __init__(self): - self.tokenizer = MockTokenizer() - - def __call__(*_): - return torch.tensor([[0, 1, 2, 3], [4, 5, 7, 6]], dtype=torch.float), None - - def sampler(biased_logits, *_): - return ( - torch.argmax(biased_logits, keepdims=True, dim=-1), - torch.tensor([0, 1]), - None, - ) - - token_ids, attention_mask = ( - torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]), - torch.tensor([[1, 1, 1, 1], [1, 1, 1, 1]]), - ) - init_fsm_states = [0, 0] - fsms = [MockFSM(), MockFSM()] - sequence = sequence_generator( - MockModel(), - sampler, - fsms, - token_ids, - torch.tensor([0, 0]), - attention_mask, - init_fsm_states, - rng=torch.Generator(), - ) - - result = next(sequence) - assert torch.equal( - result.token_ids, torch.tensor([[0, 1, 2, 3, 3], [4, 5, 6, 7, 2]]) - ) - assert torch.equal( - result.logits, torch.tensor([[0, 1, 2, 3], [4, 5, 7, 6]], dtype=torch.float) - ) - - result = next(sequence) - assert torch.equal( - result.token_ids, torch.tensor([[0, 1, 2, 3, 3, 3], [4, 5, 6, 7, 2, 2]]) - ) - assert torch.equal( - result.logits, torch.tensor([[0, 1, 2, 3], [4, 5, 7, 6]], dtype=torch.float) - ) - - with pytest.raises(StopIteration): - next(sequence) - - -def test_get_next_fsm_states(): - class MockFSM: - def get_next_state(self, state, next_token_ids): - return 0 - - def copy(self): - return self - - result = get_next_fsm_states([MockFSM()], [0], torch.tensor([[0]])) - assert result == [0] - - result = get_next_fsm_states( - [MockFSM(), MockFSM()], [0, 0], torch.tensor([[0], [0]]) - ) - assert result == [0, 0] - - -def test_get_get_next_instructions(): - class MockFSM: - def get_next_instruction(self, _): - return Generate([1, 2, 3, 4]) - - result = get_allowed_tokens([MockFSM()], [0]) - assert result == [[1, 2, 3, 4]] - - result = get_allowed_tokens([MockFSM(), MockFSM()], [0, 1]) - assert result == [[1, 2, 3, 4], [1, 2, 3, 4]] - - -def test_is_generation_finished(): - class MockFSMFinished: - def is_final_state(self, _): - return True - - result = is_generation_finished([MockFSMFinished(), MockFSMFinished()], [1, 1]) - assert result is True - - class MockFSMNotFinished: - def is_final_state(self, state): - if state == 0: - return False - else: - return True - - result = is_generation_finished( - [MockFSMNotFinished(), MockFSMNotFinished()], [0, 1] - ) - assert result is False - - -@pytest.mark.parametrize( - "token_ids,next_token_ids,ancestors,expected_result", - [ - ( - torch.tensor([[1]]), - torch.tensor([[2]]), - torch.tensor([0]), - torch.tensor([[1, 2]]), - ), - ( - torch.tensor([[1], [2]]), - torch.tensor([[3], [4]]), - torch.tensor([0, 1]), - torch.tensor([[1, 3], [2, 4]]), - ), - ( - torch.tensor([[1], [2]]), - torch.tensor([[3], [4]]), - torch.tensor([0, 0]), - torch.tensor([[1, 3], [1, 4]]), - ), - ( - torch.tensor([[1], [2]]), - torch.tensor([[3], [4]]), - torch.tensor([1, 0]), - torch.tensor([[2, 3], [1, 4]]), - ), - ( - torch.tensor([[1, 2], [3, 5]]), - torch.tensor([[3], [4]]), - torch.tensor([1, 0]), - torch.tensor([[3, 5, 3], [1, 2, 4]]), - ), - ], -) -def test_update_token_ids(token_ids, next_token_ids, ancestors, expected_result): - result = update_token_ids(token_ids, next_token_ids, ancestors) - assert torch.equal(result, expected_result) - - -@pytest.mark.parametrize( - "attention_masks,ancestors,expected_result", - [ - ( - torch.tensor([[1, 1]], dtype=torch.float), - torch.tensor([0]), - torch.tensor([[1, 1, 1]], dtype=torch.float), - ), - ( - torch.tensor([[1, 1], [1, 1]], dtype=torch.float), - torch.tensor([0, 0]), - torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.float), - ), - ( - torch.tensor([[0, 1], [1, 1]], dtype=torch.float), - torch.tensor([0, 1]), - torch.tensor([[0, 1, 1], [1, 1, 1]], dtype=torch.float), - ), - ( - torch.tensor([[0, 1], [1, 1]], dtype=torch.float), - torch.tensor([1, 0]), - torch.tensor([[1, 1, 1], [0, 1, 1]], dtype=torch.float), - ), - ( - torch.tensor([[0, 1], [1, 1]], dtype=torch.float), - torch.tensor([1, 1]), - torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.float), - ), - ], -) -def test_expand_attention_masks(attention_masks, ancestors, expected_result): - result = update_attention_masks(attention_masks, ancestors) - assert torch.equal(result, expected_result) - - -@pytest.mark.parametrize( - "logits,indices_to_mask,expected", - [ - ( - torch.tensor([[1, 2, 3, 4]], dtype=torch.float), - [[0, 1, 2, 3]], - torch.tensor([[1, 2, 3, 4]], dtype=torch.float), - ), - ( - torch.tensor([[1, 2, 3, 4]], dtype=torch.float), - [[0, 2, 3]], - torch.tensor([[1, -math.inf, 3, 4]], dtype=torch.float), - ), - ( - torch.tensor([[1, 2, 3, 4]], dtype=torch.float), - [[0, 2]], - torch.tensor([[1, -math.inf, 3, -math.inf]], dtype=torch.float), - ), - ( - torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float), - [[1, 2], [0, 1]], - torch.tensor([[-math.inf, 2, 3], [4, 5, -math.inf]], dtype=torch.float), - ), - ( - torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float), - [[0, 2], [1]], - torch.tensor( - [[1, -math.inf, 3], [-math.inf, 5, -math.inf]], dtype=torch.float - ), - ), - ], -) -def test_bias_logits(logits, indices_to_mask, expected): - masked_logits = bias_logits(logits, indices_to_mask) - assert torch.equal(masked_logits, expected) diff --git a/tests/generate/test_integration_transformers.py b/tests/generate/test_integration_transformers.py deleted file mode 100644 index f3fb9682..00000000 --- a/tests/generate/test_integration_transformers.py +++ /dev/null @@ -1,544 +0,0 @@ -import datetime -import re -from enum import Enum -from typing import List, Union - -import pytest -import torch -from pydantic import BaseModel, constr - -import outlines.generate as generate -import outlines.models as models -from outlines.fsm.regex import reduced_vocabulary -from outlines.models.transformers import Transformers, TransformerTokenizer -from outlines.samplers import beam_search, greedy, multinomial - - -@pytest.fixture(scope="session") -def model(tmp_path_factory): - return models.transformers( - "hf-internal-testing/tiny-random-GPTJForCausalLM", device="cpu" - ) - - -def test_transformers_integration_text(model): - sequence = generate.text(model)( - "Write a short sentence ", seed=10000, max_tokens=10 - ) - assert isinstance(sequence, str) - assert model.tokenizer.eos_token not in sequence - - sequence = generate.text(model)( - "Write a short sentence ", - max_tokens=20, - stop_at="a", - seed=10000, - ) - assert isinstance(sequence, str) - - prompts = ["Write a short sentence ", "And another one "] - sequence = generate.text(model)( - prompts, max_tokens=10, stop_at=[".", ","], seed=10000 - ) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert isinstance(sequence[0], str) - - -def test_transformers_integration_text_multiple_samples(model): - sampler = multinomial(2) - - sequence = generate.text(model, sampler=sampler)( - "Write a short sentence ", seed=10000 - ) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert model.tokenizer.eos_token not in sequence - - prompts = ["Write a short sentence ", "And another one "] - sequence = generate.text(model, sampler=sampler)( - prompts, max_tokens=10, stop_at=[".", ","], seed=10000 - ) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert isinstance(sequence[0], list) - assert len(sequence) == 2 - assert isinstance(sequence[0][0], str) - - -def test_transformers_integration_streaming(model): - sequence = generate.text(model).stream( - "Write a short sentence ", max_tokens=10, stop_at=[".", ","], seed=10000 - ) - - token = next(sequence) - assert isinstance(token, str) - - remaining = "".join([token for token in sequence]) - assert isinstance(remaining, str) - - sequence = generate.text(model).stream( - ["Prompt1", "Prompt2"], max_tokens=10, stop_at=[".", ","], seed=10000 - ) - tokens = next(sequence) - assert isinstance(tokens, list) - assert isinstance(tokens[0], str) - assert isinstance(tokens[1], str) - - -def test_transformers_integration_streaming_batch_samples(model): - sampler = multinomial(samples=2) - - sequence = generate.text(model, sampler=sampler).stream( - ["Prompt1", "Prompt2"], max_tokens=10, stop_at=[".", ","], seed=10000 - ) - tokens = next(sequence) - assert isinstance(tokens, list) - assert len(tokens) == 2 - assert isinstance(tokens[0], list) - assert len(tokens[0]) == 2 - assert isinstance(tokens[0], list) - assert len(tokens[1]) == 2 - - -def test_transformers_integration_streaming_batch_beam_search(model): - sampler = beam_search(beams=2) - - sequence = generate.regex(model, r"ab[cd]e", sampler=sampler).stream( - ["Prompt1", "Prompt2"], max_tokens=10, stop_at=["c", "d"], seed=10000 - ) - tokens = next(sequence) - assert isinstance(tokens, list) - assert len(tokens) == 2 - assert isinstance(tokens[0], list) - assert len(tokens[0]) == 2 - assert isinstance(tokens[0], list) - assert len(tokens[1]) == 2 - - -def test_transformers_integration_text_stop(model): - prompt = "Write a short sentence " - sequence = generate.text(model)(prompt, stop_at="a", seed=10000) - assert sequence[len(prompt) :].find("a") == -1 - - -def test_transformers_various_regexes(model): - prompt = "Write an email address" - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str) - - # One prompt - sequence = generator(prompt, seed=0) - assert re.fullmatch(regex_str, sequence) is not None - - -def test_transformers_various_regexes_prompt_list(model): - prompt = "Write an email address" - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str) - - # Two prompts - sequence = generator([prompt, prompt], seed=0) - assert re.fullmatch(regex_str, sequence[0]) is not None - assert re.fullmatch(regex_str, sequence[1]) is not None - - -def test_transformers_various_regexes_prompt_list_multiple_samples(model): - sampler = multinomial(samples=2) - prompt = "Write an email address" - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str, sampler=sampler) - - # Two prompts - sequence = generator([prompt, prompt], seed=0, max_tokens=500) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert re.fullmatch(regex_str, sequence[0][0]) is not None - assert re.fullmatch(regex_str, sequence[0][1]) is not None - assert re.fullmatch(regex_str, sequence[1][0]) is not None - assert re.fullmatch(regex_str, sequence[1][1]) is not None - - -def test_transformers_various_regexes_prompt_list_beam_search(model): - sampler = beam_search(5) - prompt_1 = "Write an email address" - prompt_2 = "Random" - regex_str = r"([a-z]{10})@([a-z]{5})\.([a-z]{3})" - generator = generate.regex(model, regex_str, sampler=sampler) - - # Two prompts - sequence = generator([prompt_1, prompt_2], seed=0) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert len(sequence[0]) == 5 - assert re.fullmatch(regex_str, sequence[0][0]) is not None - assert re.fullmatch(regex_str, sequence[0][1]) is not None - assert re.fullmatch(regex_str, sequence[1][0]) is not None - assert re.fullmatch(regex_str, sequence[1][1]) is not None - - -def test_transformers_integration_integer(model): - prompt = "Write a short sentence" - sequence = generate.format(model, int)(prompt, max_tokens=10, seed=0) - - assert isinstance(sequence, int) - - -def test_transformers_integration_integer_array(model): - prompts = ["Give me a number", "And another one"] - sequence = generate.format(model, int)(prompts, max_tokens=10, seed=0) - assert isinstance(sequence, list) - assert len(sequence) == 2 - assert isinstance(sequence[0], int) - assert isinstance(sequence[1], int) - - -def test_transformers_integration_float(model): - prompt = "Write a short sentence" - sequence = generate.format(model, float)(prompt, max_tokens=10, seed=0) - - assert sequence != "" - assert isinstance(sequence, float) - - -def test_transformers_integration_bool(model): - prompt = "Is this True or False?" - sequence = generate.format(model, bool)(prompt, max_tokens=10, seed=0) - - assert sequence != "" - assert isinstance(sequence, bool) - - -def test_transformers_integration_date(model): - prompt = "What day is it today?" - sequence = generate.format(model, datetime.date)(prompt, max_tokens=10, seed=0) - - assert sequence != "" - assert isinstance(sequence, datetime.date) - - -def test_transformers_integration_time(model): - prompt = "What time is it?" - sequence = generate.format(model, datetime.time)(prompt, max_tokens=10, seed=0) - - assert sequence != "" - assert isinstance(sequence, datetime.time) - - -def test_transformers_integration_datetime(model): - prompt = "What time is it?" - sequence = generate.format(model, datetime.datetime)(prompt, max_tokens=20, seed=0) - - assert sequence != 0 - assert isinstance(sequence, datetime.datetime) - - -def test_transformers_integration_choice(model): - prompt = "Write a short sentence " - sequence = generate.choice(model, ["test", "choice"])(prompt, seed=0) - - assert sequence == "test" or sequence == "choice" - - -def test_transformers_integration_with_pad_token(): - model_name = "hf-internal-testing/tiny-random-XLMRobertaXLForCausalLM" - model = models.transformers(model_name, device="meta") - assert model.tokenizer.pad_token_id == 1 - assert model.tokenizer.pad_token == "" - - -def test_transformers_json_basic(model): - prompt = "Output some JSON " - - class Spam(BaseModel): - foo: int - bar: float - spam: constr(max_length=10) - fuzz: bool - - result = generate.json(model, Spam)(prompt, max_tokens=500, seed=0) - assert isinstance(result, BaseModel) - assert isinstance(result.foo, int) - assert isinstance(result.bar, float) - assert isinstance(result.spam, str) - assert isinstance(result.fuzz, bool) - assert len(result.spam) <= 10 - - -def test_transformers_json_schema(model): - prompt = "Output some JSON " - - schema = """{ - "title": "spam", - "type": "object", - "properties": { - "foo" : {"type": "integer"}, - "bar": {"type": "string", "maxLength": 4} - }, - "required": ["foo", "bar"] - } - """ - - result = generate.json(model, schema)(prompt, max_tokens=500, seed=0) - assert isinstance(result, dict) - assert isinstance(result["foo"], int) - assert isinstance(result["bar"], str) - - -def test_transformers_json_batch(model): - prompts = ["Output some JSON ", "Output more JSON"] - - class Spam(BaseModel): - foo: int - bar: float - spam: constr(max_length=10) - fuzz: bool - - result = generate.json(model, Spam)(prompts, max_tokens=500, seed=0) - assert isinstance(result[0], BaseModel) - assert isinstance(result[1], BaseModel) - - -def test_transformers_json_batch_multiple_samples(model): - sampler = multinomial(samples=2) - prompts = ["Output some JSON ", "Output more JSON"] - - class Spam(BaseModel): - foo: int - bar: float - spam: constr(max_length=10) - fuzz: bool - - result = generate.json(model, Spam, sampler=sampler)( - prompts, max_tokens=500, seed=0 - ) - assert isinstance(result, list) - assert len(result) == 2 - assert isinstance(result[0][0], BaseModel) - assert isinstance(result[0][1], BaseModel) - assert isinstance(result[1][0], BaseModel) - assert isinstance(result[1][1], BaseModel) - - -def test_transformers_json_str_enum(model): - prompt = "Output some JSON " - - class Name(str, Enum): - john = "John" - marc = "Marc" - michel = "Michel" - - class User(BaseModel): - user_id: int - name: Name - - result = generate.json(model, User)(prompt, seed=0) - assert isinstance(result, BaseModel) - assert isinstance(result.user_id, int) - assert result.name in ["John", "Marc", "Michel"] - - -def test_transformers_json_int_enum(model): - prompt = "Output some JSON " - - class Id(int, Enum): - one = 1 - two = 2 - - class User(BaseModel): - user_id: Id - - result = generate.json(model, User)(prompt, seed=0) - assert isinstance(result, BaseModel) - assert isinstance(result.user_id, int) - assert result.user_id in [1, 2] - - -def test_transformers_json_array(model): - prompt = "Output some JSON " - - class User(BaseModel): - user_id: int - value: List[float] - - result = generate.json(model, User)(prompt, seed=0) - assert isinstance(result, BaseModel) - assert isinstance(result.user_id, int) - assert isinstance(result.value, list) - for value in result.value: - assert isinstance(value, float) or isinstance(value, int) - - -@pytest.mark.xfail(reason="The implementation of `anyOf` is incorrect") -def test_transformers_json_union(model): - prompt = "Output some JSON " - - class Spam(BaseModel): - foo: int - bar: Union[constr(max_length=10), float] - - result = generate.json(model, Spam)(prompt, max_tokens=100, seed=4) - assert isinstance(result, BaseModel) - assert ( - isinstance(result.bar, int) - or isinstance(result.bar, float) - or isinstance(result.bar, str) - ) - - -def test_transformers_json_function(model): - prompt = "Output arguments for the function" - - def function(foo: int, bar: List[int]): - return foo + sum(bar) - - sequence = generate.json(model, function)(prompt, max_tokens=100, seed=4) - assert isinstance(sequence, dict) - assert isinstance(function(**sequence), int) - - -def test_transformers_logits_vocab_size(model): - # Artificially increase the weights/logits size relative - # to the vocabulary - model.model.resize_token_embeddings(pad_to_multiple_of=3) - - assert len(model.tokenizer.vocabulary) == 1024 - assert model.model.base_model.wte.weight.shape[0] == 1026 - - generator = generate.choice(model, ["True", "False"]) - - sequence = generator("blah", seed=101) - assert sequence == "False" - - -def test_transformers_json_custom_ws(model): - prompt = "Output some JSON with newlines" # try to force model to use newlines - - schema = """{ - "title": "spam", - "type": "object", - "properties": { - "foo" : {"type": "integer"}, - "bar": {"type": "integer"} - }, - "required": ["foo", "bar"] - } - """ - - generator = generate.json(model, schema, whitespace_pattern=r"[ ]?") - generator.format_sequence = lambda x: x # patch to return raw text - assert "\n" not in generator(prompt, max_tokens=500, seed=0) - - -def test_transformers_reduced_vocabulary_caching(): - from transformers import AutoTokenizer - - hf_tokenizer = AutoTokenizer.from_pretrained("gpt2") - tokenizer = TransformerTokenizer(hf_tokenizer) - tokenizer2 = TransformerTokenizer(hf_tokenizer) - - # TODO: We might actually want only one copy of a given tokenizer. - assert tokenizer is not tokenizer2 - - vocab = reduced_vocabulary(tokenizer) - vocab2 = reduced_vocabulary(tokenizer2) - - assert vocab2 is vocab - - -@pytest.mark.skip(reason="Custom Sampler Disabled in Transformers Integration") -def test_custom_sampler(model): - seen = False - target_token_ids = model.tokenizer.encode(["c"])[0] - - class biased_sampler: - def __init__(self, samples: int = 1): - self.samples = samples - - def __call__( - logits: torch.DoubleTensor, samples: int, *_ - ) -> torch.DoubleTensor: - nonlocal seen - - if not seen: - seen = True - return target_token_ids, torch.tensor([0]), None - else: - return ( - torch.tensor([[model.tokenizer.eos_token_id]]), - torch.tensor([0]), - None, - ) - - generator = generate.choice(model, ["a", "b", "c"], sampler=biased_sampler(1)) - sequence = generator( - """What is 1+1? - a. 3 - b. 4 - c. 2""" - ) - - assert sequence == "c" - - -def test_transformers_use_existing_model_and_tokenizer(): - from transformers import AutoModelForCausalLM, AutoTokenizer - - model_name = "hf-internal-testing/tiny-random-GPTJForCausalLM" - hf_tokenizer = AutoTokenizer.from_pretrained(model_name) - hf_model = AutoModelForCausalLM.from_pretrained(model_name) - model = Transformers(hf_model, hf_tokenizer) - sequence = generate.text(model)("Write a short sentence ", seed=10000) - assert isinstance(sequence, str) - - -def test_RegexGuide_caching(temp_cache_dir): - import outlines.caching - from outlines.fsm.guide import create_states_mapping - - assert outlines.caching._caching_enabled - - regex = r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)" - prompt = "What is the IP address of the Google DNS servers? " - - cache = outlines.caching.get_cache() - - # Returns (hits, misses) - _ = cache.stats(enable=True) - assert cache.statistics - - assert create_states_mapping.__memory__ is cache - - model = models.transformers( - "hf-internal-testing/tiny-random-XLMRobertaXLForCausalLM", device="cpu" - ) - generator = generate.regex(model, regex, sampler=greedy()) - assert cache.stats() == (0, 1) - - model_2 = models.transformers( - "hf-internal-testing/tiny-random-GPTJForCausalLM", device="cpu" - ) - generator_2 = generate.regex(model_2, regex, sampler=greedy()) - assert cache.stats() == (0, 2) - - # These two different models and tokenizers should not have the same state - # mapping results - assert ( - generator.logits_processor.fsm.states_to_token_maps - != generator_2.logits_processor.fsm.states_to_token_maps - ) - - generator_3 = generate.regex(model_2, regex, sampler=greedy()) - assert cache.stats() == (1, 2) - assert ( - generator_2.logits_processor.fsm.states_to_token_maps - == generator_3.logits_processor.fsm.states_to_token_maps - ) - - # Just for fun... - structured = generator(prompt, max_tokens=30) - structured_2 = generator_2(prompt, max_tokens=30) - - assert re.fullmatch(regex, structured) - assert re.fullmatch(regex, structured_2) - assert structured != structured_2 diff --git a/tests/test_samplers.py b/tests/test_samplers.py deleted file mode 100644 index 88cdb0fb..00000000 --- a/tests/test_samplers.py +++ /dev/null @@ -1,254 +0,0 @@ -import math - -import pytest -import torch - -from outlines.samplers import ( - BeamSearchSampler, - GreedySampler, - MultinomialSampler, - beam_search, - greedy, - keep_top_k_logits, - keep_top_p_logits, - multinomial, - rescale_logits, -) - - -def compute_logprobs(logits): - return torch.nn.functional.log_softmax(logits, dim=-1) - - -def test_aliases(): - assert greedy == GreedySampler - assert multinomial == MultinomialSampler - assert beam_search == BeamSearchSampler - - -def test_greedy(): - sampler = GreedySampler() - logits = torch.tensor([[1.0, 2.0, 5.0]]) - weights = torch.tensor([0]) - next_token_ids, ancestors, weights = sampler(logits, weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[2]])) - assert ancestors.equal(torch.tensor([0])) - assert weights.equal(logprobs[..., 2]) - - sampler = GreedySampler() - logits = torch.tensor([[10.0, 0.0, 3.0], [-math.inf, 2.0, 5.0]]) - weights = torch.tensor([0, 0]) - next_token_ids, ancestors, weights = sampler(logits, weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[0], [2]])) - assert ancestors.equal(torch.tensor([0, 1])) - assert weights.equal(torch.tensor([logprobs[0, 0], logprobs[1, 2]])) - - -def test_multinomial(): - rng = torch.Generator() - rng.manual_seed(239) - - sampler = MultinomialSampler() - logits = torch.tensor([[1.0, 4.0, 5.0]]) - weights = torch.tensor([0]) - next_token_ids, ancestors, weights = sampler(logits, weights, rng) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[2]])) - assert ancestors.equal(torch.tensor([0])) - assert weights.equal(torch.tensor([logprobs[:, 2]])) - - sampler = MultinomialSampler() - logits = torch.tensor([[10.0, 0.0, 9.0], [-math.inf, 4.0, 5.0]]) - weights = torch.tensor([0, 0]) - next_token_ids, ancestors, weights = sampler(logits, weights, rng) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[0], [2]])) - assert ancestors.equal(torch.tensor([0, 1])) - assert weights.equal(torch.tensor([logprobs[0, 0], logprobs[1, 2]])) - - -def test_multinomial_init(): - sampler = MultinomialSampler() - assert sampler.logits_processors == [] - - sampler = MultinomialSampler(3) - assert sampler.logits_processors == [] - - sampler = MultinomialSampler(top_k=1) - assert len(sampler.logits_processors) == 1 - - sampler = MultinomialSampler(top_p=0.95) - assert len(sampler.logits_processors) == 1 - - sampler = MultinomialSampler(top_k=1, top_p=0.95) - assert len(sampler.logits_processors) == 1 - - sampler = MultinomialSampler(temperature=1.0) - assert len(sampler.logits_processors) == 1 - - sampler = MultinomialSampler(top_k=1, temperature=1.0) - assert len(sampler.logits_processors) == 2 - - sampler = MultinomialSampler(top_p=0.95, temperature=1.0) - assert len(sampler.logits_processors) == 2 - - -def test_top_k(): - with pytest.raises(ValueError, match="`k` must be a strictly"): - keep_top_k_logits(-1) - - with pytest.raises(ValueError, match="`k` must be a strictly"): - keep_top_k_logits(0.1) - - logits = torch.tensor([[1.0, 2.0, 3.0, 4.0]]) - - logits_processor = keep_top_k_logits(1) - result = logits_processor(logits) - assert result.equal(torch.tensor([[-math.inf, -math.inf, -math.inf, 4.0]])) - - logits_processor = keep_top_k_logits(10) - result = logits_processor(logits) - assert result.equal(torch.tensor([[1.0, 2.0, 3.0, 4.0]])) - - logits = torch.tensor([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]) - logits_processor = keep_top_k_logits(2) - result = logits_processor(logits) - assert result.equal( - torch.tensor( - [[-math.inf, -math.inf, 3.0, 4.0], [-math.inf, -math.inf, 7.0, 8.0]] - ) - ) - - -def test_top_p(): - with pytest.raises(ValueError, match="`p` must be a floating point"): - keep_top_p_logits(-0.1) - - with pytest.raises(ValueError, match="`p` must be a floating point"): - keep_top_p_logits(0.0) - - with pytest.raises(ValueError, match="`p` must be a floating point"): - keep_top_p_logits(1.1) - - logits = torch.tensor([[1.0, 1.01, 1.02, 4.0]]) - - logits_processor = keep_top_p_logits(0.1) - result = logits_processor(logits) - assert result.equal(torch.tensor([[-math.inf, -math.inf, -math.inf, 4.0]])) - - logits_processor = keep_top_p_logits(0.95) - result = logits_processor(logits) - assert result.equal(torch.tensor([[-math.inf, 1.01, 1.02, 4.0]])) - - logits_processor = keep_top_p_logits(1.0) - result = logits_processor(logits) - assert result.equal(torch.tensor([[1.0, 1.01, 1.02, 4.0]])) - - logits = torch.tensor([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]) - - logits_processor = keep_top_p_logits(0.1) - result = logits_processor(logits) - assert result.equal( - torch.tensor( - [ - [-math.inf, -math.inf, -math.inf, 4.0], - [-math.inf, -math.inf, -math.inf, 8.0], - ] - ) - ) - - logits_processor = keep_top_p_logits(0.95) - result = logits_processor(logits) - assert result.equal( - torch.tensor( - [ - [-math.inf, 2.0, 3.0, 4.0], - [-math.inf, 6.0, 7.0, 8.0], - ] - ) - ) - - logits_processor = keep_top_p_logits(1.0) - result = logits_processor(logits) - assert result.equal( - torch.tensor( - [ - [1.0, 2.0, 3.0, 4.0], - [5.0, 6.0, 7.0, 8.0], - ] - ) - ) - - -def test_rescale(): - with pytest.raises(ValueError, match="`temperature` must"): - rescale_logits(1) - - with pytest.raises(ValueError, match="`temperature` must"): - rescale_logits(-0.1) - - with pytest.raises(ValueError, match="Please use the greedy sampler"): - rescale_logits(0.0) - - -def test_beam_search(): - # Two beams, single sequence - sampler = BeamSearchSampler(2) - logits = torch.tensor([[0.0, 1.0], [2.0, 0.0]]) - init_weights = torch.tensor([0, 1.0]) - next_token_ids, ancestors, weights = sampler(logits, init_weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[0], [1]])) - assert ancestors.equal(torch.tensor([1, 0])) - assert weights.equal( - torch.tensor([init_weights[1] + logprobs[1][0], logprobs[0][1]]) - ) - - # Make sure that initial samples are different - sampler = BeamSearchSampler(2) - logits = torch.tensor([[0.0, 1.0], [0.0, 1.0]]) - init_weights = torch.tensor([0, 0]) - next_token_ids, ancestors, weights = sampler(logits, init_weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[1], [0]])) - assert ancestors.equal(torch.tensor([0, 0])) - assert weights.equal(torch.tensor([logprobs[0][1], logprobs[0][0]])) - - # One beam, batch of two sequences. Reduces to Greedy Search. - sampler = BeamSearchSampler(1) - logits = torch.tensor([[0.0, 1.0], [2.0, 0.0]]) - weights = torch.tensor([0, 0]) - next_token_ids, ancestors, weights = sampler(logits, weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[1], [0]])) - assert ancestors.equal(torch.tensor([0, 1])) - assert weights.equal(torch.tensor([logprobs[0][1], logprobs[1][0]])) - - # Two beams, batch of two sequences - sampler = BeamSearchSampler(2) - logits = torch.tensor([[0.0, 1.0], [2.0, 0.0], [3.0, 2.0], [0.0, 1.0]]) - init_weights = torch.tensor([0, 0, 2.0, 0]) - next_token_ids, ancestors, weights = sampler(logits, init_weights, None) - - logprobs = compute_logprobs(logits) - assert next_token_ids.equal(torch.tensor([[0], [1], [0], [1]])) - assert ancestors.equal(torch.tensor([1, 0, 2, 2])) - assert weights.equal( - torch.tensor( - [ - logprobs[1][0], - logprobs[0][1], - init_weights[2] + logprobs[2][0], - init_weights[2] + logprobs[2][1], - ] - ) - ) From 15e82e49d45574c660cbd9b94d74587b7db6879a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 14:27:46 +0200 Subject: [PATCH 09/20] Remove logits processors --- benchmarks/bench_processors.py | 108 ----------- outlines/processors/__init__.py | 7 - outlines/processors/base_logits_processor.py | 135 ------------- outlines/processors/structured.py | 194 ------------------- 4 files changed, 444 deletions(-) delete mode 100644 benchmarks/bench_processors.py delete mode 100644 outlines/processors/__init__.py delete mode 100644 outlines/processors/base_logits_processor.py delete mode 100644 outlines/processors/structured.py diff --git a/benchmarks/bench_processors.py b/benchmarks/bench_processors.py deleted file mode 100644 index 5b490154..00000000 --- a/benchmarks/bench_processors.py +++ /dev/null @@ -1,108 +0,0 @@ -import numpy as np -import torch - -import outlines.models as models -from outlines.processors import OutlinesLogitsProcessor, RegexLogitsProcessor - -try: - import mlx.core as mx -except ImportError: - pass - - -def is_mlx_lm_allowed(): - try: - import mlx.core as mx - except ImportError: - return False - return mx.metal.is_available() - - -def get_mock_processor_inputs(array_library, num_tokens=30000): - """ - logits: (4, 30,000 ) dtype=float - input_ids shape: (4, 2048) dtype=int - """ - if array_library == "torch": - logits = torch.rand((4, num_tokens), dtype=torch.float) - input_ids = torch.randint( - low=0, high=num_tokens, size=(4, 2048), dtype=torch.int - ) - elif array_library == "torch_cuda": - logits = torch.rand((4, num_tokens), dtype=torch.float, device="cuda") - input_ids = torch.randint( - low=0, high=num_tokens, size=(4, 2048), dtype=torch.int, device="cuda" - ) - elif array_library == "numpy": - logits = np.random.rand(4, num_tokens).astype(np.float32) - input_ids = np.random.randint(low=0, high=num_tokens, size=(4, 2048)) - elif array_library == "mlx": - logits = mx.random.uniform( - low=-1e9, high=1e9, shape=(4, num_tokens), dtype=mx.float32 - ) - input_ids = mx.random.randint( - low=0, high=num_tokens, shape=(4, 2048), dtype=mx.int32 - ) - else: - raise ValueError - - return logits, input_ids - - -class HalvingLogitsProcessor(OutlinesLogitsProcessor): - """Simply halve the passed logits""" - - def process_logits(self, input_ids, logits): - return logits / 2 - - -class LogitsProcessorPassthroughBenchmark: - """ - Benchmark the time it takes to convert between array frameworks - This should be on the order of microseconds - """ - - params = ["torch", "numpy"] - if is_mlx_lm_allowed(): - params += ["mlx"] - if torch.cuda.is_available(): - params += ["torch_cuda"] - - def setup(self, array_library): - self.logits_processor = HalvingLogitsProcessor() - - self.logits, self.input_ids = get_mock_processor_inputs(array_library) - - def time_passthrough(self, *params): - self.logits_processor(self.input_ids, self.logits) - - -class LogitsProcessorStructuredBenchmark: - """ - Benchmark structured generation mask application for single decoder pass - """ - - array_libraries = ["torch", "numpy"] - if is_mlx_lm_allowed(): - array_libraries += ["mlx"] - # PR TODO - if torch.cuda.is_available(): - array_libraries += ["torch_cuda"] - - # accept very many or very few tokens, respectively - patterns = [r"[^Z]*", "Z*"] - - params = [array_libraries, patterns] - param_names = ["array_library, pattern"] - - def setup(self, array_library, pattern): - tokenizer = models.transformers("facebook/opt-125m", device="cpu").tokenizer - - self.logits_processor = RegexLogitsProcessor(pattern, tokenizer) - - self.logits, self.input_ids = get_mock_processor_inputs( - array_library, len(tokenizer.vocabulary) - ) - - def time_structured_generation(self, array_library, pattern): - self.logits_processor(self.input_ids, self.logits) diff --git a/outlines/processors/__init__.py b/outlines/processors/__init__.py deleted file mode 100644 index 22c10d90..00000000 --- a/outlines/processors/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .structured import ( - CFGLogitsProcessor, - FSMLogitsProcessor, - JSONLogitsProcessor, - OutlinesLogitsProcessor, - RegexLogitsProcessor, -) diff --git a/outlines/processors/base_logits_processor.py b/outlines/processors/base_logits_processor.py deleted file mode 100644 index feedf525..00000000 --- a/outlines/processors/base_logits_processor.py +++ /dev/null @@ -1,135 +0,0 @@ -from abc import abstractmethod -from typing import TYPE_CHECKING, List, Protocol, Type, Union - -import numpy as np -import torch -from numpy.typing import NDArray - -if TYPE_CHECKING: - import mlx.core as mx - - -Array = Union[NDArray, torch.Tensor, List, "mx.array"] - - -def is_mlx_array_type(array_type): - try: - import mlx.core as mx - except ImportError: - return False - return issubclass(array_type, mx.array) - - -class OutlinesLogitsProcessor(Protocol): - """ - Base class for logits processors which normalizes types of logits: - - ndarray (used by llama-cpp-python), converted to torch.Tensor - - mlx.core.array (used by mlx-lm), converted to torch.Tensor - - torch.Tensor (used by everything else) - - Normalization of types and conversion to torch.Tensor - doesn't move memory, it just casts the type. - - Normalizing the types allows all logits processors inheriting from this class - to implement a single method for all the business logit: `process_logits()` - """ - - @abstractmethod - def process_logits( - self, input_ids: List[List[int]], logits: torch.Tensor - ) -> torch.Tensor: - """ - input_ids and logits are always 2D tensors for handling a batch of sequences. - - - input_ids -> List[List[tokens]] - - logits -> 2D_Tensor[logit floats] - - Important to keep in mind when designing universal logits processors - - logits processors are only used once and never re-applied for a new sequence generator - - Some models only pass output_ids, some models such as llamacpp and transformers prefix with input_ids - - Some sampling methods, such as beam search, result in unstable sequence ordering in models like vLLM - """ - pass - - @torch.no_grad() - def __call__( - self, - input_ids: Array, - logits: Array, - ) -> Array: - """ - Apply logits processor - - 1) Unify type - - convert input_ids: either ndarray, mlx array, List[int], or Tensor -> List[List[int]] - - convert logits: either ndarray, mlx array, or Tensor -> 2D float Tensor - 2) Unify shape, ensure logits and input_ids are 2D - 3) Call self.process_logits() to perform business logic - 4) Cast logits back to original array library type - """ - # ensure logits are torch Tensors - torch_logits = self._to_torch(logits) - input_ids = self._to_torch(input_ids) - - assert torch_logits.shape[:-1] == input_ids.shape[:-1] - - # Guarantee passed as 2D Tensors, then covert back to original (1D or 2D) shape - if len(torch_logits.shape) == 2: - processed_logits = self.process_logits(input_ids.tolist(), torch_logits) - elif len(torch_logits.shape) == 1: - processed_logits = self.process_logits( - [input_ids.tolist()], torch_logits.unsqueeze(0) - ).squeeze(0) - - # return logits as passed array type - return self._from_torch(processed_logits, type(logits)) - - @staticmethod - def _to_torch(tensor_like: Array) -> torch.Tensor: - """Convert various types to torch.Tensor.""" - if isinstance(tensor_like, torch.Tensor): - return tensor_like - - elif isinstance(tensor_like, np.ndarray): - return torch.from_numpy(tensor_like) - - elif isinstance(tensor_like, (list, tuple)): - return torch.tensor(tensor_like) - - elif is_mlx_array_type(type(tensor_like)): - # mlx -> torch -> mlx conversion docs: - # https://ml-explore.github.io/mlx/build/html/usage/numpy.html - return torch.from_dlpack(tensor_like) - - else: - raise TypeError( - "LogitsProcessor must be called with either np.NDArray, " - "torch.Tensor, list, or mlx.core.array typed logits. " - f"Logits type: `{type(tensor_like)}`" - ) - - @staticmethod - def _from_torch(tensor: torch.Tensor, target_type: Type) -> Array: - """Convert torch.Tensor to the specified target type.""" - if target_type == torch.Tensor: - return tensor - - elif target_type == np.ndarray: - return tensor.detach().numpy() - - elif target_type == list: - return tensor.detach().tolist() - - elif target_type == tuple: - return tuple(tensor.detach().tolist()) - - elif is_mlx_array_type(target_type): - import mlx.core as mx - - # numpy doesn't support bfloat16, mlx doesn't support direct conversion from torch - return mx.array(tensor.float().numpy()) - - else: - raise TypeError( - f"Failed to convert torch tensors to target_type `{target_type}`" - ) diff --git a/outlines/processors/structured.py b/outlines/processors/structured.py deleted file mode 100644 index f8d63d7b..00000000 --- a/outlines/processors/structured.py +++ /dev/null @@ -1,194 +0,0 @@ -""" - _______________________________ -/ Don't want to self-host? \ -\\ Try .json at http://dottxt.co / - ------------------------------- - \\ ^__^ - \\ (oo)\\_______ - (__)\\ )\\/\ - ||----w | - || || - -Copyright 2024- the Outlines developers - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import math -from typing import TYPE_CHECKING, Dict, List, Optional, Type, Union - -import torch -from pydantic import BaseModel - -from outlines.fsm.guide import CFGGuide, Guide, RegexGuide -from outlines.fsm.json_schema import build_regex_from_schema, convert_json_schema_to_str - -from .base_logits_processor import OutlinesLogitsProcessor - -if TYPE_CHECKING: - from outlines.models.tokenizer import Tokenizer - - -class FSMLogitsProcessor(OutlinesLogitsProcessor): - """Bias generation using a finite state machine. - - Attributes - ---------- - tokenizer - The tokenizer used to convert tokens to ids. - fsm - The finite state machine which is used to bias the logits. - """ - - def __init__(self, tokenizer: "Tokenizer", fsm: Guide): - """A FSM-based logits processor. - - Parameters - ---------- - tokenizer - The tokenizer used to convert tokens to ids. - fsm - The finite state machine which is used to bias the logits. - """ - self.tokenizer = tokenizer - self._fsm_states: Dict[int, int] = {hash(tuple([])): 0} - self.fsm: Guide = fsm - self._seq_start_idx: Optional[int] = None - - def process_logits( - self, input_ids: List[List[int]], logits: torch.Tensor - ) -> torch.Tensor: - """Use the FSM to bias the logits before sampling the next token. - - Parameters - ---------- - input_ids - The input token ids. - logits - The logits. - - Returns - ------- - torch.Tensor - The biased logits. - """ - if self._seq_start_idx is None: - self._seq_start_idx = len(input_ids[0]) - - sequence_states: List[int] = [] # vector of states corresponding to `input_ids` - - for seq_ids in input_ids: - gen_ids = seq_ids[self._seq_start_idx :] - curr_state_key = hash(tuple(gen_ids)) - - if curr_state_key not in self._fsm_states: - prev_state = self._fsm_states[hash(tuple(gen_ids[:-1]))] - curr_state = self.fsm.get_next_state(prev_state, gen_ids[-1]) - self._fsm_states[curr_state_key] = curr_state - - sequence_states.append(self._fsm_states[curr_state_key]) - - mask = torch.full_like(logits, -math.inf) - for i, fsm_state in enumerate(sequence_states): - allowed_tokens = self.fsm.get_next_instruction(fsm_state).tokens - mask[i, allowed_tokens] = logits[i, allowed_tokens] - - return mask - - def copy(self) -> "FSMLogitsProcessor": - """Return a copy of the logits processor.""" - return FSMLogitsProcessor(tokenizer=self.tokenizer, fsm=self.fsm.copy()) - - -class RegexLogitsProcessor(FSMLogitsProcessor): - """Bias generation based on a regular expression. - - Attributes - ---------- - tokenizer - The tokenizer used to convert tokens to ids. - fsm - The finite state machine which is used to bias the logits. - """ - - def __init__(self, regex_string: str, tokenizer: "Tokenizer"): - """Compile the FSM that drives the regex-guided generation. - - Parameters - ---------- - regex_string - A string that represents a regular expression - tokenizer - An Outlines tokenizer - """ - fsm = RegexGuide(regex_string, tokenizer) - super().__init__(tokenizer=tokenizer, fsm=fsm) - - -class JSONLogitsProcessor(RegexLogitsProcessor): - """Bias generation based on a JSON schema. - - Attributes - ---------- - tokenizer - The tokenizer used to convert tokens to ids. - fsm - The finite state machine which is used to bias the logits. - """ - - def __init__( - self, - schema: Union[dict, Type[BaseModel], str], - tokenizer: "Tokenizer", - whitespace_pattern: Optional[str] = None, - ): - """Compile the FSM that drives the JSON-guided generation. - - Parameters - ---------- - schema - A JSON schema that encodes the structure we want the model to generate. - tokenizer - The tokenizer used to convert tokens to ids. - whitespace_pattern - Pattern to use for JSON syntactic whitespace (doesn't impact string - literals). For example, to allow only a single space or newline with - `whitespace_pattern=r"[\n ]?"` - """ - schema_str = convert_json_schema_to_str(json_schema=schema) - regex_string = build_regex_from_schema(schema_str, whitespace_pattern) - super().__init__(regex_string=regex_string, tokenizer=tokenizer) - - -class CFGLogitsProcessor(FSMLogitsProcessor): - """Bias generation based on a context-free grammar. - - Attributes - ---------- - tokenizer - The tokenizer used to convert tokens to ids. - fsm - The finite state machine which is used to bias the logits. - """ - - def __init__(self, cfg_str: str, tokenizer: "Tokenizer"): - """Compile the FSM that drives the CFG-guided generation. - - Parameters - ---------- - cfg_str - A string that represents a grammar - tokenizer - The tokenizer used to convert tokens to ids. - """ - cfg_automata = CFGGuide(cfg_string=cfg_str, tokenizer=tokenizer) - super().__init__(tokenizer=tokenizer, fsm=cfg_automata) From ffe5c32bdfb3cf052989aea1dad113c78075468e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 14:31:54 +0200 Subject: [PATCH 10/20] Remove CFG logic --- outlines/fsm/fsm.py | 24 +-- outlines/fsm/guide.py | 179 ------------------- outlines/models/transformers.py | 8 +- tests/fsm/test_fsm.py | 262 +--------------------------- tests/fsm/test_guide.py | 300 +------------------------------- tests/fsm/test_parsing.py | 206 ---------------------- 6 files changed, 6 insertions(+), 973 deletions(-) delete mode 100644 tests/fsm/test_parsing.py diff --git a/outlines/fsm/fsm.py b/outlines/fsm/fsm.py index 4a7fce8c..bfcf55c0 100644 --- a/outlines/fsm/fsm.py +++ b/outlines/fsm/fsm.py @@ -1,7 +1,7 @@ import warnings from typing import TYPE_CHECKING, Iterable, NewType, Optional -from outlines.fsm.guide import CFGGuide, RegexGuide, StopAtEOSGuide +from outlines.fsm.guide import RegexGuide, StopAtEOSGuide if TYPE_CHECKING: from outlines.models.tokenizer import Tokenizer @@ -45,25 +45,3 @@ def allowed_token_ids(self, state: FSMState) -> Optional[Iterable[int]]: def next_state(self, state: FSMState, token_id: int) -> FSMState: return FSMState(self.get_next_state(state, token_id)) - - -class CFGFSM(CFGGuide): - """FSM to generate text that is in the language of a context-free grammar.""" - - def __init__(self, cfg_string: str, tokenizer): - warnings.warn( - UserWarning( - "The `CFGFSM` interface is deprecated and will be removed on 2024-06-01. Please use `CFGGuide` instead." - ) - ) - super().__init__(cfg_string, tokenizer) - - def allowed_token_ids(self, state: FSMState) -> Optional[Iterable[int]]: - return self.get_next_instruction(state).tokens - - def next_state(self, state: FSMState, token_id: int) -> FSMState: - return FSMState(self.get_next_state(state, token_id)) - - def copy(self) -> "CFGFSM": - """Create a copy of the FSM.""" - return CFGFSM(self.cfg_string, self.tokenizer) diff --git a/outlines/fsm/guide.py b/outlines/fsm/guide.py index 2e441514..c846c441 100644 --- a/outlines/fsm/guide.py +++ b/outlines/fsm/guide.py @@ -13,9 +13,7 @@ import interegular import torch -from lark import Lark -from outlines import grammars from outlines.caching import cache from outlines.fsm.regex import ( create_fsm_index_tokenizer, @@ -298,180 +296,3 @@ def is_final_state(self, state: int) -> bool: def copy(self): return self - - -class CFGGuide(Guide): - """Guide to generate text that is in the language of a context-free grammar.""" - - def __init__(self, cfg_string: str, tokenizer): - self.cfg_string = cfg_string - self.tokenizer = tokenizer - - self.parser = Lark( - cfg_string, - parser="lalr", - lexer="contextual", - propagate_positions=False, - maybe_placeholders=False, - regex=True, - import_paths=[grammars.GRAMMAR_PATH], - ) - self.terminal_regexps = dict() - for terminal in self.parser.terminals: - if terminal.pattern is not None: - self.terminal_regexps[terminal.name] = terminal.pattern.to_regexp() - self.terminal_regexps["$END"] = tokenizer.eos_token - - self.generation = "" - self.reset_state = False - self.allow_eos = False - self.regex_fsm: RegexGuide - - self.check_last = False - self.proposal_last: List[int] = [] - self.regex_fsm_last: RegexGuide - - self.start_state = 0 - self.final_state = -1 - - def get_next_instruction(self, state: int) -> Instruction: - """Generate an instruction for the next step. - - Upon initialization, the CFG incremental parser is used to determine the - first regex and construct the first FSM to generate the first terminal. - - This FSM is used for proposals until either: - - - The FSM is exhausted, and its only remaining option is the EOS token, - in which case we feed the generated terminal to the - CFG incremental parser and allow it to propose the next regex - corresponding to the next set of valid terminals. - - The current FSM can be exhausted, but the EOS token is not the only - remaining option. In this case we allow proposal of current terminal - extensions, store the current FSM and its state, then also use the CFG - parser to propose a new regex corresponding to terminating the current - terminal and starting the next one. The model can then sample from - either of these sets to determine whether to extend the current - terminal or terminate it and start the next one. - - The CFG incremental parser is allowed to propose the EOS token from any accepting state, - and once it is generated, the FSM will continue to always generate the EOS token. - - Parameters - ---------- - state - The current state of the FSM. - - Returns - ------- - A list that contains the tokens to mask. - - """ - if self.is_final_state(state): - return Write([self.tokenizer.eos_token_id]) - - proposal: List[int] = [] - if self.generation != "": - if self.check_last: - proposer = self.regex_fsm_last - else: - proposer = self.regex_fsm - - instruction = proposer.get_next_instruction(state) - - assert instruction.tokens is not None - - if isinstance(instruction, Write): - proposal += instruction.tokens - else: - proposal += instruction.tokens - - if self.tokenizer.eos_token_id not in proposal: - return Generate(proposal) - - self.check_last = False - proposal = [x for x in proposal if x != self.tokenizer.eos_token_id] - if len(proposal) > 0: - self.check_last = True - self.proposal_last = proposal.copy() - self.regex_fsm_last = proposer - - interactive = self.parser.parse_interactive(self.generation) - interactive.exhaust_lexer() - - options = {self.terminal_regexps[x] for x in interactive.accepts()} - # add %ignore terminals - options |= {self.terminal_regexps[x] for x in self.parser.lexer_conf.ignore} - - if self.terminal_regexps["$END"] in options: - options.remove(self.terminal_regexps["$END"]) - if len(options) == 0: - return Write([self.tokenizer.eos_token_id]) - self.allow_eos = True - options.add("") - assert len(options) > 1 - - regex_string = r"(" + r"|".join([r"(" + x + r")" for x in options]) + r")" - self.regex_fsm = RegexGuide(regex_string, self.tokenizer) - self.reset_state = True - - instruction = self.regex_fsm.get_next_instruction(self.start_state) - - assert instruction.tokens is not None - - if isinstance(instruction, Write): - proposal += instruction.tokens - else: - proposal += instruction.tokens - - if self.allow_eos: - self.allow_eos = False - else: - proposal = [x for x in proposal if x != self.tokenizer.eos_token_id] - assert len(proposal) > 0 - - return Generate(proposal) - - def get_next_state(self, state: int, token_id: int) -> int: - """Update the state of the guide. - - Transitions the underlying regex FSM to its next state. - If at max tokens or EOS token, transition permanently to the final state. - Update stored partial generations for subsequent incremental parsing. - - Parameters - ---------- - state - The current state of the FSM. - token_id - The id of the token that was just generated. - - Returns - ------- - The new state of the FSM. - """ - - # We need to return the final state when in the final state because we - # then generate EOS tokens instead of stopping the generation. - if token_id == self.tokenizer.eos_token_id or state == self.final_state: - return self.final_state - - self.generation += self.tokenizer.decode([token_id])[0] - - if self.check_last: - if token_id in self.proposal_last: - return self.regex_fsm_last.get_next_state(state, token_id) - self.check_last = False - - if self.reset_state: - self.reset_state = False - state = self.start_state - - return self.regex_fsm.get_next_state(state, token_id) - - def is_final_state(self, state: int) -> bool: - return state == self.final_state - - def copy(self) -> "CFGGuide": - """Create a copy of the FSM.""" - return CFGGuide(self.cfg_string, self.tokenizer) diff --git a/outlines/models/transformers.py b/outlines/models/transformers.py index 949d3c9c..10f8f248 100644 --- a/outlines/models/transformers.py +++ b/outlines/models/transformers.py @@ -10,8 +10,6 @@ import torch from transformers import PreTrainedModel, PreTrainedTokenizer - from outlines.processors import OutlinesLogitsProcessor - __all__ = ["transformers"] @@ -217,7 +215,7 @@ def generate( self, prompts: Union[str, List[str]], generation_parameters: GenerationParameters, - logits_processor: Optional["OutlinesLogitsProcessor"], + logits_processor, sampling_parameters: SamplingParameters, ) -> Union[str, List[str], List[List[str]]]: """Generate text using `transformers`. @@ -275,7 +273,7 @@ def stream( self, prompts: Union[str, List[str]], generation_parameters: GenerationParameters, - logits_processor: Optional["OutlinesLogitsProcessor"], + logits_processor, sampling_parameters: SamplingParameters, ) -> Iterator[Union[str, List[str]]]: """ @@ -319,7 +317,7 @@ def _get_generation_kwargs( self, prompts: Union[str, List[str]], generation_parameters: GenerationParameters, - logits_processor: Optional["OutlinesLogitsProcessor"], + logits_processor, sampling_parameters: SamplingParameters, ) -> dict: """ diff --git a/tests/fsm/test_fsm.py b/tests/fsm/test_fsm.py index 21163b70..94166fd9 100644 --- a/tests/fsm/test_fsm.py +++ b/tests/fsm/test_fsm.py @@ -1,6 +1,6 @@ import pytest -from outlines.fsm.fsm import CFGFSM, RegexFSM, StopAtEosFSM +from outlines.fsm.fsm import RegexFSM, StopAtEosFSM def assert_expected_tensor_ids(tensor, ids): @@ -90,263 +90,3 @@ def convert_token_to_string(self, token): state = fsm.next_state(state=5, token_id=103) assert fsm.is_final_state(state) - - -def test_cfg(): - class MockTokenizer: - vocabulary = {"{": 1, "}": 2, "[": 3, "]": 4, "eos": 5} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 5 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: expr - expr: "{" expr "}" | "[" expr "]" | - """ - tokenizer = MockTokenizer() - - with pytest.warns(UserWarning): - fsm = CFGFSM(cfg_str, tokenizer) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=fsm.start_state), [1, 3, 5]) - state = fsm.next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "{" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2, 3]) - state = fsm.next_state(state=state, token_id=3) - assert fsm.generation == "{[" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 3, 4]) - state = fsm.next_state(state=state, token_id=4) - assert fsm.generation == "{[]" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [2]) - state = fsm.next_state(state=state, token_id=2) - assert fsm.generation == "{[]}" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [5]) - state = fsm.next_state(state=state, token_id=5) - assert fsm.generation == "{[]}" - assert fsm.is_final_state(state) - - -def test_cfg_early_termination(): - class MockTokenizer: - vocabulary = {"(": 1, ")": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: expr+ - expr: "(" subexpr ")" - subexpr: expr | - """ - tokenizer = MockTokenizer() - - with pytest.warns(UserWarning): - fsm = CFGFSM(cfg_str, tokenizer) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=fsm.start_state), [1]) - state = fsm.next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "(" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2]) - state = fsm.next_state(state=state, token_id=2) - assert fsm.generation == "()" - assert not fsm.is_final_state(state) - - # possible to continue or terminate - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 3]) - state = fsm.next_state(state=state, token_id=3) # feed eos - assert fsm.generation == "()" - assert fsm.is_final_state(state) - - # once eos generated, can only terminate - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [3]) - - -def test_cfg_ignore_directive(): - class MockTokenizer: - vocabulary = {"a": 1, " ": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: LETTER+ - LETTER: "a" - WS: " " - %ignore WS - """ - tokenizer = MockTokenizer() - - with pytest.warns(UserWarning): - fsm = CFGFSM(cfg_str, tokenizer) - - state = 0 - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=0), [1, 2]) - state = fsm.next_state(state=0, token_id=2) - assert fsm.generation == " " - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=0), [1, 2]) - state = fsm.next_state(state=0, token_id=1) - assert fsm.generation == " a" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2, 3]) - state = fsm.next_state(state=state, token_id=2) - assert fsm.generation == " a " - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2, 3]) - state = fsm.next_state(state=state, token_id=2) - assert fsm.generation == " a " - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2, 3]) - state = fsm.next_state(state=state, token_id=1) - assert fsm.generation == " a a" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 2, 3]) - state = fsm.next_state(state=state, token_id=3) - assert fsm.generation == " a a" - assert fsm.is_final_state(state) - - # once eos generated, can only terminate - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [3]) - - -def test_cfg_multitoken_terminal(): - class MockTokenizer: - vocabulary = {"a": 1, "b": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: S - S: "aa" | "bb" - """ - tokenizer = MockTokenizer() - - with pytest.warns(UserWarning): - fsm = CFGFSM(cfg_str, tokenizer) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=fsm.start_state), [1, 2]) - assert fsm.reset_state # starting new regex - state = fsm.next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "a" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1]) - assert not fsm.reset_state # continuing current regex - state = fsm.next_state(state=state, token_id=1) - assert fsm.generation == "aa" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [3]) - assert not fsm.reset_state # completing current regex - state = fsm.next_state(state=state, token_id=3) - assert fsm.generation == "aa" - assert fsm.is_final_state(state) - - -def test_cfg_allow_both_extend_and_shift_terminal(): - class MockTokenizer: - vocabulary = {"(": 1, ")": 2, "a": 3, "eos": 4} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 4 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: s - s: "(" s ")" | /a+/ - """ - tokenizer = MockTokenizer() - - with pytest.warns(UserWarning): - fsm = CFGFSM(cfg_str, tokenizer) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=fsm.start_state), [1, 3]) - state = fsm.next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "(" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [1, 3]) - state = fsm.next_state(state=state, token_id=3) - assert fsm.generation == "(a" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [2, 3]) - state = fsm.next_state(state=state, token_id=3) - assert fsm.generation == "(aa" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [2, 3]) - state = fsm.next_state(state=state, token_id=2) - assert fsm.generation == "(aa)" - assert not fsm.is_final_state(state) - - assert_expected_tensor_ids(fsm.allowed_token_ids(state=state), [4]) - state = fsm.next_state(state=state, token_id=4) - assert fsm.generation == "(aa)" - assert fsm.is_final_state(state) diff --git a/tests/fsm/test_guide.py b/tests/fsm/test_guide.py index 20ba7589..9a66bc04 100644 --- a/tests/fsm/test_guide.py +++ b/tests/fsm/test_guide.py @@ -1,6 +1,6 @@ import pytest -from outlines.fsm.guide import CFGGuide, Generate, RegexGuide, StopAtEOSGuide, Write +from outlines.fsm.guide import Generate, RegexGuide, StopAtEOSGuide, Write def assert_expected_tensor_ids(tensor, ids): @@ -188,301 +188,3 @@ def convert_token_to_string(self, token): state = fsm.get_next_state(state=5, token_id=103) assert fsm.is_final_state(state) - - -def test_cfg(): - class MockTokenizer: - vocabulary = {"{": 1, "}": 2, "[": 3, "]": 4, "eos": 5} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 5 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: expr - expr: "{" expr "}" | "[" expr "]" | - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - - instruction = fsm.get_next_instruction(fsm.start_state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 3, 5]) - state = fsm.get_next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "{" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2, 3]) - state = fsm.get_next_state(state=state, token_id=3) - assert fsm.generation == "{[" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 3, 4]) - state = fsm.get_next_state(state=state, token_id=4) - assert fsm.generation == "{[]" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [2]) - state = fsm.get_next_state(state=state, token_id=2) - assert fsm.generation == "{[]}" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Write) - assert_expected_tensor_ids(instruction.tokens, [5]) - state = fsm.get_next_state(state=state, token_id=5) - assert fsm.generation == "{[]}" - assert fsm.is_final_state(state) - - -def test_cfg_early_termination(): - class MockTokenizer: - vocabulary = {"(": 1, ")": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: expr+ - expr: "(" subexpr ")" - subexpr: expr | - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - - instruction = fsm.get_next_instruction(fsm.start_state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1]) - state = fsm.get_next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "(" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2]) - state = fsm.get_next_state(state=state, token_id=2) - assert fsm.generation == "()" - assert not fsm.is_final_state(state) - - # possible to continue or terminate - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 3]) - state = fsm.get_next_state(state=state, token_id=3) # feed eos - assert fsm.generation == "()" - assert fsm.is_final_state(state) - - # once eos generated, can only terminate - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Write) - assert_expected_tensor_ids(instruction.tokens, [3]) - - -def test_cfg_ignore_directive(): - class MockTokenizer: - vocabulary = {"a": 1, " ": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: LETTER+ - LETTER: "a" - WS: " " - %ignore WS - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - - state = 0 - - instruction = fsm.get_next_instruction(0) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2]) - state = fsm.get_next_state(state=0, token_id=2) - assert fsm.generation == " " - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(0) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2]) - state = fsm.get_next_state(state=0, token_id=1) - assert fsm.generation == " a" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2, 3]) - state = fsm.get_next_state(state=state, token_id=2) - assert fsm.generation == " a " - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2, 3]) - state = fsm.get_next_state(state=state, token_id=2) - assert fsm.generation == " a " - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2, 3]) - state = fsm.get_next_state(state=state, token_id=1) - assert fsm.generation == " a a" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2, 3]) - state = fsm.get_next_state(state=state, token_id=3) - assert fsm.generation == " a a" - assert fsm.is_final_state(state) - - # once eos generated, can only terminate - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Write) - assert_expected_tensor_ids(instruction.tokens, [3]) - - -def test_cfg_multitoken_terminal(): - class MockTokenizer: - vocabulary = {"a": 1, "b": 2, "eos": 3} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 3 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: S - S: "aa" | "bb" - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - - instruction = fsm.get_next_instruction(fsm.start_state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 2]) - assert fsm.reset_state # starting new regex - state = fsm.get_next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "a" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1]) - assert not fsm.reset_state # continuing current regex - state = fsm.get_next_state(state=state, token_id=1) - assert fsm.generation == "aa" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Write) - assert_expected_tensor_ids(instruction.tokens, [3]) - assert not fsm.reset_state # completing current regex - state = fsm.get_next_state(state=state, token_id=3) - assert fsm.generation == "aa" - assert fsm.is_final_state(state) - - -def test_cfg_allow_both_extend_and_shift_terminal(): - class MockTokenizer: - vocabulary = {"(": 1, ")": 2, "a": 3, "eos": 4} - special_tokens = {"eos"} - eos_token = "eos" - eos_token_id = 4 - - def convert_token_to_string(self, token): - return token - - @property - def inverse_vocabulary(self): - return {v: k for k, v in self.vocabulary.items()} - - def decode(self, token_ids): - return [self.inverse_vocabulary[t] for t in token_ids] - - cfg_str = """ - start: s - s: "(" s ")" | /a+/ - """ - tokenizer = MockTokenizer() - fsm = CFGGuide(cfg_str, tokenizer) - - instruction = fsm.get_next_instruction(fsm.start_state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 3]) - state = fsm.get_next_state(state=fsm.start_state, token_id=1) - assert fsm.generation == "(" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [1, 3]) - state = fsm.get_next_state(state=state, token_id=3) - assert fsm.generation == "(a" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [2, 3]) - state = fsm.get_next_state(state=state, token_id=3) - assert fsm.generation == "(aa" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Generate) - assert_expected_tensor_ids(instruction.tokens, [2, 3]) - state = fsm.get_next_state(state=state, token_id=2) - assert fsm.generation == "(aa)" - assert not fsm.is_final_state(state) - - instruction = fsm.get_next_instruction(state) - assert isinstance(instruction, Write) - assert_expected_tensor_ids(instruction.tokens, [4]) - state = fsm.get_next_state(state=state, token_id=4) - assert fsm.generation == "(aa)" - assert fsm.is_final_state(state) diff --git a/tests/fsm/test_parsing.py b/tests/fsm/test_parsing.py deleted file mode 100644 index 3f4c1ba4..00000000 --- a/tests/fsm/test_parsing.py +++ /dev/null @@ -1,206 +0,0 @@ -import importlib -from copy import copy - -import lark.lark -import pytest -from lark.indenter import DedentError -from lark.lexer import UnexpectedCharacters, UnexpectedToken - -from outlines.fsm.parsing import PartialLark, PartialPythonIndenter - - -@pytest.fixture -def cleanup_lark_import(): - yield - # Clean up lark.lark.LarkOptions._defaults - importlib.reload(lark.lark) - - -def test_partial_parsing(cleanup_lark_import): - lp = PartialLark.open_from_package( - "tests", - "partial_python.lark", - ["fsm"], - parser="lalr", - postlex=PartialPythonIndenter(), - start="file_input", - deterministic=True, - ) - - # End with a potentially unfinished NAME - parser_state = lp.parse("x") - assert parser_state.state_stack == [0] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 15) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - # End with an ignored token - parser_state = lp.parse("x ") - assert parser_state.state_stack == [0, 692] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 1) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - # Could be a complete `=` or the start of a `==` - parser_state = lp.parse("x =") - assert parser_state.state_stack == [0, 692] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert any( - term_info.terminal_name == "EQUAL" - for term_info in last_token.value.terminals_and_info - ) - assert not parser_state.value_stack - - parser_state = lp.parse("x = '") - assert parser_state.state_stack == [0, 58, 59] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 6) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - parser_state = lp.parse("x = 'hi") - assert parser_state.state_stack == [0, 58, 59] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 6, 6, 6) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - parser_state = lp.parse("x = ('hi") - assert parser_state.state_stack == [0, 58, 59, 254] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 6, 6, 6) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - parser_state = lp.parse("def") - assert parser_state.state_stack == [0] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - assert last_token.value.fsm_state_seq == (0, 26, 99, 100) - assert last_token.value.is_not_finished is True - assert not parser_state.value_stack - - # Now, try something incremental - last_lexer_state = parser_state.lexer.state - last_lexer_state.text += " blah()" - lp.parse_from_state(parser_state, is_end=False) - last_token = parser_state.lexer.state.last_token - assert not parser_state.value_stack - - last_lexer_state = parser_state.lexer.state - last_valid_token = last_lexer_state.last_token - assert last_valid_token.type == "RPAR" - assert not parser_state.value_stack - - # Something incremental and a little more complicated - parser_state = lp.parse("x = 1\ndef foo(x):\n ") - assert parser_state.state_stack == [0, 94, 600, 601, 602, 607, 608, 269] - last_lexer_state = parser_state.lexer.state - last_lexer_state.text += " return x" - - lp.parse_from_state(parser_state, is_end=False) - assert parser_state.state_stack == [ - 0, - 94, - 600, - 601, - 602, - 607, - 608, - 269, - 764, - 95, - 305, - ] - last_token = parser_state.lexer.state.last_token - assert last_token.type == "partial" - - with pytest.raises(UnexpectedToken): - lp.parse("def \n") - - with pytest.raises(UnexpectedToken): - lp.parse("def hot no") - - lp = PartialLark.open_from_package( - "tests", - "partial_python.lark", - ["fsm"], - parser="lalr", - postlex=PartialPythonIndenter(), - start="file_input", - use_value_stack=True, - ) - parser_state = lp.parse("x = ('hi") - lp.parse_from_state(parser_state, is_end=False) - assert len(parser_state.state_stack) == 4 - assert parser_state.value_stack[-1].type == "LPAR" - - -def test_sequential_parse_example(cleanup_lark_import): - input_tokens = [ - "x ", - "= ", - "1", - "\nde", - "f ", - "foo(", - "x)", - ":\n", - " ", - " return x", - " + 1", - "\n", - "z ", - "= ", - "foo(", - '"hi', - '")\n', - ] - vocab = sorted(set(input_tokens)) - - lp = PartialLark.open_from_package( - "tests", - "partial_python.lark", - ["fsm"], - parser="lalr", - postlex=PartialPythonIndenter(), - start="file_input", - deterministic=True, - ) - parser_state = lp.parse("") - - token_seq = "" - for i, token in enumerate(input_tokens): - token_seq += token - - lex_state = parser_state.lexer.state - lex_state.text = token_seq - - lp.parse_from_state(parser_state, is_end=False) - - next_vocab = set() - for test_token in vocab: - ps = copy(parser_state) - ls = ps.lexer.state - ls.text = token_seq + test_token - - if i + 1 < len(input_tokens) and test_token == input_tokens[i + 1]: - lp.parse_from_state(ps, is_end=False) - next_vocab.add(test_token) - else: - try: - lp.parse_from_state(ps, is_end=False) - next_vocab.add(test_token) - except (EOFError, UnexpectedToken, UnexpectedCharacters, DedentError): - pass - - if i + 1 == len(input_tokens): - assert all(tk in next_vocab for tk in ["\n", "\nde", " ", " + 1"]) From 11d3033421f398695c80b3d5c244fd54cfa469ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 14:57:37 +0200 Subject: [PATCH 11/20] Adopt the `src` layout and rename to outlines-core --- .github/scripts/build_sdist_and_wheel.sh | 8 +- .github/workflows/release_docker.yml | 36 - .github/workflows/release_pypi.yaml | 2 +- .github/workflows/tests.yml | 2 +- Dockerfile | 17 - README.md | 368 +------- benchmarks/asv.conf.json | 6 +- benchmarks/bench_json_schema.py | 6 +- benchmarks/bench_numba_compile.py | 4 +- benchmarks/bench_regex_guide.py | 4 +- benchmarks/common.py | 4 +- environment.yml | 3 +- outlines/fsm/parsing.py | 870 ------------------ pyproject.toml | 38 +- {outlines => src/outlines_core}/__init__.py | 5 +- {outlines => src/outlines_core}/caching.py | 10 +- .../outlines_core}/fsm/__init__.py | 0 {outlines => src/outlines_core}/fsm/fsm.py | 4 +- {outlines => src/outlines_core}/fsm/guide.py | 6 +- .../outlines_core}/fsm/json_schema.py | 0 {outlines => src/outlines_core}/fsm/regex.py | 2 +- {outlines => src/outlines_core}/fsm/types.py | 0 .../outlines_core}/models/__init__.py | 0 .../outlines_core}/models/tokenizer.py | 0 .../outlines_core}/models/transformers.py | 2 +- {outlines => src/outlines_core}/py.typed | 0 tests/fsm/test_fsm.py | 2 +- tests/fsm/test_guide.py | 2 +- tests/fsm/test_json_schema.py | 2 +- tests/fsm/test_regex.py | 18 +- tests/fsm/test_types.py | 2 +- tests/models/test_tokenizer.py | 2 +- tests/models/test_transformers.py | 2 +- tests/test_cache.py | 28 +- 34 files changed, 73 insertions(+), 1382 deletions(-) delete mode 100644 .github/workflows/release_docker.yml delete mode 100644 Dockerfile delete mode 100644 outlines/fsm/parsing.py rename {outlines => src/outlines_core}/__init__.py (55%) rename {outlines => src/outlines_core}/caching.py (95%) rename {outlines => src/outlines_core}/fsm/__init__.py (100%) rename {outlines => src/outlines_core}/fsm/fsm.py (92%) rename {outlines => src/outlines_core}/fsm/guide.py (98%) rename {outlines => src/outlines_core}/fsm/json_schema.py (100%) rename {outlines => src/outlines_core}/fsm/regex.py (99%) rename {outlines => src/outlines_core}/fsm/types.py (100%) rename {outlines => src/outlines_core}/models/__init__.py (100%) rename {outlines => src/outlines_core}/models/tokenizer.py (100%) rename {outlines => src/outlines_core}/models/transformers.py (99%) rename {outlines => src/outlines_core}/py.typed (100%) diff --git a/.github/scripts/build_sdist_and_wheel.sh b/.github/scripts/build_sdist_and_wheel.sh index ca770f5b..6a1b24d1 100755 --- a/.github/scripts/build_sdist_and_wheel.sh +++ b/.github/scripts/build_sdist_and_wheel.sh @@ -9,14 +9,14 @@ python -m build mkdir -p test-sdist cd test-sdist python -m venv venv-sdist -venv-sdist/bin/python -m pip install ../dist/outlines-*.tar.gz -venv-sdist/bin/python -c "import outlines" +venv-sdist/bin/python -m pip install ../dist/outlines_core-*.tar.gz +venv-sdist/bin/python -c "import outlines_core" cd .. # Check wheel install and imports mkdir -p test-wheel cd test-wheel python -m venv venv-wheel -venv-wheel/bin/python -m pip install ../dist/outlines-*.whl -venv-wheel/bin/python -c "import outlines" +venv-wheel/bin/python -m pip install ../dist/outlines_core-*.whl +venv-wheel/bin/python -c "import outlines_core" cd .. diff --git a/.github/workflows/release_docker.yml b/.github/workflows/release_docker.yml deleted file mode 100644 index 7a0ca4db..00000000 --- a/.github/workflows/release_docker.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: Release Docker - -on: - release: - types: - - created - workflow_dispatch: - inputs: - release_tag: - description: 'Release Tag (for manual dispatch)' - required: false - default: 'latest' -jobs: - release-job: - name: Build and publish on Docker Hub - runs-on: ubuntu-latest - environment: release - steps: - - name: Checkout - uses: actions/checkout@v4 - - name: Log in to Docker Hub - uses: docker/login-action@v3 - with: - username: ${{ secrets.DOCKERHUB_USERNAME }} - password: ${{ secrets.DOCKERHUB_TOKEN }} - - name: Build and push Docker image - uses: docker/build-push-action@v5 - with: - push: true - tags: | - outlinesdev/outlines:latest - outlinesdev/outlines:${{ github.event.release.tag_name || github.event.inputs.release_tag }} - build-args: | - BUILDKIT_CONTEXT_KEEP_GIT_DIR=true - - name: Clean docker cache - run: docker system prune --all --force diff --git a/.github/workflows/release_pypi.yaml b/.github/workflows/release_pypi.yaml index 9f78cfc4..eb4a9a7c 100644 --- a/.github/workflows/release_pypi.yaml +++ b/.github/workflows/release_pypi.yaml @@ -19,7 +19,7 @@ jobs: run: ./.github/scripts/build_sdist_and_wheel.sh - name: Check that the package version matches the Release name run: | - grep -Rq "^Version: ${GITHUB_REF:10}$" outlines.egg-info/PKG-INFO + grep -Rq "^Version: ${GITHUB_REF:10}$" outlines_core.egg-info/PKG-INFO - name: Publish to PyPi uses: pypa/gh-action-pypi-publish@v1.4.2 with: diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index b76d723f..8da373c3 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -35,7 +35,7 @@ jobs: pip install .[test] - name: Run tests run: | - pytest --cov=outlines + pytest --cov=outlines_core env: COVERAGE_FILE: .coverage.${{ steps.matrix-id.outputs.id }} - name: Upload coverage data diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index c6e5f067..00000000 --- a/Dockerfile +++ /dev/null @@ -1,17 +0,0 @@ -FROM python:3.10 - -WORKDIR /outlines - -RUN pip install --upgrade pip - -# Copy necessary build components -COPY pyproject.toml . -COPY outlines ./outlines - -# Install outlines and outlines[serve] -# .git required by setuptools-scm -RUN --mount=source=.git,target=.git,type=bind \ - pip install --no-cache-dir .[serve] - -# https://outlines-dev.github.io/outlines/reference/vllm/ -ENTRYPOINT ["python3", "-m", "outlines.serve.serve"] diff --git a/README.md b/README.md index 364e7724..769c87c1 100644 --- a/README.md +++ b/README.md @@ -1,367 +1 @@ -
- -# Outlines 〰️ - -Outlines Logo - -[![.txt Twitter][dottxt-twitter-badge]][dottxt-twitter] -[![Outlines Twitter][outlines-twitter-badge]][outlines-twitter] - -[![Contributors][contributors-badge]][contributors] -[![Downloads][downloads-badge]][pypistats] -[![Discord][discord-badge]][discord] - - -*Robust (structured) text generation.* - -Made with β€πŸ‘·οΈ by the team at [.txt](https://dottxt.co). - -
- - -``` bash -pip install outlines -``` - -First time here? Go to our [setup guide](https://outlines-dev.github.io/outlines/welcome) - -## Features - -- [x] πŸ€– [Multiple model integrations](https://outlines-dev.github.io/outlines/installation): OpenAI, transformers, llama.cpp, exllama2, mamba -- [x] πŸ–οΈ Simple and powerful prompting primitives based on the [Jinja templating engine](https://jinja.palletsprojects.com/) -- [x] πŸš„ [Multiple choices](#multiple-choices), [type constraints](#type-constraint) and dynamic stopping -- [x] ⚑ Fast [regex-structured generation](#efficient-regex-structured-generation) -- [x] πŸ”₯ Fast [JSON generation](#efficient-json-generation-following-a-pydantic-model) following a JSON schema or a Pydantic model -- [x] πŸ“ [Grammar-structured generation](#using-context-free-grammars-to-guide-generation) -- [x] 🐍 Interleave completions with loops, conditionals, and custom Python functions -- [x] πŸ’Ύ Caching of generations -- [x] πŸ—‚οΈ Batch inference -- [x] 🎲 Sample with the greedy, multinomial and beam search algorithms (and more to come!) -- [x] πŸš€ [Serve with vLLM](https://outlines-dev.github.io/outlines/reference/serve/vllm), with official Docker image, [`outlinesdev/outlines`](https://hub.docker.com/r/outlinesdev/outlines)! - - -Outlines γ€° has new releases and features coming every week. Make sure to ⭐ star and πŸ‘€ watch this repository, follow [@dottxtai][dottxt-twitter] to stay up to date! - -## Why should I use structured generation? - -* It doesn't add any overhead during inference (cost-free) -* It allows Open Source models to beat closed source models ([Mistral](https://x.com/dottxtai/status/1797692104023363765), [GPT-4](https://x.com/dottxtai/status/1798443290913853770)) -* [It speeds up inference](http://blog.dottxt.co/coalescence.html) -* [It improves the performance of base models (GSM8K)](http://blog.dottxt.co/performance-gsm8k.html) -* [It improves the performance of finetuned models (CoNNL)](https://predibase.com/blog/lorax-outlines-better-json-extraction-with-structured-generation-and-lora) -* [It improves model efficiency (less examples needed)](https://huggingface.co/blog/evaluation-structured-outputs) - -## .txt company - -
-Outlines Logo -
- -We started a company to keep pushing the boundaries of structured generation. Learn more about [.txt](https://twitter.com/dottxtai), and [give our .json API a try](https://h1xbpbfsf0w.typeform.com/to/ZgBCvJHF) if you need a hosted solution ✨ - -## Structured generation - -The first step towards reliability of systems that include large language models -is to ensure that there is a well-defined interface between their output and -user-defined code. **Outlines** provides ways to control the generation of -language models to make their output more predictable. - -### Multiple choices - -You can reduce the completion to a choice between multiple possibilities: - -``` python -import outlines - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") - -prompt = """You are a sentiment-labelling assistant. -Is the following review positive or negative? - -Review: This restaurant is just awesome! -""" - -generator = outlines.generate.choice(model, ["Positive", "Negative"]) -answer = generator(prompt) -``` - -### Type constraint - -You can instruct the model to only return integers or floats: - - -``` python -import outlines - -model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1") - -prompt = "result of 9 + 9 = 18result of 1 + 2 = " -answer = outlines.generate.format(model, int)(prompt) -print(answer) -# 3 - -prompt = "sqrt(2)=" -generator = outlines.generate.format(model, float) -answer = generator(prompt, max_tokens=10) -print(answer) -# 1.41421356 -``` - -### Efficient regex-structured generation - -Outlines also comes with fast regex-structured generation. In fact, the `choice` and -`format` functions above all use regex-structured generation under the -hood: - -``` python -import outlines - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") - -prompt = "What is the IP address of the Google DNS servers? " - -generator = outlines.generate.text(model) -unstructured = generator(prompt, max_tokens=30) - -generator = outlines.generate.regex( - model, - r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)", -) -structured = generator(prompt, max_tokens=30) - -print(unstructured) -# What is the IP address of the Google DNS servers? -# -# Passive DNS servers are at DNS servers that are private. -# In other words, both IP servers are private. The database -# does not contain Chelsea Manning - -print(structured) -# What is the IP address of the Google DNS servers? -# 2.2.6.1 -``` - -Unlike other libraries, regex-structured generation in Outlines is almost as fast -as non-structured generation. - -### Efficient JSON generation following a Pydantic model - -Outlines γ€° allows to guide the generation process so the output is *guaranteed* to follow a [JSON schema](https://json-schema.org/) or [Pydantic model](https://docs.pydantic.dev/latest/): - -```python -from enum import Enum -from pydantic import BaseModel, constr - -import outlines -import torch - - -class Weapon(str, Enum): - sword = "sword" - axe = "axe" - mace = "mace" - spear = "spear" - bow = "bow" - crossbow = "crossbow" - - -class Armor(str, Enum): - leather = "leather" - chainmail = "chainmail" - plate = "plate" - - -class Character(BaseModel): - name: constr(max_length=10) - age: int - armor: Armor - weapon: Weapon - strength: int - - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") - -# Construct structured sequence generator -generator = outlines.generate.json(model, Character) - -# Draw a sample -seed = 789001 - -character = generator("Give me a character description", seed=seed) - -print(repr(character)) -# Character(name='Anderson', age=28, armor=, weapon=, strength=8) - -character = generator("Give me an interesting character description", rng=rng) - -print(repr(character)) -# Character(name='Vivian Thr', age=44, armor=, weapon=, strength=125) -``` - -The method works with union types, optional types, arrays, nested schemas, etc. Some field constraints are [not supported yet](https://github.com/outlines-dev/outlines/issues/215), but everything else should work. - -### Efficient JSON generation following a JSON Schema - -Sometimes you just want to be able to pass a JSON Schema instead of a Pydantic model. We've got you covered: - -``` python -import outlines - -schema = '''{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}''' - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = outlines.generate.json(model, schema) -character = generator("Give me a character description") -``` - -### Using context-free grammars to guide generation - -Formal grammars rule the world, and Outlines makes them rule LLMs too. You can pass any context-free grammar in the EBNF format and Outlines will generate an output that is valid to this grammar: - -``` python -import outlines - -arithmetic_grammar = """ - ?start: expression - - ?expression: term (("+" | "-") term)* - - ?term: factor (("*" | "/") factor)* - - ?factor: NUMBER - | "-" factor - | "(" expression ")" - - %import common.NUMBER -""" - -model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1") -generator = outlines.generate.cfg(model, arithmetic_grammar) -sequence = generator("Alice had 4 apples and Bob ate 2. Write an expression for Alice's apples:") - -print(sequence) -# (8-2) -``` - -This was a very simple grammar, and you can use `outlines.generate.cfg` to generate syntactically valid Python, SQL, and much more than this. Any kind of structured text, really. All you have to do is search for "X EBNF grammar" on the web, and take a look at the [Outlines `grammars` module](https://github.com/outlines-dev/outlines/tree/main/outlines/grammars). - -### Open functions - -Outlines can infer the structure of the output from the signature of a function. The result is a dictionary, and can be passed directly to the function using the usual dictionary expansion syntax `**`: - -```python -import outlines - - -def add(a: int, b: int): - return a + b - -model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1") -generator = outlines.generate.json(model, add) -result = generator("Return json with two integers named a and b respectively. a is odd and b even.") - -print(add(**result)) -# 3 -``` - -A great advantage of passing functions directly to specify the structure is that the structure of the LLM will change with the function's definition. No need to change the code at several places! - -## Prompting - -Building prompts can get messy. **Outlines** makes it easier to write and manage -prompts by encapsulating templates inside "template functions". - -These functions make it possible to neatly separate the prompt logic from the -general program logic; they can be imported from other modules and libraries. - -Template functions require no superfluous abstraction, they use the Jinja2 -templating engine to help build complex prompts in a concise manner: - -``` python -import outlines - -examples = [ - ("The food was disgusting", "Negative"), - ("We had a fantastic night", "Positive"), - ("Recommended", "Positive"), - ("The waiter was rude", "Negative") -] - -@outlines.prompt -def labelling(to_label, examples): - """You are a sentiment-labelling assistant. - - {% for example in examples %} - {{ example[0] }} // {{ example[1] }} - {% endfor %} - {{ to_label }} // - """ - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") -prompt = labelling("Just awesome", examples) -answer = outlines.generate.text(model)(prompt, max_tokens=100) -``` - -## Join us - -- πŸ’‘ **Have an idea?** Come chat with us on [Discord][discord] -- πŸ”¨ **Want to contribute?** Consult our [contribution guide](https://outlines-dev.github.io/outlines/community/contribute/). -- 🐞 **Found a bug?** Open an [issue](https://github.com/outlines-dev/outlines/issues) - - -## Cite Outlines - -``` -@article{willard2023efficient, - title={Efficient Guided Generation for LLMs}, - author={Willard, Brandon T and Louf, R{\'e}mi}, - journal={arXiv preprint arXiv:2307.09702}, - year={2023} -} -``` - -[contributors]: https://github.com/outlines-dev/outlines/graphs/contributors -[contributors-badge]: https://img.shields.io/github/contributors/outlines-dev/outlines?style=flat-square&logo=github&logoColor=white&color=ECEFF4 -[dottxt-twitter]: https://twitter.com/dottxtai -[outlines-twitter]: https://twitter.com/OutlinesOSS -[discord]: https://discord.gg/R9DSu34mGd -[discord-badge]: https://img.shields.io/discord/1182316225284554793?color=81A1C1&logo=discord&logoColor=white&style=flat-square -[downloads-badge]: https://img.shields.io/pypi/dm/outlines?color=89AC6B&logo=python&logoColor=white&style=flat-square -[pypistats]: https://pypistats.org/packages/outlines -[dottxt-twitter-badge]: https://img.shields.io/twitter/follow/dottxtai?style=social -[outlines-twitter-badge]: https://img.shields.io/twitter/follow/OutlinesOSS?style=social +# Outlines-core diff --git a/benchmarks/asv.conf.json b/benchmarks/asv.conf.json index f57db9a0..3dc3f620 100644 --- a/benchmarks/asv.conf.json +++ b/benchmarks/asv.conf.json @@ -1,7 +1,7 @@ { "version": 1, - "project": "Outlines", - "project_url": "https://outlines-dev.github.io/outlines/", + "project": "Outlines-core", + "project_url": "https://outlines-dev.github.io/outlines-core/", "repo": "..", "branches": [ "HEAD" @@ -11,7 +11,7 @@ "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}", ], "environment_type": "virtualenv", - "show_commit_url": "https://github.com/outlines-dev/outlines/commit/", + "show_commit_url": "https://github.com/outlines-dev/outlines-core/commit/", "benchmark_dir": ".", "env_dir": "env", "results_dir": "results", diff --git a/benchmarks/bench_json_schema.py b/benchmarks/bench_json_schema.py index 8d1ceeb2..c85982c7 100644 --- a/benchmarks/bench_json_schema.py +++ b/benchmarks/bench_json_schema.py @@ -1,6 +1,6 @@ -from outlines.caching import cache_disabled -from outlines.fsm.guide import RegexGuide -from outlines.fsm.json_schema import build_regex_from_schema +from outlines_core.caching import cache_disabled +from outlines_core.fsm.guide import RegexGuide +from outlines_core.fsm.json_schema import build_regex_from_schema from .common import ensure_numba_compiled, setup_tokenizer # noqa: E402 diff --git a/benchmarks/bench_numba_compile.py b/benchmarks/bench_numba_compile.py index 2713707e..35edc953 100644 --- a/benchmarks/bench_numba_compile.py +++ b/benchmarks/bench_numba_compile.py @@ -3,8 +3,8 @@ import interegular import numba -from outlines.caching import cache_disabled -from outlines.fsm import regex +from outlines_core.caching import cache_disabled +from outlines_core.fsm import regex from .common import setup_tokenizer diff --git a/benchmarks/bench_regex_guide.py b/benchmarks/bench_regex_guide.py index 099f94df..eeb1f983 100644 --- a/benchmarks/bench_regex_guide.py +++ b/benchmarks/bench_regex_guide.py @@ -1,5 +1,5 @@ -from outlines.caching import cache_disabled -from outlines.fsm.guide import RegexGuide +from outlines_core.caching import cache_disabled +from outlines_core.fsm.guide import RegexGuide from .common import ensure_numba_compiled, setup_tokenizer diff --git a/benchmarks/common.py b/benchmarks/common.py index 7d999ea9..db25593d 100644 --- a/benchmarks/common.py +++ b/benchmarks/common.py @@ -1,7 +1,7 @@ from transformers import AutoTokenizer -from outlines.fsm.guide import RegexGuide -from outlines.models.transformers import TransformerTokenizer +from outlines_core.fsm.guide import RegexGuide +from outlines_core.models.transformers import TransformerTokenizer def setup_tokenizer(): diff --git a/environment.yml b/environment.yml index c267f86a..3e7a92bb 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,7 @@ # $ conda env create -f environment.yml # `mamba` works too for this command # $ conda activate outlines-dev # -name: outlines-dev +name: outlines-core-dev channels: - conda-forge - huggingface @@ -12,7 +12,6 @@ dependencies: - jinja2 - numpy - pydantic - - scipy - pytest - pre-commit - referencing diff --git a/outlines/fsm/parsing.py b/outlines/fsm/parsing.py deleted file mode 100644 index e4fa7b76..00000000 --- a/outlines/fsm/parsing.py +++ /dev/null @@ -1,870 +0,0 @@ -from copy import copy, deepcopy -from dataclasses import dataclass -from functools import lru_cache -from typing import Any, Dict, FrozenSet, Iterator, Optional, Set, Tuple, Union - -import interegular -from interegular.fsm import FSM -from interegular.patterns import Unsupported -from lark import Lark, Token -from lark.common import LexerConf, ParserConf -from lark.exceptions import LexError, UnexpectedInput -from lark.indenter import Indenter -from lark.lexer import ( - BasicLexer, - ContextualLexer, - LexerState, - LexerThread, - Scanner, - UnexpectedCharacters, - UnexpectedToken, - _create_unless, -) -from lark.parser_frontends import ( - ParsingFrontend, - PostLexConnector, - _validate_frontend_args, -) -from lark.parsers.lalr_analysis import ( - Action, - IntParseTable, - LALR_Analyzer, - ParseTable, - Shift, -) -from lark.parsers.lalr_interactive_parser import InteractiveParser -from lark.parsers.lalr_parser import LALR_Parser, ParseConf, ParserState, _Parser - -from outlines.fsm.regex import ( - fsm_union, - get_sub_fsms_from_seq, - get_token_transition_keys, - make_deterministic_fsm, - walk_fsm, -) - -PartialParseState = Tuple[str, int] -ParseStateType = Union[int, FrozenSet] - - -@dataclass -class PartialTerminalInfo: - priority: int - terminal_name: str - can_transition: bool - is_final: bool - - -@dataclass -class PartialTokensInfo: - fsm_state_seq: Tuple[int, ...] - is_not_finished: bool - terminals_and_info: Tuple[PartialTerminalInfo, ...] - final_terminals_and_info: Tuple[PartialTerminalInfo, ...] - - -class PartialParserConf(ParserConf): - __serialize_fields__ = ( - "rules", - "start", - "parser_type", - "deterministic", - "use_value_stack", - ) - - def __init__(self, rules, callbacks, start, deterministic, use_value_stack): - super().__init__(rules, callbacks, start) - self.deterministic = deterministic - self.use_value_stack = use_value_stack - - -class PartialLark(Lark): - __serialize_fields__ = ( - "parser", - "rules", - "options", - "deterministic", - "use_value_stack", - ) - - def __init__(self, grammar, **options): - # TODO: Could've extended `LarkOptions`, but all these extensions are - # already way too much (and brittle). This library really needs a - # complete refactoring. - self.deterministic = options.pop("deterministic", False) - self.use_value_stack = options.pop("use_value_stack", False) - options["regex"] = True - super().__init__(grammar, **options) - assert self.options.parser == "lalr" - - def _build_lexer(self, dont_ignore: bool = False) -> "PartialBasicLexer": - lexer_conf = self.lexer_conf - if dont_ignore: - from copy import copy - - lexer_conf = copy(lexer_conf) - lexer_conf.ignore = () - - return PartialBasicLexer(lexer_conf) - - def _build_parser(self) -> "PartialParsingFrontend": - self._prepare_callbacks() - _validate_frontend_args(self.options.parser, self.options.lexer) - parser_conf = PartialParserConf( - self.rules, - self._callbacks, - self.options.start, - self.deterministic, - self.use_value_stack, - ) - - # This is `_construct_parsing_frontend` expanded/inlined - parser_type = self.options.parser - lexer_type = self.options.lexer - lexer_conf = self.lexer_conf - - assert isinstance(lexer_conf, LexerConf) - assert isinstance(parser_conf, ParserConf) - parser_conf.parser_type = parser_type - self.lexer_conf.lexer_type = lexer_type - return PartialParsingFrontend(lexer_conf, parser_conf, self.options) - - def __repr__(self): - return "{}(open({!r}), parser={!r}, lexer={!r}, ...)".format( - type(self).__name__, - self.source_path, - self.options.parser, - self.options.lexer, - ) - - def parse_from_state(self, parse_state: "PartialParseState", is_end=False): - return self.parser.parser.parser.parse_from_state(parse_state, is_end=is_end) - - -class PartialLexerThread(LexerThread): - def __copy__(self): - return type(self)(copy(self.lexer), copy(self.state)) - - def __repr__(self): - return f"{type(self).__name__}(lexer={self.lexer!r}, state={self.state!r})" - - -class PartialPostLexConnector(PostLexConnector): - def __copy__(self): - return type(self)(self.lexer, copy(self.postlexer)) - - def __repr__(self): - return ( - f"{type(self).__name__}(lexer={self.lexer!r}, postlexer={self.postlexer!r})" - ) - - -class PartialParsingFrontend(ParsingFrontend): - def __init__(self, lexer_conf, parser_conf, options, parser=None): - assert parser_conf.parser_type == "lalr" - - options._plugins["LALR_Parser"] = PartialLALRParser - options._plugins["BasicLexer"] = PartialBasicLexer - options._plugins["ContextualLexer"] = PartialContextualLexer - options._plugins["LexerThread"] = PartialLexerThread - - super().__init__(lexer_conf, parser_conf, options, parser=parser) - - if lexer_conf.postlex: - self.lexer = PartialPostLexConnector(self.lexer.lexer, lexer_conf.postlex) - - self._termset_fsm_info = None - self._symbols_to_states: Optional[ - Dict[str, Set[Tuple[ParseStateType, Action]]] - ] = None - self._reverse_shifts: Optional[ - Dict[ParseStateType, Dict[str, Set[ParseStateType]]] - ] = None - # self._state_transition_map: Optional[ - # Dict[Tuple[ParseStateType, str], Set[ParseStateType]] - # ] = None - - def _compute_maps( - self, - ): - """Compute state transition and symbols-to-states maps.""" - self._reverse_shifts = {} - self._symbols_to_states = {} - - parse_table = self.parser.parser.parse_table - - for from_state, symbols_to_ops in parse_table.states.items(): - for symbol, op in symbols_to_ops.items(): - if op[0] == Shift: - symbols_to_from_states = self._reverse_shifts.setdefault(op[1], {}) - symbols_to_from_states.setdefault(symbol, set()).add(from_state) - self._symbols_to_states.setdefault(symbol, set()).add((from_state, op)) - - # # TODO: This approach is very wasteful. - # context_lexer = get_contextual_lexer(self) - # self._state_transition_map = {} - # - # for from_state, transitions in parse_table.states.items(): - # for symbol, action in transitions.items(): - # # TODO: Filter non-terminals - # if symbol not in context_lexer.root_lexer.terminals_by_name: - # continue - # - # if action[0] is Shift: - # self._state_transition_map.setdefault( - # (from_state, symbol), set() - # ).add(action[1]) - # continue - # - # antecedent_state_seqs = parse_to_terminal(self, [(from_state,)], symbol) - # - # for antecedent_state_seq in antecedent_state_seqs: - # antecedent_state = antecedent_state_seq[-1] - # self._state_transition_map.setdefault( - # (from_state, symbol), set() - # ).add(antecedent_state) - - def _compute_termset_fsm_info(self): - """Collect and return information about terminal symbol sets and their FSMs. - - Terminal symbol sets (or "termsets") are ordered sequences of terminal - symbols that are used by each parser state. Associated with each is a - collection of FSMs for each terminal and a single parse state FSM that is - the union of each terminal's FSM. - - This constructs a list of tuples containing the termset, the set of - parse states that use the termsets, parse state FSMs, and information - mapping the components of the parse state FSMs to their terminal symbol - FSMs. - - """ - context_lexer = get_contextual_lexer(self) - termsets_to_fsms = {} - termsets_to_parse_states: Dict[Tuple[str, ...], Set[ParseStateType]] = {} - for parse_state, lexer in context_lexer.lexers.items(): - scanner = lexer.scanner - key = tuple(term.name for term in scanner.terminals) - termsets_to_fsms[key] = (scanner.fsm, scanner.fsms_to_trans_finals) - termsets_to_parse_states.setdefault(key, set()).add(parse_state) - - self._termset_fsm_info = [ - ( - termset, - frozenset(termsets_to_parse_states[termset]), - fsm, - fsms_to_trans_finals, - ) - for termset, (fsm, fsms_to_trans_finals) in termsets_to_fsms.items() - ] - - @property - def termset_fsm_info(self): - if self._termset_fsm_info is None: - self._compute_termset_fsm_info() - return self._termset_fsm_info - - @property - def symbols_to_states(self): - if self._symbols_to_states is None: - self._compute_maps() - return self._symbols_to_states - - @property - def reverse_shifts(self): - if self._reverse_shifts is None: - self._compute_maps() - return self._reverse_shifts - - # @property - # def state_transition_map(self): - # if self._state_transition_map is None: - # self._compute_maps() - # return self._state_transition_map - - -class PartialLALRParser(LALR_Parser): - def __init__(self, parser_conf, debug=False, strict=False): - analysis = LALR_Analyzer( - parser_conf, debug=debug if not parser_conf.deterministic else True - ) - analysis.compute_lalr() - callbacks = parser_conf.callbacks - - self.parser_conf = parser_conf - self._parse_table = analysis.parse_table - - if parser_conf.deterministic: - old_to_new = {} - - def to_tuple(v): - new = old_to_new.get(v) - if new is None: - new = tuple(sorted(v, key=lambda y: str(y))) - old_to_new[v] = new - return new - - enum = sorted( - self._parse_table.states.keys(), - key=lambda x: str(sorted(x, key=lambda y: str(y))), - ) - - new_states = {} - for s in enum: - transitions = { - term: op if op[0] is not Shift else (op[0], to_tuple(op[1])) - for term, op in self._parse_table.states[s].items() - } - new_states[to_tuple(s)] = transitions - - self._parse_table = type(self._parse_table)( - new_states, - {k: to_tuple(v) for k, v in self._parse_table.start_states.items()}, - {k: to_tuple(v) for k, v in self._parse_table.end_states.items()}, - ) - - if not debug: - self._parse_table = IntParseTable.from_ParseTable(self._parse_table) - self.states_to_rulesets = dict( - zip(self._parse_table.states.keys(), new_states.keys()) - ) - - self.parser = PartialParser( - self._parse_table, - callbacks, - debug, - use_value_stack=parser_conf.use_value_stack, - ) - - @classmethod - def deserialize(cls, data, memo, callbacks, debug=False): - inst = cls.__new__(cls) - inst._parse_table = ParseTable.deserialize(data, memo) - inst.parser = PartialParser(inst._parse_table, callbacks, debug) - return inst - - -class PartialParserState(ParserState): - __slots__ = "use_value_stack" - - def __init__( - self, - parse_conf, - lexer, - state_stack=None, - value_stack=None, - use_value_stack=False, - ): - super().__init__( - parse_conf, lexer, state_stack=state_stack, value_stack=value_stack - ) - self.use_value_stack = use_value_stack - - def feed_token(self, token, is_end=False): - if token.type == "partial": - # If none of the potential terminals can transition, we need to know now - current_state = self.state_stack[-1] - current_lexer = get_contextual_lexer(self.lexer).lexers[current_state] - - # We have to feed the token and determine whether or not at least - # one terminal is consistent with the stack; otherwise, we'll miss - # invalid REDUCE cases. - # TODO: We should track separate parses conditional on possible - # token/symbol types, then we can coherently reuse the following - # results instead of recomputing it later. - can_transition = False - for terminal_info in token.value.terminals_and_info: - if terminal_info.terminal_name not in current_lexer.ignore_types: - test_token = Token.new_borrow_pos( - terminal_info.terminal_name, "", token - ) - - stack = copy(self.state_stack) - try: - self.feed_token_no_stack(test_token, is_end=is_end) - can_transition = True - break - except UnexpectedToken: - continue - finally: - self.state_stack = stack - else: - can_transition = True - - if not can_transition: - expected = { - s - for s in self.parse_conf.states[current_state].keys() - if s.isupper() - } - raise UnexpectedToken( - token, expected, state=self, interactive_parser=None - ) - - elif self.use_value_stack: - super().feed_token(token, is_end=is_end) - else: - self.feed_token_no_stack(token, is_end=is_end) - - def feed_token_no_stack(self, token, is_end=False): - """ - This is a copy of `ParserState.feed_token` with all the value stack - steps removed. Since we're not exactly parsing in order to obtain a - CST or anything similar, we can avoid the growing expense of tracking - the parse tree. - """ - state_stack = self.state_stack - states = self.parse_conf.states - end_state = self.parse_conf.end_state - - while True: - state = state_stack[-1] - try: - action, arg = states[state][token.type] - except KeyError: - expected = {s for s in states[state].keys() if s.isupper()} - raise UnexpectedToken( - token, expected, state=self, interactive_parser=None - ) - - assert arg != end_state - - if action is Shift: - # shift once and return - assert not is_end - state_stack.append(arg) - return - else: - # reduce+shift as many times as necessary - rule = arg - size = len(rule.expansion) - if size: - del state_stack[-size:] - - _action, new_state = states[state_stack[-1]][rule.origin.name] - assert _action is Shift - state_stack.append(new_state) - - if is_end and state_stack[-1] == end_state: - return - - def __copy__(self): - return type(self)( - self.parse_conf, - copy(self.lexer), - copy(self.state_stack), - deepcopy(self.value_stack), - use_value_stack=self.use_value_stack, - ) - - def __repr__(self): - return f"{type(self).__name__}(lexer={self.lexer!r}, state_stack={self.state_stack!r})" - - -class PartialParser(_Parser): - def __init__(self, parse_table, callbacks, debug=False, use_value_stack=False): - super().__init__(parse_table, callbacks, debug=debug) - self.use_value_stack = use_value_stack - - def parse( - self, lexer, start, value_stack=None, state_stack=None, start_interactive=False - ): - parse_conf = ParseConf(self.parse_table, self.callbacks, start) - parser_state = PartialParserState( - parse_conf, copy(lexer), state_stack, value_stack, self.use_value_stack - ) - if start_interactive: - return InteractiveParser(self, parser_state, parser_state.lexer) - return self.parse_from_state(parser_state) - - def parse_from_state(self, state, last_token=None, is_end=False): - try: - token = last_token - for token in state.lexer.lex(state): - state.feed_token(token) - - if is_end and (not token or token.type != "partial"): - end_token = ( - Token.new_borrow_pos("$END", "", token) - if token - else Token("$END", "", 0, 1, 1) - ) - state.feed_token(end_token, True) - - return state - except UnexpectedInput as e: - try: - e.interactive_parser = InteractiveParser(self, state, state.lexer) - except NameError: - pass - raise e - except Exception: - if self.debug: - print("") - print("STATE STACK DUMP") - print("----------------") - for i, s in enumerate(state.state_stack): - print("%d)" % i, s) - print("") - - raise - - -class PartialScanner(Scanner): - @classmethod - @lru_cache - def construct_terminal_fsm(cls, terminal): - # TODO: This should really be done at the lexer/parser level so that - # the lifetime of these objects is tied to the parser itself. - regex_str = terminal.pattern.to_regexp() - pattern = interegular.parse_pattern(regex_str) - fsm, _ = make_deterministic_fsm(pattern.to_fsm().reduce()) - return fsm, pattern.prefix_postfix - - def __init__(self, terminals, g_regex_flags, re_, use_bytes, match_whole=False): - self.terminals = terminals - self.g_regex_flags = g_regex_flags - self.use_bytes = use_bytes - self.match_whole = match_whole - self.allowed_types = {t.name for t in self.terminals} - self._mres = None - - fsms = [] - for t in self.terminals: - fsm, prefix_postfix = self.construct_terminal_fsm(t) - - # TODO FIXME: We don't support this right now. - assert prefix_postfix == (0, 0) - - fsms.append(fsm) - - self.fsm, self.fsms_to_trans_finals = fsm_union(fsms) - - def get_terminals_info( - self, fsm_state_seq - ) -> Tuple[Tuple[PartialTerminalInfo, ...], Tuple[PartialTerminalInfo, ...]]: - """Get the possible terminal symbols for an FSM state sequence.""" - terminals_and_info: Tuple[PartialTerminalInfo, ...] = () - final_terminals_and_info: Tuple[PartialTerminalInfo, ...] = () - for i, (fsm_id, fsm_reads_more, in_final) in enumerate( - get_sub_fsms_from_seq(fsm_state_seq, self.fsms_to_trans_finals) - ): - terminal_name = self.terminals[fsm_id].name - info = PartialTerminalInfo(i, terminal_name, fsm_reads_more, in_final) - terminals_and_info += (info,) - if in_final: - final_terminals_and_info += (info,) - - return terminals_and_info, final_terminals_and_info - - def match(self, text, pos, last_fsm_state_seq: Optional[Tuple[int, ...]] = None): - """Determine an FSM match over `text` starting at `pos` and continuing `last_fsm_state_seq`.""" - - start_pos = pos - - if last_fsm_state_seq: - assert len(last_fsm_state_seq) > 1 - start_pos += len(last_fsm_state_seq) - 1 - start_state = last_fsm_state_seq[-1] - else: - start_state = self.fsm.initial - - text_part = text[start_pos:] - - text_transitions = get_token_transition_keys( - self.fsm.fsm_info.alphabet_symbol_mapping, - self.fsm.fsm_info.alphabet_anything_value, - text_part, - ) - - state_seq = walk_fsm( - self.fsm, - text_transitions, - start_state, - full_match=self.match_whole, - ) - - if not state_seq: - return None - - if last_fsm_state_seq: - res = last_fsm_state_seq + tuple(state_seq) - else: - res = (start_state,) + tuple(state_seq) - - return res - - -class PartialContextualLexer(ContextualLexer): - def __init__(self, conf: "LexerConf", states, always_accept=()): - terminals = list(conf.terminals) - terminals_by_name = conf.terminals_by_name - - trad_conf = copy(conf) - trad_conf.terminals = terminals - - lexer_by_symbols: Dict = {} - self.lexers = {} - for state, accepts in states.items(): - key = frozenset(accepts) - try: - lexer = lexer_by_symbols[key] - except KeyError: - accepts = set(accepts) | set(conf.ignore) | set(always_accept) - lexer_conf = copy(trad_conf) - lexer_conf.terminals = [ - terminals_by_name[n] for n in accepts if n in terminals_by_name - ] - lexer = PartialBasicLexer(lexer_conf) - lexer_by_symbols[key] = lexer - - self.lexers[state] = lexer - - assert trad_conf.terminals is terminals - self.root_lexer = PartialBasicLexer(trad_conf) - - def lex(self, lexer_state: LexerState, parser_state: Any) -> Iterator[Token]: - try: - while True: - lexer = self.lexers[parser_state.position] - yield lexer.next_token(lexer_state, parser_state) - except EOFError: - pass - - -class PartialBasicLexer(BasicLexer): - def __init__(self, conf: "LexerConf"): - super().__init__(conf) - # Eagerly construct the scanner - self._build_scanner() - - def _build_scanner(self): - # This seems incredibly convoluted: `lark` creates callback-triggered - # nested scanners for regex-defined terminals that overlap with - # string-defined terminals when both types of terminals have the same - # priority. Unless I'm missing something important, why not simply - # reorder the terminals so that the string-defined ones come before the - # regex-defined ones? - terminals, self.callback = _create_unless( - self.terminals, self.g_regex_flags, self.re, self.use_bytes - ) - - # We can't let people arbitrarily mess with the scanning process. - assert not self.user_callbacks - # for type_, f in self.user_callbacks.items(): - # if type_ in self.callback: - # # Already a callback there, probably UnlessCallback - # self.callback[type_] = CallChain( - # self.callback[type_], f, lambda t: t.type == type_ - # ) - # else: - # self.callback[type_] = f - - # We used the "callback" results to reorder the terminals (see the - # comments above). - for terminal_name, callback in self.callback.items(): - terminal = self.terminals_by_name[terminal_name] - for sub_terminal in callback.scanner.terminals: - self.terminals.remove(sub_terminal) - idx = self.terminals.index(terminal) - self.terminals.insert(idx, sub_terminal) - - self._scanner = PartialScanner( - self.terminals, self.g_regex_flags, self.re, self.use_bytes - ) - - def match(self, text, pos, last_fsm_state_seq=None): - return self.scanner.match(text, pos, last_fsm_state_seq) - - def next_token(self, lex_state: LexerState, parser_state: Any = None) -> Token: - last_token = lex_state.last_token - - last_fsm_state_seq = None - if last_token and last_token.type == "partial": - # Continue from last partial lexer state - last_fsm_state_seq = last_token.value.fsm_state_seq - - line_ctr = lex_state.line_ctr - end_pos = line_ctr.char_pos + ( - len(last_fsm_state_seq) - 1 if last_fsm_state_seq else 0 - ) - while end_pos < len(lex_state.text): - res = self.match(lex_state.text, line_ctr.char_pos, last_fsm_state_seq) - - if not res: - if ( - not last_fsm_state_seq - or last_fsm_state_seq[-1] not in self.scanner.fsm.finals - ): - allowed = self.scanner.allowed_types - self.ignore_types - if not allowed: - allowed = {""} - raise UnexpectedCharacters( - lex_state.text, - line_ctr.char_pos, - line_ctr.line, - line_ctr.column, - allowed=allowed, - token_history=lex_state.last_token and [lex_state.last_token], - state=parser_state, - terminals_by_name=self.terminals_by_name, - ) - - # The partial match might be complete now - fsm_state_seq = last_token.value.fsm_state_seq - terminals_and_info = last_token.value.terminals_and_info - final_terminals_and_info = last_token.value.final_terminals_and_info - else: - fsm_state_seq = res - ( - terminals_and_info, - final_terminals_and_info, - ) = self.scanner.get_terminals_info(fsm_state_seq) - - priority_terminal_info = ( - final_terminals_and_info[0] - if final_terminals_and_info - else terminals_and_info[0] - ) - - is_not_finished = ( - not priority_terminal_info.is_final - or priority_terminal_info.can_transition - or len(terminals_and_info) > 1 - ) - - start_pos = line_ctr.char_pos - end_pos = start_pos + len(fsm_state_seq) - 1 - - if end_pos >= len(lex_state.text) and is_not_finished: - type_name = "partial" - token_value = PartialTokensInfo( - fsm_state_seq, - is_not_finished, - terminals_and_info, - final_terminals_and_info, - ) - # Don't update the line counter states until we've finished - value = "" - else: - type_name = priority_terminal_info.terminal_name - # The token value should contain all partial scan parts in this - # case - value = token_value = lex_state.text[start_pos:end_pos] - - assert isinstance(self.callback, Dict) - - if type_name not in self.ignore_types: - t = Token( - type_name, - token_value, - line_ctr.char_pos, - line_ctr.line, - line_ctr.column, - ) - - line_ctr.feed(value, type_name in self.newline_types) - - t.end_line = line_ctr.line - t.end_column = line_ctr.column - t.end_pos = line_ctr.char_pos - if t.type in self.callback: - t = self.callback[t.type](t) - if not isinstance(t, Token): - raise LexError( - "Callbacks must return a token (returned %r)" % t - ) - lex_state.last_token = t - return t - - if type_name in self.callback: - t2 = Token( - type_name, value, line_ctr.char_pos, line_ctr.line, line_ctr.column - ) - self.callback[type_name](t2) - - line_ctr.feed(value, type_name in self.newline_types) - - last_fsm_state_seq = None - - raise EOFError(self) - - -class PartialIndenter(Indenter): - """An `Indenter` that doesn't reset its state every time `process` is called.""" - - def process(self, stream): - return self._process(stream) - - def _process(self, stream): - for token in stream: - # These were previously *after* the `yield`, but that makes the - # state tracking unnecessarily convoluted. - if token.type in self.OPEN_PAREN_types: - self.paren_level += 1 - elif token.type in self.CLOSE_PAREN_types: - self.paren_level -= 1 - if self.paren_level < 0: - raise UnexpectedToken(token, []) - - if token.type == self.NL_type: - yield from self.handle_NL(token) - else: - yield token - - # TODO: What do we want to do here? - # while len(self.indent_level) > 1: - # self.indent_level.pop() - # yield Token(self.DEDENT_type, "") - - def accepts_token_type(self, token_type): - if token_type in self.CLOSE_PAREN_types and self.paren_level - 1 < 0: - return False - - # TODO: - # if token_type == self.NL_type and self.paren_level == 0: - # ... - # return False - - return True - - def __copy__(self): - res = type(self)() - res.paren_level = self.paren_level - res.indent_level = copy(self.indent_level) - return res - - def __repr__(self): - return f"{type(self).__name__}(paren_level={self.paren_level!r}, indent_level={self.indent_level!r})" - - -class PartialPythonIndenter(PartialIndenter): - NL_type = "_NEWLINE" - OPEN_PAREN_types = ["LPAR", "LSQB", "LBRACE"] - CLOSE_PAREN_types = ["RPAR", "RSQB", "RBRACE"] - INDENT_type = "_INDENT" - DEDENT_type = "_DEDENT" - tab_len = 8 - - -def get_contextual_lexer(x: Union[PartialLexerThread, PartialParsingFrontend]): - if isinstance(x.lexer, ContextualLexer): - return x.lexer - else: - return x.lexer.lexer - - -def terminals_to_fsms(lp: PartialLark) -> Dict[str, FSM]: - """Construct a ``dict`` mapping terminal symbol names to their finite state machines.""" - - symbol_names_and_fsms = {} - for terminal in lp.terminals: - pattern = interegular.parse_pattern(terminal.pattern.to_regexp()) - # TODO: Use `pyparser.terminals[0].pattern.flags`? - try: - fsm, _ = make_deterministic_fsm(pattern.to_fsm().reduce()) - except Unsupported: - fsm = None - - symbol_names_and_fsms[terminal.name] = fsm - - return symbol_names_and_fsms diff --git a/pyproject.toml b/pyproject.toml index e8d1abf2..e828e20d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,9 +3,9 @@ requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2"] build-backend = "setuptools.build_meta" [project] -name = "outlines" +name = "outlines_core" authors= [{name = "Outlines Developers"}] -description = "Probabilistic Generative Model Programming" +description = "Structured Text Generation in Rust" requires-python = ">=3.8" license = {text = "Apache-2.0"} keywords=[ @@ -25,9 +25,6 @@ classifiers = [ ] dependencies = [ "interegular", - "jinja2", - "lark", - "nest_asyncio", "numpy<2.0.0", "cloudpickle", "diskcache", @@ -35,12 +32,9 @@ dependencies = [ "numba", "referencing", "jsonschema", - "requests", "tqdm", "datasets", "typing_extensions", - "pycountry", - "pyairports", ] dynamic = ["version"] @@ -55,36 +49,30 @@ test = [ "diff-cover", "accelerate", "beartype<0.16.0", - "responses", "huggingface_hub", "torch", "transformers", "pillow", ] -serve = [ - "vllm>=0.3.0", - "uvicorn", - "fastapi", - "pydantic>=2.0", -] [project.urls] -homepage = "https://github.com/outlines-dev/outlines" -documentation = "https://outlines-dev.github.io/outlines/" -repository = "https://github.com/outlines-dev/outlines" +homepage = "https://github.com/outlines-dev/outlines-core" +documentation = "https://outlines-dev.github.io/outlines-core/" +repository = "https://github.com/outlines-dev/outlines-core/" [project.readme] file="README.md" content-type = "text/markdown" [tool.setuptools] -packages = ["outlines"] +packages = ["outlines_core"] +package-dir = {"" = "src"} [tool.setuptools.package-data] "outlines" = ["py.typed"] [tool.setuptools_scm] -write_to = "outlines/_version.py" +write_to = "src/outlines_core/_version.py" [tool.pytest.ini_options] testpaths = ["tests"] @@ -104,10 +92,7 @@ enable_incomplete_feature = ["Unpack"] [[tool.mypy.overrides]] module = [ - "exllamav2.*", - "jinja2", "jsonschema.*", - "nest_asyncio", "numpy.*", "cloudpickle.*", "diskcache.*", @@ -117,20 +102,15 @@ module = [ "torch.*", "transformers.*", "huggingface_hub", - "lark.*", "interegular.*", "datasets.*", "numba.*", - "requests.*", - "responses.*", - "uvicorn.*", - "fastapi.*", ] ignore_missing_imports = true [tool.coverage.run] omit = [ - "outlines/_version.py", + "outlines_core/_version.py", "tests/*", ] branch = true diff --git a/outlines/__init__.py b/src/outlines_core/__init__.py similarity index 55% rename from outlines/__init__.py rename to src/outlines_core/__init__.py index f8d13c66..1a322f2b 100644 --- a/outlines/__init__.py +++ b/src/outlines_core/__init__.py @@ -1,10 +1,9 @@ """Outlines is a Generative Model Programming Framework.""" -import outlines.models -from outlines.caching import clear_cache, disable_cache, get_cache +import outlines_core.models +from outlines_core.caching import clear_cache, disable_cache, get_cache __all__ = [ "clear_cache", "disable_cache", "get_cache", - "vectorize", ] diff --git a/outlines/caching.py b/src/outlines_core/caching.py similarity index 95% rename from outlines/caching.py rename to src/outlines_core/caching.py index 6fdda621..92a08415 100644 --- a/outlines/caching.py +++ b/src/outlines_core/caching.py @@ -48,10 +48,12 @@ def get_cache(): environment variable. """ - from outlines._version import __version__ as outlines_version # type: ignore + from outlines_core._version import ( + __version__ as outlines_core_version, # type: ignore + ) home_dir = os.path.expanduser("~") - cache_dir = os.environ.get("OUTLINES_CACHE_DIR", f"{home_dir}/.cache/outlines") + cache_dir = os.environ.get("OUTLINES_CACHE_DIR", f"{home_dir}/.cache/outlines-core") memory = Cache( cache_dir, eviction_policy="none", @@ -60,9 +62,9 @@ def get_cache(): ) # ensure if version upgrade occurs, old cache is pruned - if outlines_version != memory.get("__version__"): + if outlines_core_version != memory.get("__version__"): memory.clear() - memory["__version__"] = outlines_version + memory["__version__"] = outlines_core_version return memory diff --git a/outlines/fsm/__init__.py b/src/outlines_core/fsm/__init__.py similarity index 100% rename from outlines/fsm/__init__.py rename to src/outlines_core/fsm/__init__.py diff --git a/outlines/fsm/fsm.py b/src/outlines_core/fsm/fsm.py similarity index 92% rename from outlines/fsm/fsm.py rename to src/outlines_core/fsm/fsm.py index bfcf55c0..4daf3c86 100644 --- a/outlines/fsm/fsm.py +++ b/src/outlines_core/fsm/fsm.py @@ -1,10 +1,10 @@ import warnings from typing import TYPE_CHECKING, Iterable, NewType, Optional -from outlines.fsm.guide import RegexGuide, StopAtEOSGuide +from outlines_core.fsm.guide import RegexGuide, StopAtEOSGuide if TYPE_CHECKING: - from outlines.models.tokenizer import Tokenizer + from outlines_core.models.tokenizer import Tokenizer FSMState = NewType("FSMState", int) diff --git a/outlines/fsm/guide.py b/src/outlines_core/fsm/guide.py similarity index 98% rename from outlines/fsm/guide.py rename to src/outlines_core/fsm/guide.py index c846c441..3773505d 100644 --- a/outlines/fsm/guide.py +++ b/src/outlines_core/fsm/guide.py @@ -14,15 +14,15 @@ import interegular import torch -from outlines.caching import cache -from outlines.fsm.regex import ( +from outlines_core.caching import cache +from outlines_core.fsm.regex import ( create_fsm_index_tokenizer, make_byte_level_fsm, make_deterministic_fsm, ) if TYPE_CHECKING: - from outlines.models.tokenizer import Tokenizer + from outlines_core.models.tokenizer import Tokenizer @dataclass(frozen=True) diff --git a/outlines/fsm/json_schema.py b/src/outlines_core/fsm/json_schema.py similarity index 100% rename from outlines/fsm/json_schema.py rename to src/outlines_core/fsm/json_schema.py diff --git a/outlines/fsm/regex.py b/src/outlines_core/fsm/regex.py similarity index 99% rename from outlines/fsm/regex.py rename to src/outlines_core/fsm/regex.py index 8cfd81ea..3c06790a 100644 --- a/outlines/fsm/regex.py +++ b/src/outlines_core/fsm/regex.py @@ -29,7 +29,7 @@ from tqdm import tqdm if TYPE_CHECKING: - from outlines.models.tokenizer import Tokenizer + from outlines_core.models.tokenizer import Tokenizer class BetterAlphabet(Alphabet): diff --git a/outlines/fsm/types.py b/src/outlines_core/fsm/types.py similarity index 100% rename from outlines/fsm/types.py rename to src/outlines_core/fsm/types.py diff --git a/outlines/models/__init__.py b/src/outlines_core/models/__init__.py similarity index 100% rename from outlines/models/__init__.py rename to src/outlines_core/models/__init__.py diff --git a/outlines/models/tokenizer.py b/src/outlines_core/models/tokenizer.py similarity index 100% rename from outlines/models/tokenizer.py rename to src/outlines_core/models/tokenizer.py diff --git a/outlines/models/transformers.py b/src/outlines_core/models/transformers.py similarity index 99% rename from outlines/models/transformers.py rename to src/outlines_core/models/transformers.py index 10f8f248..e219d8a4 100644 --- a/outlines/models/transformers.py +++ b/src/outlines_core/models/transformers.py @@ -4,7 +4,7 @@ from datasets.fingerprint import Hasher -from outlines.models.tokenizer import Tokenizer +from outlines_core.models.tokenizer import Tokenizer if TYPE_CHECKING: import torch diff --git a/outlines/py.typed b/src/outlines_core/py.typed similarity index 100% rename from outlines/py.typed rename to src/outlines_core/py.typed diff --git a/tests/fsm/test_fsm.py b/tests/fsm/test_fsm.py index 94166fd9..bb074b0b 100644 --- a/tests/fsm/test_fsm.py +++ b/tests/fsm/test_fsm.py @@ -1,6 +1,6 @@ import pytest -from outlines.fsm.fsm import RegexFSM, StopAtEosFSM +from outlines_core.fsm.fsm import RegexFSM, StopAtEosFSM def assert_expected_tensor_ids(tensor, ids): diff --git a/tests/fsm/test_guide.py b/tests/fsm/test_guide.py index 9a66bc04..c48b1ad9 100644 --- a/tests/fsm/test_guide.py +++ b/tests/fsm/test_guide.py @@ -1,6 +1,6 @@ import pytest -from outlines.fsm.guide import Generate, RegexGuide, StopAtEOSGuide, Write +from outlines_core.fsm.guide import Generate, RegexGuide, StopAtEOSGuide, Write def assert_expected_tensor_ids(tensor, ids): diff --git a/tests/fsm/test_json_schema.py b/tests/fsm/test_json_schema.py index 21571da8..12b26912 100644 --- a/tests/fsm/test_json_schema.py +++ b/tests/fsm/test_json_schema.py @@ -6,7 +6,7 @@ import pytest from pydantic import BaseModel, Field, constr -from outlines.fsm.json_schema import ( +from outlines_core.fsm.json_schema import ( BOOLEAN, DATE, DATE_TIME, diff --git a/tests/fsm/test_regex.py b/tests/fsm/test_regex.py index f7aa4ae6..bf535eb4 100644 --- a/tests/fsm/test_regex.py +++ b/tests/fsm/test_regex.py @@ -4,7 +4,7 @@ import pytest from transformers import AutoTokenizer -from outlines.fsm.regex import ( +from outlines_core.fsm.regex import ( _walk_fsm, create_fsm_index_end_to_end, create_fsm_index_tokenizer, @@ -18,7 +18,7 @@ reduced_vocabulary, walk_fsm, ) -from outlines.models.transformers import TransformerTokenizer +from outlines_core.models.transformers import TransformerTokenizer def identity(s): @@ -533,7 +533,7 @@ def test_json_index_performance(): from line_profiler import LineProfiler # type: ignore [import] from pydantic import BaseModel, constr - import outlines + import outlines_core class Weapon(str, Enum): sword = "sword" @@ -557,16 +557,16 @@ class Character(BaseModel): # TODO: Add support for conint strength: int # conint(int, ge=0, le=100) - model = outlines.models.transformers("gpt2", device="cuda") + model = outlines_core.models.transformers("gpt2", device="cuda") json_schema = json.dumps(Character.model_json_schema()) def build_regex(): - regex_str = outlines.index.json_schema.build_regex_from_object(json_schema) - outlines.generate.regex(model, regex_str) + regex_str = outlines_core.index.json_schema.build_regex_from_object(json_schema) + outlines_core.generate.regex(model, regex_str) profiler = LineProfiler(create_fsm_index_end_to_end) profiler.add_function(create_fsm_index_tokenizer) - profiler.add_function(outlines.index.index.RegexFSM.__init__) + profiler.add_function(outlines_core.index.index.RegexFSM.__init__) profiler.runctx( "build_regex()", @@ -664,7 +664,7 @@ def convert_token_to_string(self, token): def test_numba_leading_null_byte_UnicodeCharSeq_remains_broken(): """Assert numba UnicodeCharSeq w/ leading \x00 is still broken""" # EXPLANATION: - # https://github.com/outlines-dev/outlines/pull/930#issuecomment-2143535968 + # https://github.com/outlines_core-dev/outlines/pull/930#issuecomment-2143535968 # from https://github.com/numba/numba/issues/9542 d = numba.typed.typeddict.Dict.empty(numba.types.UnicodeCharSeq(1), numba.int64) @@ -682,7 +682,7 @@ def test_numba_leading_null_byte_UnicodeCharSeq_remains_broken(): def test_numba_leading_null_byte_unicode_type_sane(input_key): """Assert numba unicode_type w/ leading \x00 is working""" # EXPLANATION: - # https://github.com/outlines-dev/outlines/pull/930#issuecomment-2143535968 + # https://github.com/outlines_core-dev/outlines/pull/930#issuecomment-2143535968 # from https://github.com/numba/numba/issues/9542 d = numba.typed.typeddict.Dict.empty(numba.types.unicode_type, numba.int64) diff --git a/tests/fsm/test_types.py b/tests/fsm/test_types.py index d5450434..2102db92 100644 --- a/tests/fsm/test_types.py +++ b/tests/fsm/test_types.py @@ -2,7 +2,7 @@ import pytest -from outlines.fsm.types import ( +from outlines_core.fsm.types import ( BOOLEAN, DATE, DATETIME, diff --git a/tests/models/test_tokenizer.py b/tests/models/test_tokenizer.py index 831f7fe3..95e9cc8f 100644 --- a/tests/models/test_tokenizer.py +++ b/tests/models/test_tokenizer.py @@ -1,6 +1,6 @@ import pytest -from outlines.models.tokenizer import Tokenizer +from outlines_core.models.tokenizer import Tokenizer def test_tokenizer(): diff --git a/tests/models/test_transformers.py b/tests/models/test_transformers.py index 1404d287..8ac8d466 100644 --- a/tests/models/test_transformers.py +++ b/tests/models/test_transformers.py @@ -3,7 +3,7 @@ from transformers import AutoTokenizer from transformers.models.gpt2 import GPT2TokenizerFast -from outlines.models.transformers import TransformerTokenizer, transformers +from outlines_core.models.transformers import TransformerTokenizer, transformers TEST_MODEL = "hf-internal-testing/tiny-random-GPTJForCausalLM" diff --git a/tests/test_cache.py b/tests/test_cache.py index eb4ec406..766d97ad 100644 --- a/tests/test_cache.py +++ b/tests/test_cache.py @@ -32,20 +32,20 @@ def test_cache(refresh_environment): """Initialize a temporary cache and delete it after the test has run.""" with tempfile.TemporaryDirectory() as tempdir: os.environ["OUTLINES_CACHE_DIR"] = tempdir - import outlines + import outlines_core - memory = outlines.get_cache() + memory = outlines_core.get_cache() assert memory.directory == tempdir - yield outlines.caching.cache() + yield outlines_core.caching.cache() memory.clear() def test_get_cache(test_cache): - import outlines + import outlines_core - memory = outlines.get_cache() + memory = outlines_core.get_cache() assert isinstance(memory, diskcache.Cache) # If the cache is enabled then the size @@ -70,9 +70,9 @@ def f(x): def test_disable_cache(test_cache): """Make sure that we can disable the cache.""" - import outlines + import outlines_core - outlines.disable_cache() + outlines_core.disable_cache() # If the cache is disabled then the size # of `store` should increase every time @@ -92,7 +92,7 @@ def f(x): def test_clear_cache(test_cache): """Make sure that we can clear the cache.""" - import outlines + import outlines_core store = list() @@ -110,7 +110,7 @@ def f(x): # The size of `store` should increase if we call `f` # after clearing the cache. - outlines.clear_cache() + outlines_core.clear_cache() f(1) assert len(store) == store_size + 1 @@ -118,14 +118,14 @@ def f(x): def test_version_upgrade_cache_invalidate(test_cache, mocker): """Ensure we can change the signature of a cached function if we upgrade the version""" - import outlines.caching + import outlines_core.caching def simulate_restart_outlines(): # clearing in-memory lru_cache which returns the diskcache in # order to simulate a reload, we're not clearing the diskcache itself - outlines.caching.get_cache.cache_clear() + outlines_core.caching.get_cache.cache_clear() - mocker.patch("outlines._version.__version__", new="0.0.0") + mocker.patch("outlines_core._version.__version__", new="0.0.0") simulate_restart_outlines() # initialize cache with signature of Tuple-of-3 @@ -148,7 +148,7 @@ def foo(): a, b = foo() # "restart" outlines WITH version upgrade - mocker.patch("outlines._version.__version__", new="0.0.1") + mocker.patch("outlines_core._version.__version__", new="0.0.1") simulate_restart_outlines() # change signature to Tuple-of-2 @@ -163,7 +163,7 @@ def foo(): def test_cache_disabled_decorator(test_cache): """Ensure cache can be disabled in a local scope""" - from outlines.caching import cache_disabled + from outlines_core.caching import cache_disabled mock = unittest.mock.MagicMock() From 5675353cf037da88e1032092c37c7480a4b147b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Thu, 15 Aug 2024 15:05:37 +0200 Subject: [PATCH 12/20] Remove the documentation --- .github/workflows/build_documentation.yml | 22 - .github/workflows/publish_documentation.yml | 30 -- docs/api/guide.md | 1 - docs/api/index.md | 1 - docs/api/json_schema.md | 1 - docs/api/models.md | 3 - docs/api/parsing.md | 1 - docs/api/prompts.md | 1 - docs/api/regex.md | 1 - docs/api/samplers.md | 1 - docs/assets/images/dottxt.png | Bin 42419 -> 0 bytes .../images/logits_processing_diagram.svg | 157 ------- docs/assets/images/logo.png | Bin 372647 -> 0 bytes docs/assets/images/normal_computing.jpg | Bin 64918 -> 0 bytes docs/blog/.authors.yml | 5 - docs/blog/assets/4000_stars.png | Bin 57307 -> 0 bytes docs/blog/index.md | 1 - docs/blog/posts/roadmap-2024.md | 91 ---- docs/community/belonging.png | Bin 142520 -> 0 bytes docs/community/contribute.md | 132 ------ docs/community/examples.md | 19 - docs/community/feedback.md | 81 ---- docs/community/index.md | 12 - docs/community/versioning.md | 26 -- 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--git a/.github/workflows/publish_documentation.yml b/.github/workflows/publish_documentation.yml deleted file mode 100644 index 4679121b..00000000 --- a/.github/workflows/publish_documentation.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: Publish the documentation - -on: - push: - branches: - - main - -permissions: - contents: write - -jobs: - deploy: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 - with: - python-version: 3.x - - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - - uses: actions/cache@v3 - with: - key: mkdocs-material-${{ env.cache_id }} - path: .cache - restore-keys: | - mkdocs-material- - - run: pip install -r requirements-doc.txt - - name: Build documentation - env: - GOOGLE_ANALYTICS_KEY: ${{ secrets.GOOGLE_ANALYTICS_KEY }} - run: mkdocs gh-deploy --force diff --git a/docs/api/guide.md b/docs/api/guide.md deleted file mode 100644 index 1c3160c8..00000000 --- a/docs/api/guide.md +++ /dev/null @@ -1 +0,0 @@ -::: outlines.fsm.guide diff --git a/docs/api/index.md b/docs/api/index.md deleted file mode 100644 index b0d5c88f..00000000 --- a/docs/api/index.md +++ /dev/null @@ -1 +0,0 @@ -# API Reference diff --git a/docs/api/json_schema.md b/docs/api/json_schema.md deleted file mode 100644 index 471cb3a8..00000000 --- a/docs/api/json_schema.md +++ /dev/null @@ -1 +0,0 @@ -::: outlines.fsm.json_schema diff --git a/docs/api/models.md b/docs/api/models.md deleted file mode 100644 index 27ad297f..00000000 --- a/docs/api/models.md +++ /dev/null @@ -1,3 +0,0 @@ -::: outlines.models.transformers - -::: outlines.models.openai diff --git a/docs/api/parsing.md b/docs/api/parsing.md deleted file mode 100644 index e9662999..00000000 --- a/docs/api/parsing.md +++ /dev/null @@ -1 +0,0 @@ -::: outlines.fsm.parsing diff --git a/docs/api/prompts.md b/docs/api/prompts.md deleted file mode 100644 index 9d28f838..00000000 --- a/docs/api/prompts.md +++ /dev/null @@ -1 +0,0 @@ -::: outlines.prompts diff --git 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- - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -# Let us know! - -We highly value the insights of our users, and we would love to hear from you. If you are using Outlines for your projects and would like to share your experience with us, let's connect: - -- What are you building with it? -- What do you like about it? -- What challenges are you facing? -- What do you think could be improved? - -To schedule an appointment follow [this link](https://cal.com/dottxt/outlines). This is exclusively intended to share your experience, please go on [Discord][discord] or [GitHub](https://github.com/outlines-dev/outlines/discussions) for support. - -[discord]: https://discord.gg/UppQmhEpe8 -[twitter]: https://twitter.com/dottxtai diff --git a/docs/community/index.md b/docs/community/index.md deleted file mode 100644 index 9519f5b5..00000000 --- a/docs/community/index.md +++ /dev/null @@ -1,12 +0,0 @@ -# Community - -![Belonging](belonging.png) - -Outlines exists for a community of users who believe software doesn't need to be complicated. Who share the same passion for Large Language Models but don't want to compromise on robustness. Together, we are bringing these powerful models back to the world of software. - -## Connect on Discord - -The Outlines community lives on our Discord server. There you can ask questions, share ideas or just chat with people like you. Don't be a stranger and [join us][discord]. - - -[discord]: https://discord.gg/UppQmhEpe8 diff --git a/docs/community/versioning.md b/docs/community/versioning.md deleted file mode 100644 index d64a56e7..00000000 --- a/docs/community/versioning.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -title: Versioning Guide ---- - -# Versioning Guide - - -The Outlines project follows a structured versioning scheme designed to provide clarity and minimize risk for downstream dependents. - -Each part of the version number (`major.minor.patch`) conveys information about the nature and impact of the changes included in the release. - -- **Major Releases** includes compatibility-breaking changes to core interfaces, such as `LogitsProcessor`s and `Guides`. -- **Minor Releases** introduce changes of substance to internal or unexposed functionality. These changes are well tested and intended to maintain compatability with existing use of core interfaces. -- **Patch Releases** address bug fixes and incorporate low-risk changes to improve stability and performance. - -## Releases - -Releases along with release notes can be found on the [Outlines Releases GitHub Page](https://github.com/outlines-dev/outlines/releases). - -## Version Pinning Recommendations - -Here are our recommendations for managing dependencies on the Outlines package: - -**Small, Risk-Tolerant Projects:** Pin to a specific major version. - -**Large, Conservative Projects:** Pin to a specific minor version. diff --git a/docs/cookbook/chain_of_density.md b/docs/cookbook/chain_of_density.md deleted file mode 100644 index 16c2838f..00000000 --- a/docs/cookbook/chain_of_density.md +++ /dev/null @@ -1,125 +0,0 @@ -# Summarize documents using Chain of Density prompting - -A good summary should be informative, concise and clear. While large language models are generally good at summarizing documents, their summaries tend to be long and contain redundant information; their information density tends to be on the lower end. This is where [chain of Density](https://arxiv.org/abs/2309.04269), a new prompting technique, comes in. In this example we will show how one can implement chain of density with a few lines of code using Outlines, leveraging both Outline's prompt templating and its structured generation capabilities. - -The article we will try to summarize is the first three paragraphs of the [Alan Turing page on Wikipedia](https://en.wikipedia.org/wiki/Alan_Turing): - -```python -article = """ -Alan Mathison Turing OBE FRS (/ˈtjΚŠΙ™rΙͺΕ‹/; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher and theoretical biologist.[5] Turing was highly influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general-purpose computer.[6][7][8] He is widely considered to be the father of theoretical computer science and artificial intelligence.[9] - -Born in Maida Vale, London, Turing was raised in southern England. He graduated at King's College, Cambridge, with a degree in mathematics. Whilst he was a fellow at Cambridge, he published a proof demonstrating that some purely mathematical yes–no questions can never be answered by computation. He defined a Turing machine and proved that the halting problem for Turing machines is undecidable. In 1938, he obtained his PhD from the Department of Mathematics at Princeton University. During the Second World War, Turing worked for the Government Code and Cypher School at Bletchley Park, Britain's codebreaking centre that produced Ultra intelligence. For a time he led Hut 8, the section that was responsible for German naval cryptanalysis. Here, he devised a number of techniques for speeding the breaking of German ciphers, including improvements to the pre-war Polish bomba method, an electromechanical machine that could find settings for the Enigma machine. Turing played a crucial role in cracking intercepted coded messages that enabled the Allies to defeat the Axis powers in many crucial engagements, including the Battle of the Atlantic.[10][11] - -After the war, Turing worked at the National Physical Laboratory, where he designed the Automatic Computing Engine, one of the first designs for a stored-program computer. In 1948, Turing joined Max Newman's Computing Machine Laboratory at the Victoria University of Manchester, where he helped develop the Manchester computers[12] and became interested in mathematical biology. He wrote a paper on the chemical basis of morphogenesis[1] and predicted oscillating chemical reactions such as the Belousov–Zhabotinsky reaction, first observed in the 1960s. Despite these accomplishments, Turing was never fully recognised in Britain during his lifetime because much of his work was covered by the Official Secrets Act.[13] -""" -``` - -## How Chain Of Density works - -Chain Of Density starts with asking the model to generate a first long and non-specific summary. Then it asks the model to generate 4 extra summaries by proceeding in the following way: - -1. Identify 1-3 entities missing in the previous summary; -2. Add all entities marked as missing in the previous step, while not dropping entities; -3. Make the summary more concise; - -The prompt also asks the model to return a list of JSON objects that contain the missing entities and the new summary. This is where structured generation will come in handy :) The paper provides the prompt and an example: - -![Figure 2 in the paper](./images/chain_of_density.png) - -We can now implement the prompt provided in the paper: - -```python -import outlines - -@outlines.prompt -def chain_of_density(article): - """Article: {{ article }} - - You will generate increasingly concise, entity-dense summaries of the above Article. - - Repeat the following 2 steps 5 times. - - Step 1. Identify 1-3 informative Entities ("; " delimited) from the Article which are missing from the previously generated summary. - Step 2. Write a new, denser summary of identical length which covers every entity and detail from the previous summary plus the Missing Entities. - - A Missing Entity is: - - Relevant: to the main story. - - Specific: descriptive yet concise (5 words or fewer). - - Novel: not in the previous summary. - - Faithful: present in the Article. - - Anywhere: located anywhere in the Article. - - Guidelines: - - The first summary should be long (4-5 sentences, ~80 words) yet highly non-specific, containing little information beyond the entities marked as missing. Use overly verbose language and fillers (e.g., "this article discusses") to reach ~80 words. - - Make every word count: rewrite the previous summary to improve flow and make space for additional entities. - - Make space with fusion, compression, and removal of uninformative phrases like "the article discusses". - - The summaries should become highly dense and concise yet self-contained, e.g., easily understood without the Article. - - Missing entities can appear anywhere in the new summary. - - Never drop entities from the previous summary. If space cannot be made, add fewer new entities. - - Remember, use the exact same number of words for each summary. - - Answer in JSON. The JSON should be a a dictionary with key "summaries" that contains a list (length 5) of dictionaries whose keys are "Missing_Entities" and "Denser_Summary". - """ -``` - -??? Note - - Note that we modified the prompt slightly so it returns a JSON object that contains the summaries, instead of a list of summaries. - - -## Outlines implementation - -We will use Outline's JSON-structured generation to ensure that the model's output is consistent with the format specified in the prompt. We start with defining the JSON objects that the model is asked to return using Pydantic. One JSON object that contains a list of `Summary` objects that contain the missing entities and new summary: - -```python -from pydantic import BaseModel, conlist - -class Summary(BaseModel): - missing_entities: str - denser_summary: str - -class Summaries(BaseModel): - summaries: conlist(Summary, max_length=5, min_length=5) -``` - -We now generate the prompt by passing the article we want to summarize to the template. We load a quantized version of Mistral-7B using the AutoAWQ library, and then use JSON-structured generation to generate the summaries: - -```python -model = outlines.models.transformers("TheBloke/Mistral-7B-OpenOrca-AWQ") - -prompt = chain_of_density(article) -result = outlines.generate.json(model, Summaries)(prompt) -``` - -We can now check the results: - -```python -print(result.model_dump()) -# {'summaries': [ -# { -# 'missing_entities': 'English mathematician, cryptanalyst, philosopher', -# 'denser_summary': 'Alan Mathison Turing was an English mathematician, cryptanalyst, philosopher.' -# }, -# { -# 'missing_entities': '', -# 'denser_summary': "Alan Mathison Turing was an English mathematician who was a crucial figure in WW2's Bletchley Park codebreaking centre and designed one of the first computers." -# }, -# { -# 'missing_entities': 'cryptanalyst, studied, biology, father', -# 'denser_summary': 'Alan Mathison Turing was an English cryptanalyst, studied theoretical computer science, and contributed to mathematical biology.' -# }, -# { -# 'missing_entities': 'biology, morphogenesis, chemical', -# 'denser_summary': 'Alan Mathison Turing was an English cryptanalyst, studied theoretical computer science, and predicted chemical reactions in morphogenesis. -# '}, -# { -# 'missing_entities': '', -# 'denser_summary': 'Alan Mathison Turing was an English cryptanalyst, developed computer science, and made strides in mathematical biology research.' -# } -# ]} -``` - -Not bad, considering we used a smallish model to generate the summary! Chain of Density seems to be a very effective prompting technique to generate dense summaries, even with small quantized models. Its implementation in Outlines is also very short. - -Note that this is the first article I tried and it worked out of the box. Try it out on other articles, and please share the results on Twitter, or by opening [a new discussion](https://github.com/outlines-dev/outlines/discussions/categories/show-and-tell) on the Outlines repository! diff --git a/docs/cookbook/chain_of_thought.md b/docs/cookbook/chain_of_thought.md deleted file mode 100644 index bd76f40b..00000000 --- a/docs/cookbook/chain_of_thought.md +++ /dev/null @@ -1,138 +0,0 @@ -# Chain of thought - - -Chain of thought is a prompting technique introduced in the paper ["Chain-of-Thought Prompting Elicits Reasoning in Large Language Models"](https://arxiv.org/abs/2201.11903) where throught prompting the authors generate a series of intermediate reasoning steps which improves the ability of LLMs to perform complex reasoning. - -In this guide, we use [outlines](https://outlines-dev.github.io/outlines/) to apply chain of thought through structured output. - -We use [llama.cpp](https://github.com/ggerganov/llama.cpp) using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) library. Outlines supports llama-cpp-python, but we need to install it ourselves: - -```bash -pip install llama-cpp-python -``` - -We download the model weights by passing the name of the repository on the HuggingFace Hub, and the filenames (or glob pattern): -```python -import llama_cpp -from outlines import generate, models - -model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", - "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False) -``` - -??? note "(Optional) Store the model weights in a custom folder" - - By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/): - - ```bash - wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf - ``` - - We initialize the model: - - ```python - import llama_cpp - from llama_cpp import Llama - from outlines import generate, models - - llm = Llama( - "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False - ) - ``` - -## Chain of thought - -We first define our Pydantic class for a reasoning step: - -```python -from pydantic import BaseModel, Field - -class Reasoning_Step(BaseModel): - reasoning_step: str = Field(..., description="Reasoning step") -``` - -We then define the Pydantic class for reasoning which will consist on a list of reasoning steps and a conclusion, and we get its JSON schema: - -```python -from typing import List - -class Reasoning(BaseModel): - reasoning: List[Reasoning_Step] = Field(..., description="List of reasoning steps") - conclusion: str = Field(..., description="Conclusion") - -json_schema = Reasoning.model_json_schema() -``` - -We could generate a response using the json schema but for a change we will use the regex: - -```python -from outlines.integrations.utils import convert_json_schema_to_str -from outlines.fsm.json_schema import build_regex_from_schema - -schema_str = convert_json_schema_to_str(json_schema=json_schema) -regex_str = build_regex_from_schema(schema_str) -``` - -We then need to adapt our prompt to the [Hermes prompt format for JSON schema](https://github.com/NousResearch/Hermes-Function-Calling?tab=readme-ov-file#prompt-format-for-json-mode--structured-outputs): - -```python -def generate_hermes_prompt(user_prompt): - return ( - "<|im_start|>system\n" - "You are a world class AI model who answers questions in JSON " - f"Here's the json schema you must adhere to:\n\n{json_schema}\n<|im_end|>\n" - "<|im_start|>user\n" - + user_prompt - + "<|im_end|>" - + "\n<|im_start|>assistant\n" - "" - ) -``` - -For a given user prompt: - -```python -user_prompt = "9.11 and 9.9 -- which is bigger?" -``` - -we can use `generate.regex` by passing the Pydantic class we previously defined, and call the generator with the Hermes prompt: - -```python -generator = generate.regex(model, regex_str) -prompt = generate_hermes_prompt(user_prompt) -response = generator(prompt, max_tokens=1024, temperature=0, seed=42) -``` - -We obtain a series of intermediate reasoning steps as well as the conclusion: - -```python -import json - -json_response = json.loads(response) - -print(json_response["reasoning"]) -print(json_response["conclusion"]) -# [{'reasoning_step': 'Both 9.11 and 9.9 are decimal numbers.'}, -# {'reasoning_step': 'When comparing decimal numbers, we look at the numbers after the decimal point.'}, -# {'reasoning_step': 'In this case, 9.11 has the number 1 after the decimal point, while 9.9 has the number 9.'}, -# {'reasoning_step': 'Since 1 is greater than 9, 9.11 is greater than 9.9.'}] -# '9.11 is bigger.' -``` - -We notice that the 4th reasoning step is wrong ``Since 1 is greater than 9, 9.11 is greater than 9.9.'', so we should probably give the model some examples for this particular task. - -This example was originally contributed by [Alonso Silva](https://github.com/alonsosilvaallende). diff --git a/docs/cookbook/classification.md b/docs/cookbook/classification.md deleted file mode 100644 index c5609631..00000000 --- a/docs/cookbook/classification.md +++ /dev/null @@ -1,103 +0,0 @@ -# Classification - -Classification is a classic problem in NLP and finds many applications: spam detection, sentiment analysis, triaging of incoming requests, etc. We will use the example of a company that wants to sort support requests between those that require immediate attention (`URGENT`), those that can wait a little (`STANDARD`). You could easily extend the example by adding new labels. - - -This tutorial shows how one can implement multi-label classification using Outlines. We will use two functionalities of the library: `generate.choice` and `generate.json`. - -As always, we start with initializing the model. Since we are GPU poor we will be using a quantized version of Mistal-7B-v0.1: - -```python -import outlines - -model = outlines.models.transformers("TheBloke/Mistral-7B-OpenOrca-AWQ", device="cuda") -``` - -We will use the following prompt template: - -```python -@outlines.prompt -def customer_support(request): - """You are an experienced customer success manager. - - Given a request from a client, you need to determine when the - request is urgent using the label "URGENT" or when it can wait - a little with the label "STANDARD". - - # Examples - - Request: "How are you?" - Label: STANDARD - - Request: "I need this fixed immediately!" - Label: URGENT - - # TASK - - Request: {{ request }} - Label: """ -``` - -## Choosing between multiple choices - -Outlines provides a shortcut to do multi-label classification, using the `outlines.generate.choice` function to initialize a generator. Outlines uses multinomial sampling by default, here we will use the greedy sampler to get the label with the highest probability: - -```python -from outlines.samplers import greedy - -generator = outlines.generate.choice(model, ["URGENT", "STANDARD"], sampler=greedy()) -``` -Outlines supports batched requests, so we will pass two requests to the model: - -```python -requests = [ - "My hair is one fire! Please help me!!!", - "Just wanted to say hi" -] - -prompts = [customer_support(request) for request in requests] -``` - -We can now asks the model to classify the requests: - -```python -labels = generator(prompts) -print(labels) -# ['URGENT', 'STANDARD'] -``` - -Now, you might be in a hurry and don't want to wait until the model finishes completion. After all, you only need to see the first letter of the response to know whether the request is urgent or standard. You can instead stream the response: - -```python -tokens = generator.stream(prompts) -labels = ["URGENT" if "U" in token else "STANDARD" for token in next(tokens)] -print(labels) -# ['URGENT', 'STANDARD'] -``` - -## Using JSON-structured generation - -Another (convoluted) way to do multi-label classification is to JSON-structured generation in Outlines. We first need to define our Pydantic schema that contains the labels: - -```python -from enum import Enum -from pydantic import BaseModel - - -class Label(str, Enum): - urgent = "URGENT" - standard = "STANDARD" - - -class Classification(BaseModel): - label: Label -``` - -and we can use `generate.json` by passing this Pydantic model we just defined, and call the generator: - -```python -generator = outlines.generate.json(model, Classification, sampler=greedy()) -labels = generator(prompts) -print(labels) -# [Classification(label=), Classification(label=)] -``` diff --git a/docs/cookbook/dating_profiles.md b/docs/cookbook/dating_profiles.md deleted file mode 100644 index d0fb9b57..00000000 --- a/docs/cookbook/dating_profiles.md +++ /dev/null @@ -1,229 +0,0 @@ -# Generate a synthetic dating profile from a description - -In this example we will see how we can use Outlines to generate synthetic data for a dating application. This example was originally contributed by [Vibhor Kumar](https://github.com/veezbo). - -```python -from dataclasses import dataclass -from enum import Enum - -import torch -import transformers -from pydantic import BaseModel, conlist, constr - -import outlines -``` - -## Defining the profile with Pydantic - -Here a dating profile will consist in a biography, a job, a list of interests and two question-answer pairs. The questions are written in advance by the team, and the users are asked to provide an answer: - -```python -class QuestionChoice(str, Enum): - A = "The key to my heart is" - B = "The first item on my bucket list is" - C = "Perks of dating me" - D = "Message me if you also love" - E = "People would describe me as" - F = "I can beat you in a game of" - -@dataclass -class QuestionAnswer: - question: QuestionChoice - answer: str -``` - -Users need to provide a short biography, with a minimum of 10 and a maximum of 300 characters. The application also limits job descriptions to 50 characters. In addition to the question-answer pairs, the user is required to provide a list of between 1 and 5 interests: - -```python -class DatingProfile(BaseModel): - bio: constr(str, min_length=10, max_length=300) - job: constr(str, max_lengt=50) - interests: conlist(str, min_length=1, max_length=5) # type: ignore - qna1: QuestionAnswer - qna2: QuestionAnswer -``` - -## Prompt template and examples - -We will ask the model to generate profiles from a high-level description: - -```python -@dataclass -class Example: - description: str - profile: DatingProfile -``` - -We will use Outlines' prompt templating abilities to generate the prompt for us. This help clearly separate the general prompting logic from what is specific to an example. - -```python - -@outlines.prompt -def dating_profile_prompt(description: str, examples: list[Example]): - """ - You are a world-renowned matchmaker who understands the modern dating - market. Your job is to generate dating app profiles for male clients - interested in women based on a provided description. The profiles should be - authentic, show off their strengths, and maximize their likelihood of - getting matches on dating apps. Here are some examples of past clients that - you have successfully created profiles for: - - {% for example in examples %} - Description: - {{ example.description }} - Profile: - {{ example.profile }} - {% endfor %} - - Here is the new client who you need to create a profile for: - Description: {{ description }} - Profile: - """ -``` - -We will provide the model with several few-shot examples: - -```python -samples: list[Example] = [ - Example( - description="I'm an author and former professional soccer player living in Seattle who publishes popular fiction books. A typical day for me starts by hanging out with my cat, drinking a coffee, and reading as much as I can in a few hours. Then, I'll prepare a quick smoothie before starting to write for a few hours, take a break with soccer or running a few miles, and finally meet friends for dinner at a new, hip restaurant in the evening. Sometimes we go axe-throwing afterwards, or play poker, or watch a comedy show, or visit a dive bar. On my vacations, I travel extensively to countries South America, Europe, and Asia, with the goal of visiting them all!", - profile=DatingProfile( - bio="Adventurer, dreamer, author, and soccer enthusiast. Life’s too short to waste time so I make the most of each day by exploring new places and playing with my friends on the pitch. What’s your favorite way to get out and have fun?", - job="Famous Soccer Player -> Famous Author", - interests=["Soccer", "Travel", "Friends", "Books", "Fluffy Animals"], - qna1=QuestionAnswer( - question=QuestionChoice.B, answer="swim in all seven oceans!" - ), - qna2=QuestionAnswer( - question=QuestionChoice.E, - answer="fun-loving, adventurous, and a little bit crazy", - ), - ), - ), - Example( - description="I run my company and build houses for a living. I'm a big fan of the outdoors and love to go hiking, camping, and fishing. I don't like video games, but do like to watch movies. My love language is home-cooked food, and I'm looking for someone who isn't afraid to get their hands dirty.", - profile=DatingProfile( - bio="If you're looking for a Montana man who loves to get outdoors and hunt, and who's in-tune with his masculinity then I'm your guy!", - job="House Construction Manager / Entrepreneur", - interests=["Hunting", "Hiking", "The outdoors", "Home-cooked food"], - qna1=QuestionAnswer(question=QuestionChoice.A, answer="food made at home"), - qna2=QuestionAnswer( - question=QuestionChoice.C, - answer="having a man in your life who can fix anything", - ), - ), - ), - Example( - description="I run my own Youtube channel with 10M subscribers. I love working with kids, and my audience skews pretty young too. In my free time, I play Fortnite and Roblox. I'm looking for someone who is also a gamer and likes to have fun. I'm learning Japanese in my free time as well as how to cook.", - profile=DatingProfile( - bio="Easy on the eyes (find me on Youtube!) and great with kids. What more do you need?", - job="Youtuber 10M+ subscribers", - interests=["Kids", "Gaming", "Japanese"], - qna1=QuestionAnswer(question=QuestionChoice.D, answer="anime and gaming!"), - qna2=QuestionAnswer(question=QuestionChoice.F, answer="Fortnite, gg ez"), - ), - ), -] -``` - -## Load the model - -We will use Mosaic's MPT-7B model (requires 13GB of GPU memory) which can fit on a single GPU with a reasonable context window. We initialize it with Outlines: - -```python -config = transformers.AutoConfig.from_pretrained( - "mosaicml/mpt-7b-8k-instruct", trust_remote_code=True -) -config.init_device = "meta" -model = outlines.models.transformers( - model_name="mosaicml/mpt-7b-8k-instruct", - device="cuda", - model_kwargs={ - "config": config, - "trust_remote_code": True, - "torch_dtype": torch.bfloat16, - "device_map": {"": 0}, - }, -) -``` - -## JSON-structured generation of profiles - -We will now generate a dating profile from a textual description of oneself: - -``` python -new_description = """I'm a laid-back lawyer who spends a lot of his free-time -gaming. I work in a corporate office, but ended up here after the start-up I -cofounded got acquired, so still play ping pong with my cool coworkers every -day. I have a bar at home where I make cocktails, which is great for -entertaining friends. I secretly like to wear suits and get a new one tailored -every few months. I also like weddings because I get to wear those suits, and -it's a good excuse for a date. I watch the latest series because I'm paying, -with my hard-earned money, for every streaming service.""" - -prompt = dating_profile_prompt(new_description, samples) -profile = outlines.generate.json(model, DatingProfile)(prompt) -parsed_profile = DatingProfile.model_validate_json(profile) -``` - -## Results - -Here are a couple of results: - -```json -{ - "bio": """I'm an ambitious lawyer with a casual and fashionable style. I love - games and sports, but my true passion is preparing refreshing cocktails at - home and dressing to the nines at weddings. I'm currently looking for a woman - to show a good time to and get a kiss on the opulent suit I just had made. - Send resume to this inbox.""", - "job": "Lawyer", - "interests": - [ - "Stylish guys", - "Gaming", - "Ping pong", - "Cocktails", - "Weddings" - ], - "qna1": - { - "question": "The first item on my bucket list is", - "answer": "be married and have a family." - }, - "qna2": - { - "question": "People would describe me as", - "answer": "charming, stylish, and funny." - } -} -``` - -```json -{ - "bio": """I’m a sexy lawyer with time on my hands. I love to game and - play ping pong, but the real reason you should swipe to the right - is because I look great in a suit. Who doesn’t love a man in a - suit? Just saying. Send me a message if you think it’s time to take - your dating life to the next level.""", - "job": "Lawyer", - "interests": - [ - "Gaming", - "Ping Pong", - "Tailored Suits", - "Weddings", - "Streaming Services" - ], - "qna1": - { - "question": "The first item on my bucket list is", - "answer": "simulate space but stay alive for as long as possible" - }, - "qna2": - { - "question": "People would describe me as", - "answer": "easy-going, a little nerdy but with a mature essence" - } -} -``` diff --git a/docs/cookbook/deploy-using-bentoml.md b/docs/cookbook/deploy-using-bentoml.md deleted file mode 100644 index 6bee7744..00000000 --- a/docs/cookbook/deploy-using-bentoml.md +++ /dev/null @@ -1,224 +0,0 @@ -# Run Outlines using BentoML - -[BentoML](https://github.com/bentoml/BentoML) is an open-source model serving library for building performant and scalable AI applications with Python. It comes with tools that you need for serving optimization, model packaging, and production deployment. - -In this guide, we will show you how to use BentoML to run programs written with Outlines on GPU locally and in [BentoCloud](https://www.bentoml.com/), an AI Inference Platform for enterprise AI teams. The example source code in this guide is also available in the [examples/bentoml/](https://github.com/outlines-dev/outlines/blob/main/examples/bentoml/) directory. - -## Import a model - -First we need to download an LLM (Mistral-7B-v0.1 in this example and you can use any other LLM) and import the model into BentoML's [Model Store](https://docs.bentoml.com/en/latest/guides/model-store.html). Let's install BentoML and other dependencies from PyPi (preferably in a virtual environment): - -```bash -pip install -r requirements.txt -``` - -Then save the code snippet below as `import_model.py` and run `python import_model.py`. - -**Note**: You need to accept related conditions on [Hugging Face](https://huggingface.co/mistralai/Mistral-7B-v0.1) first to gain access to Mistral-7B-v0.1. - -```python -import bentoml - -MODEL_ID = "mistralai/Mistral-7B-v0.1" -BENTO_MODEL_TAG = MODEL_ID.lower().replace("/", "--") - -def import_model(model_id, bento_model_tag): - - import torch - from transformers import AutoModelForCausalLM, AutoTokenizer - - tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) - model = AutoModelForCausalLM.from_pretrained( - MODEL_ID, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - ) - - with bentoml.models.create(bento_model_tag) as bento_model_ref: - tokenizer.save_pretrained(bento_model_ref.path) - model.save_pretrained(bento_model_ref.path) - - -if __name__ == "__main__": - import_model(MODEL_ID, BENTO_MODEL_TAG) -``` - -You can verify the download is successful by running: - -```bash -$ bentoml models list - -Tag Module Size Creation Time -mistralai--mistral-7b-v0.1:m7lmf5ac2cmubnnz 13.49 GiB 2024-04-25 06:52:39 -``` - -## Define a BentoML Service - -As the model is ready, we can define a [BentoML Service](https://docs.bentoml.com/en/latest/guides/services.html) to wrap the capabilities of the model. - -We will run the JSON-structured generation example [in the README](https://github.com/outlines-dev/outlines?tab=readme-ov-file#efficient-json-generation-following-a-json-schema), with the following schema: - -```python -DEFAULT_SCHEMA = """{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}""" -``` - -First, we need to define a BentoML service by decorating an ordinary class (`Outlines` here) with `@bentoml.service` decorator. We pass to this decorator some configuration and GPU on which we want this service to run in BentoCloud (here an L4 with 24GB memory): - -```python -import typing as t -import bentoml - -from import_model import BENTO_MODEL_TAG - -@bentoml.service( - traffic={ - "timeout": 300, - }, - resources={ - "gpu": 1, - "gpu_type": "nvidia-l4", - }, -) -class Outlines: - - bento_model_ref = bentoml.models.get(BENTO_MODEL_TAG) - - def __init__(self) -> None: - - import outlines - import torch - self.model = outlines.models.transformers( - self.bento_model_ref.path, - device="cuda", - model_kwargs={"torch_dtype": torch.float16}, - ) - - ... -``` - -We then need to define an HTTP endpoint using `@bentoml.api` to decorate the method `generate` of `Outlines` class: - -```python - ... - - @bentoml.api - async def generate( - self, - prompt: str = "Give me a character description.", - json_schema: t.Optional[str] = DEFAULT_SCHEMA, - ) -> t.Dict[str, t.Any]: - - import outlines - - generator = outlines.generate.json(self.model, json_schema) - character = generator(prompt) - - return character -``` - -Here `@bentoml.api` decorator defines `generate` as an HTTP endpoint that accepts a JSON request body with two fields: `prompt` and `json_schema` (optional, which allows HTTP clients to provide their own JSON schema). The type hints in the function signature will be used to validate incoming JSON requests. You can define as many HTTP endpoints as you want by using `@bentoml.api` to decorate other methods of `Outlines` class. - -Now you can save the above code to `service.py` (or use [this implementation](https://github.com/outlines-dev/outlines/blob/main/examples/bentoml/)), and run the code using the BentoML CLI. - -## Run locally for testing and debugging - -Then you can run a server locally by: - -```bash -bentoml serve . -``` - -The server is now active at . You can interact with it using the Swagger UI or in other different ways: - -
- -CURL - -```bash -curl -X 'POST' \ - 'http://localhost:3000/generate' \ - -H 'accept: application/json' \ - -H 'Content-Type: application/json' \ - -d '{ - "prompt": "Give me a character description." -}' -``` - -
- -
- -Python client - -```python -import bentoml - -with bentoml.SyncHTTPClient("http://localhost:3000") as client: - response = client.generate( - prompt="Give me a character description" - ) - print(response) -``` - -
- -Expected output: - -```bash -{ - "name": "Aura", - "age": 15, - "armor": "plate", - "weapon": "sword", - "strength": 20 -} -``` - -## Deploy to BentoCloud - -After the Service is ready, you can deploy it to [BentoCloud](https://docs.bentoml.com/en/latest/bentocloud/get-started.html) for better management and scalability. [Sign up](https://cloud.bentoml.com/signup) if you haven't got a BentoCloud account. - -Make sure you have [logged in to BentoCloud](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html), then run the following command to deploy it. - -```bash -bentoml deploy . -``` - -Once the application is up and running on BentoCloud, you can access it via the exposed URL. - -**Note**: For custom deployment in your own infrastructure, use [BentoML to generate an OCI-compliant image](https://docs.bentoml.com/en/latest/guides/containerization.html). diff --git a/docs/cookbook/deploy-using-cerebrium.md b/docs/cookbook/deploy-using-cerebrium.md deleted file mode 100644 index 6fbaab39..00000000 --- a/docs/cookbook/deploy-using-cerebrium.md +++ /dev/null @@ -1,121 +0,0 @@ -# Run Outlines using Cerebrium - -[Cerebrium](https://www.cerebrium.ai/) is a serverless AI infrastructure platform that makes it easier for companies to build and deploy AI based applications. They offer Serverless GPU'sΒ with low cold start times with over 12 varieties of GPU chips that auto scale and you only pay for the compute you use. - -In this guide we will show you how you can use Cerebrium to run programs written with Outlines on GPUs in the cloud. - -# Setup Cerebrium - -First, we install Cerebrium and login to get authenticated. - -```bash -pip install cerebrium -cerebrium login -``` - -Then let us create our first project - -```bash -cerebrium init outlines-project -``` - -## Setup Environment and Hardware - -You set up your environment and hardware in the cerebrium.toml file that was created using the init function above. - -```toml -[cerebrium.deployment] -docker_base_image_url = "nvidia/cuda:12.1.1-runtime-ubuntu22.04" - -[cerebrium.hardware] -cpu = 2 -memory = 14.0 -gpu = "AMPERE A10" -gpu_count = 1 -provider = "aws" -region = "us-east-1" - -[cerebrium.dependencies.pip] -outline = "==0.0.37" -transformers = "==4.38.2" -datasets = "==2.18.0" -accelerate = "==0.27.2" -``` - -## Setup inference - -Running code in Cerebrium is like writing normal python with no special syntax. In a `main.py` file specify the following: - -```python -import outlines - - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") - -schema = """{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}""" - -generator = outlines.generate.json(model, schema) -``` - -On first deploy, it will download the model and store it on disk therefore for subsequent calls it will load the model from disk. - -Every function in Cerebrium is callable through an API endpoint. Code at the top most layer (ie: not in a function) is instantiated only when the container is spun up the first time so for subsequent calls, it will simply run the code defined in the function you call. - -To deploy an API that creates a new character when called with a prompt you can add the following code to `main.py`: - -```python -def generate( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - - character = generator( - f"[INST]Give me a character description. Describe {prompt}.[/INST]" - ) - - return character -``` - - -## Run on the cloud - -```bash -cerebrium deploy -``` - -You will see your application deploy, install pip packages and download the model. Once completed it will output a CURL request you can use to call your endpoint. Just remember to end -the url with the function you would like to call - in this case /generate. You should see your response returned! diff --git a/docs/cookbook/deploy-using-modal.md b/docs/cookbook/deploy-using-modal.md deleted file mode 100644 index 835924d2..00000000 --- a/docs/cookbook/deploy-using-modal.md +++ /dev/null @@ -1,128 +0,0 @@ -# Run Outlines using Modal - -[Modal](https://modal.com/) is a serverless platform that allows you to easily run code on the cloud, including GPUs. It can come very handy for those of us who don't have a monster GPU at home and want to be able to quickly and easily provision, configure and orchestrate cloud infrastructure. - -In this guide we will show you how you can use Modal to run programs written with Outlines on GPU in the cloud. - -## Build the image - -First we need to define our container image. We download the Mistral-7B-v0.1 model from HuggingFace as part of the definition of the image so it only needs to be done once (you need to provide an [access token](https://huggingface.co/settings/tokens)) - -```python -from modal import Image, App, gpu - -app = App(name="outlines-app") - -outlines_image = Image.debian_slim(python_version="3.11").pip_install( - "outlines==0.0.37", - "transformers==4.38.2", - "datasets==2.18.0", - "accelerate==0.27.2", -) - -def import_model(): - import os - os.environ["HF_TOKEN"] = "YOUR_HUGGINGFACE_TOKEN" - import outlines - outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.2") - -outlines_image = outlines_image.run_function(import_model) -``` - -We will run the JSON-structured generation example [in the README](https://github.com/outlines-dev/outlines?tab=readme-ov-file#efficient-json-generation-following-a-json-schema), with the following schema: - -## Run inference - -```python -schema = """{ - "title": "Character", - "type": "object", - "properties": { - "name": { - "title": "Name", - "maxLength": 10, - "type": "string" - }, - "age": { - "title": "Age", - "type": "integer" - }, - "armor": {"$ref": "#/definitions/Armor"}, - "weapon": {"$ref": "#/definitions/Weapon"}, - "strength": { - "title": "Strength", - "type": "integer" - } - }, - "required": ["name", "age", "armor", "weapon", "strength"], - "definitions": { - "Armor": { - "title": "Armor", - "description": "An enumeration.", - "enum": ["leather", "chainmail", "plate"], - "type": "string" - }, - "Weapon": { - "title": "Weapon", - "description": "An enumeration.", - "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"], - "type": "string" - } - } -}""" -``` - -To make the inference work on Modal we need to wrap the corresponding function in a `@app.function` decorator. We pass to this decorator the image and GPU on which we want this function to run (here an A100 with 80GB memory): - -```python -@app.function(image=outlines_image, gpu=gpu.A100(size='80GB')) -def generate( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - import outlines - - model = outlines.models.transformers( - "mistralai/Mistral-7B-v0.1", device="cuda" - ) - - generator = outlines.generate.json(model, schema) - character = generator( - f"[INST]Give me a character description. Describe {prompt}.[/INST]" - ) - - print(character) -``` - -We then need to define a `local_entrypoint` to call our function `generate` remotely: - -```python -@app.local_entrypoint() -def main( - prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.", -): - generate.remote(prompt) -``` - -Here `@app.local_entrypoint()` decorator defines `main` as the function to start from locally when running the Modal CLI. You can save above code to `example.py` (or use [this implementation](https://github.com/outlines-dev/outlines/blob/main/examples/modal_example.py)). Let's now see how to run the code on the cloud using the Modal CLI. - -## Run on the cloud - -First install the Modal client from PyPi: - -```bash -pip install modal -``` - -You then need to obtain a token from Modal. To do so easily, run the following command: - -```bash -modal setup -``` - -Once that is set you can run inference on the cloud using: - -```bash -modal run example.py -``` - -You should see the Modal app initialize, and soon after see the result of the `print` function in your terminal. That's it! diff --git a/docs/cookbook/extraction.md b/docs/cookbook/extraction.md deleted file mode 100644 index 28317b6b..00000000 --- a/docs/cookbook/extraction.md +++ /dev/null @@ -1,87 +0,0 @@ -# Named entity extraction - -Named Entity Extraction is a fundamental problem in NLP. It involves identifying and categorizing named entities within a document: people, organization, dates, places, etc. It is usually the first step in a more complex NLP worklow. Here we will use the example of a pizza restaurant that receives orders via their website and need to identify the number and types of pizzas that are being ordered. - -Getting LLMs to output the extracted entities in a structured format can be challenging. In this tutorial we will see how we can use Outlines' JSON-structured generation to extract entities from a document and return them in a valid JSON data structure 100% of the time. - -As always, we start with initializing the model. We will be using a quantized version of Mistal-7B-v0.1 (we're GPU poor): - -```python -import outlines - -model = outlines.models.transformers("TheBloke/Mistral-7B-OpenOrca-AWQ", device="cuda") -``` - -And we will be using the following prompt template: - -```python -@outlines.prompt -def take_order(order): - """You are the owner of a pizza parlor. Customers \ - send you orders from which you need to extract: - - 1. The pizza that is ordered - 2. The number of pizzas - - # EXAMPLE - - ORDER: I would like one Margherita pizza - RESULT: {"pizza": "Margherita", "number": 1} - - # OUTPUT INSTRUCTIONS - - Answer in valid JSON. Here are the different objects relevant for the output: - - Order: - pizza (str): name of the pizza - number (int): number of pizzas - - Return a valid JSON of type "Order" - - # OUTPUT - - ORDER: {{ order }} - RESULT: """ -``` - -We now define our data model using Pydantic: - -```python -from enum import Enum -from pydantic import BaseModel - -class Pizza(str, Enum): - margherita = "Margherita" - pepperonni = "Pepperoni" - calzone = "Calzone" - -class Order(BaseModel): - pizza: Pizza - number: int -``` - -We can now define our generator and call it on several incoming orders: - -```python -orders = [ - "Hi! I would like to order two pepperonni pizzas and would like them in 30mins.", - "Is it possible to get 12 margheritas?" -] -prompts = [take_order(order) for order in orders] - -generator = outlines.generate.json(model, Order) - -results = generator(prompts) -print(results) -# [Order(pizza=, number=2), -# Order(pizza=, number=12)] -``` - -There are several ways you could improve this example: - -- Clients may order several types of pizzas. -- Clients may order drinks as well. -- If the pizza place has a delivery service we need to extract the client's address and phone number -- Clients may specify the time for which they want the pizza. 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zOT|F3nFaiUHNdC#Jaw{+GRBVv%KaE~-GG1UtrYZR$*^%1_6F^gJfCRwuK$yW$<{?` zoIscMbgOhk+HXNbk0&f*YraFe+th;aT*UmMr)SOkjqt(^UF7h!!rf21g0f_T(inC* z4WpBf%#E&~t#AG*B!y{#>6GH$xBuaVKU`|GgQb zCPi-;IRT&Nagfp$PQw*aAtMGaCs1(4GSBET)6unwJ`J~w2v7o>+F$8q4AJa2>Q_sp zfr8g2kby}UU%3*0E>q6=bDiMsOc;5fxuFpZ=Nt?c0&%K_L{@845!cT~nP@Z$rM8ny zkuJmgx3?Z}QWwWJttadf+2+?h-hK&?^x1bMlR0J?G(E}VNC%Y^$;Yy(KeVAV!=-B& zNhzIvLd5#DFB<}5r1s=9Z~^(fI}Qhg-H%GEb-*V(YfudgBVmx-&BfA?{QNkC(A4xD zvm|ziI#fNy-Gn^1rtmU;hcYcI%$rn$bp zE(3x=v`kFwcI1B0G2@>Vq!crqEDbew`-B2PzQ89_nMGQ<_gEtOh<|%9i$bulr~@!| zsx$6%0M=fBys#~wV3coPHN;M#h&X|edp#8nQyrf{JHdp$n7{3EP?M{x5>WLVlvpwx zD#nj^KX$976lO0L8^f~Vk9TaU{&4P{!ardC@6;QrlUZp{i<;lDy+7WZ-#?8}frDVo zChJxb7+w^-nghDVH3z4qrA?okn+vzI7y+5Zk&qkV@K(KuWJ9jr?2pvK43$XG$buo> z^e?2o2lE3tA7`!+jk3BjSH^1#LbSUaO-5K?br3%z!>b79$@AF5wLHgFx@E$C=^Lr4 z`LPSHSqswsystem\n" - "You are a world class AI model who answers questions in JSON " - f"Here's the json schema you must adhere to:\n\n{schema}\n<|im_end|>\n" - "<|im_start|>user\n" - + user_prompt - + "<|im_end|>" - + "\n<|im_start|>assistant\n" - "" - ) -``` - -For a given user prompt, for example: - -```python -user_prompt = "Alice loves Bob and she hates Charlie." -``` - -We can use `generate.json` by passing the Pydantic class we previously defined, and call the generator with the Hermes prompt: - -```python -from outlines import generate, models - -model = models.LlamaCpp(llm) -generator = generate.json(model, KnowledgeGraph) -prompt = generate_hermes_prompt(user_prompt) -response = generator(prompt, max_tokens=1024, temperature=0, seed=42) -``` - -We obtain the nodes and edges of the knowledge graph: - -```python -print(response.nodes) -print(response.edges) -# [Node(id=1, label='Alice', property='Person'), -# Node(id=2, label='Bob', property='Person'), -# Node(id=3, label='Charlie', property='Person')] -# [Edge(source=1, target=2, label='love', property='Relationship'), -# Edge(source=1, target=3, label='hate', property='Relationship')] -``` - -## (Optional) Visualizing the Knowledge Graph - -We can use the [Graphviz library](https://graphviz.readthedocs.io/en/stable/) to visualize the generated knowledge graph. For detailed installation instructions, see [here](https://graphviz.readthedocs.io/en/stable/#installation). - -```python -from graphviz import Digraph - -dot = Digraph() -for node in response.nodes: - dot.node(str(node.id), node.label, shape='circle', width='1', height='1') -for edge in response.edges: - dot.edge(str(edge.source), str(edge.target), label=edge.label) - -dot.render('knowledge-graph.gv', view=True) -``` - -![Image of the Extracted Knowledge Graph](./images/knowledge-graph-extraction.png) - -This example was originally contributed by [Alonso Silva](https://github.com/alonsosilvaallende). diff --git a/docs/cookbook/models_playing_chess.md b/docs/cookbook/models_playing_chess.md deleted file mode 100644 index a0df2eb2..00000000 --- a/docs/cookbook/models_playing_chess.md +++ /dev/null @@ -1,88 +0,0 @@ -# Large language models playing chess - -In this example we will make a Phi-2 model play chess against itself. On its own the model easily generates invalid moves, so we will give it a little help. At each step we will generate a regex that only matches valid move, and use it to help the model only generating valid moves. - -## The chessboard - -The game will be played on a standard checkboard. We will use the `chess` [library](https://github.com/niklasf/python-chess) to track the opponents' moves, and check that the moves are valid. - -```python -%pip install outlines -q -%pip install chess -q -%pip install transformers accelerate einops -q - -import chess - -board = chess.Board("rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1") -``` - -## The opponents - -Phi-2 will be playing against itself: - -```python -from outlines import models - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") - -``` - -## A little help for the language model - -To make sure Phi-2 generates valid chess moves we will use Outline's regex-structured generation. We define a function that takes the current state of the board and returns a regex that matches all possible legal moves: - -```python -import re - -def legal_moves_regex(board): - """Build a regex that only matches valid moves.""" - legal_moves = list(board.legal_moves) - legal_modes_str = [board.san(move) for move in legal_moves] - legal_modes_str = [re.sub(r"[+#]", "", move) for move in legal_modes_str] - regex_pattern = "|".join(re.escape(move) for move in legal_modes_str) - regex_pattern = f"{regex_pattern}" - return regex_pattern -``` - -## Prompting the language model - -The prompt corresponds to the current state of the board, so we start with: - -```python -prompt = "Let's play Chess. Moves: " - -``` - -We update the prompt at each step so it reflects the state of the board after the previous move. - -## Let's play - -```python -from outlines import generate - -board_state = " " -turn_number = 0 -while not board.is_game_over(): - regex_pattern = legal_moves_regex(board) - structured = generate.regex(model, regex_pattern)(prompt + board_state) - move = board.parse_san(structured) - - if turn_number % 2 == 0 : # It's White's turn - board_state += board.san(move) + " " - else: - board_state += board.san(move) + " " + str(turn_number) + "." - - turn_number += 1 - - board.push(move) - - print(board_state) -``` - -Interestingly enough, Phi-2 hates capturing. - -```pgn - e4 e5 1.Nf3 Ne7 3.b4 Nf5 5.Nc3 Ne7 7.Bb5 a6 9.Na4 b6 11.c3 Nec6 13.c4 a5 15.d4 Qg5 17.Nd2 Bb7 19.dxe5 -``` - -*This example was originally authored by [@903124S](https://x.com/903124S) in [this gist](https://gist.github.com/903124/cfbefa24da95e2316e0d5e8ef8ed360d).* diff --git a/docs/cookbook/qa-with-citations.md b/docs/cookbook/qa-with-citations.md deleted file mode 100644 index 79a2214c..00000000 --- a/docs/cookbook/qa-with-citations.md +++ /dev/null @@ -1,255 +0,0 @@ -# Generate Synthetic Data and Q&A with Citations - -This tutorial is adapted from the [instructor-ollama notebook](https://github.com/alonsosilvaallende/Hermes-Function-Calling/blob/main/examples/instructor_ollama.ipynb). We start with a simple example to generate synthetic data and then we approach the problem of question answering by providing citations. - -We will use [llama.cpp](https://github.com/ggerganov/llama.cpp) using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) library. Outlines supports llama-cpp-python, but we need to install it ourselves: - -```bash -pip install llama-cpp-python -``` - -We download the model weights by passing the name of the repository on the HuggingFace Hub, and the filenames (or glob pattern): -```python -import llama_cpp -from outlines import generate, models - -model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", - "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False) -``` - -??? note "(Optional) Store the model weights in a custom folder" - - By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/): - - ```bash - wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf - ``` - - We initialize the model: - - ```python - import llama_cpp - from llama_cpp import Llama - from outlines import generate, models - - llm = Llama( - "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False - ) - ``` - -## Generate Synthetic Data - -We first need to define our Pydantic class for a user: - -```python -from pydantic import BaseModel, Field - -class UserDetail(BaseModel): - id: int = Field(..., description="Unique identifier") # so the model keeps track of the number of users - first_name: str - last_name: str - age: int -``` - -We then define a Pydantic class for a list of users: - -```python -from typing import List - -class Users(BaseModel): - users: List[UserDetail] -``` - -We can use a `generate.json` by passing this Pydantic class we just defined, and call the generator: - -```python -model = models.LlamaCpp(llm) -generator = generate.json(model, Users) -response = generator("Create 5 fake users", max_tokens=1024, temperature=0, seed=42) -print(response.users) -# [UserDetail(id=1, first_name='John', last_name='Doe', age=25), -# UserDetail(id=2, first_name='Jane', last_name='Doe', age=30), -# UserDetail(id=3, first_name='Bob', last_name='Smith', age=40), -# UserDetail(id=4, first_name='Alice', last_name='Smith', age=35), -# UserDetail(id=5, first_name='John', last_name='Smith', age=20)] -``` - -```python -for user in response.users: - print(user.first_name) - print(user.last_name) - print(user.age) - print(#####) -# John -# Doe -# 25 -# ##### -# Jane -# Doe -# 30 -# ##### -# Bob -# Smith -# 40 -# ##### -# Alice -# Smith -# 35 -# ##### -# John -# Smith -# 20 -# ##### -``` - -## QA with Citations - -We first need to define our Pydantic class for QA with citations: - -```python -from typing import List -from pydantic import BaseModel - -class QuestionAnswer(BaseModel): - question: str - answer: str - citations: List[str] - -schema = QuestionAnswer.model_json_schema() -``` - -We then need to adapt our prompt to the [Hermes prompt format for JSON schema](https://github.com/NousResearch/Hermes-Function-Calling?tab=readme-ov-file#prompt-format-for-json-mode--structured-outputs): - -```python -def generate_hermes_prompt(question, context, schema=schema): - return ( - "<|im_start|>system\n" - "You are a world class AI model who answers questions in JSON with correct and exact citations " - "extracted from the `Context`. " - f"Here's the json schema you must adhere to:\n\n{schema}\n<|im_end|>\n" - "<|im_start|>user\n" - + "`Context`: " - + context - + "\n`Question`: " - + question + "<|im_end|>" - + "\n<|im_start|>assistant\n" - "" - ) -``` - -We can use `generate.json` by passing the Pydantic class we previously defined, and call the generator with Hermes prompt: - -```python -question = "What did the author do during college?" -context = """ -My name is Jason Liu, and I grew up in Toronto Canada but I was born in China. -I went to an arts high school but in university I studied Computational Mathematics and physics. -As part of coop I worked at many companies including Stitchfix, Facebook. -I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years. -""" -generator = generate.json(model, QuestionAnswer) -prompt = generate_hermes_prompt(question, context) -response = generator(prompt, max_tokens=1024, temperature=0, seed=42) -print(response) -# QuestionAnswer(question='What did the author do during college?', answer='The author studied Computational Mathematics and physics in university and was also involved in starting the Data Science club, serving as its president for 2 years.', citations=['I went to an arts high school but in university I studied Computational Mathematics and physics.', 'I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years.']) -``` - -We can do the same for a list of question-context pairs: - -```python -question1 = "Where was John born?" -context1 = """ -John Doe is a software engineer who was born in New York, USA. -He studied Computer Science at the Massachusetts Institute of Technology. -During his studies, he interned at Google and Microsoft. -He also founded the Artificial Intelligence club at his university and served as its president for three years. -""" - -question2 = "What did Emily study in university?" -context2 = """ -Emily Smith is a data scientist from London, England. -She attended the University of Cambridge where she studied Statistics and Machine Learning. -She interned at IBM and Amazon during her summer breaks. -Emily was also the head of the Women in Tech society at her university. -""" - -question3 = "Which companies did Robert intern at?" -context3 = """ -Robert Johnson, originally from Sydney, Australia, is a renowned cybersecurity expert. -He studied Information Systems at the University of Melbourne. -Robert interned at several cybersecurity firms including NortonLifeLock and McAfee. -He was also the leader of the Cybersecurity club at his university. -""" - -question4 = "What club did Alice start at her university?" -context4 = """ -Alice Williams, a native of Dublin, Ireland, is a successful web developer. -She studied Software Engineering at Trinity College Dublin. -Alice interned at several tech companies including Shopify and Squarespace. -She started the Web Development club at her university and was its president for two years. -""" - -question5 = "What did Michael study in high school?" -context5 = """ -Michael Brown is a game developer from Tokyo, Japan. -He attended a specialized high school where he studied Game Design. -He later attended the University of Tokyo where he studied Computer Science. -Michael interned at Sony and Nintendo during his university years. -He also started the Game Developers club at his university. -""" - -for question, context in [ - (question1, context1), - (question2, context2), - (question3, context3), - (question4, context4), - (question5, context5), -]: - final_prompt = my_final_prompt(question, context) - generator = generate.json(model, QuestionAnswer) - response = generator(final_prompt, max_tokens=1024, temperature=0, seed=42) - display(question) - display(response.answer) - display(response.citations) - print("\n\n") - -# 'Where was John born?' -# 'John Doe was born in New York, USA.' -# ['John Doe is a software engineer who was born in New York, USA.'] -# -# -# 'What did Emily study in university?' -# 'Emily studied Statistics and Machine Learning in university.' -# ['She attended the University of Cambridge where she studied Statistics and Machine Learning.'] -# -# -# 'Which companies did Robert intern at?' -# 'Robert interned at NortonLifeLock and McAfee.' -# ['Robert Johnson, originally from Sydney, Australia, is a renowned cybersecurity expert. He interned at several cybersecurity firms including NortonLifeLock and McAfee.'] -# -# -# 'What club did Alice start at her university?' -# 'Alice started the Web Development club at her university.' -# ['Alice Williams, a native of Dublin, Ireland, is a successful web developer. She started the Web Development club at her university and was its president for two years.'] -# -# -# 'What did Michael study in high school?' -# 'Michael studied Game Design in high school.' -# ['Michael Brown is a game developer from Tokyo, Japan. He attended a specialized high school where he studied Game Design.'] -``` - -This example was originally contributed by [Alonso Silva](https://github.com/alonsosilvaallende). diff --git a/docs/cookbook/react_agent.md b/docs/cookbook/react_agent.md deleted file mode 100644 index ca4829d5..00000000 --- a/docs/cookbook/react_agent.md +++ /dev/null @@ -1,273 +0,0 @@ -# ReAct Agent - -This example shows how to use [outlines](https://outlines-dev.github.io/outlines/) to build your own agent with open weights local models and structured outputs. It is inspired by the blog post [A simple Python implementation of the ReAct pattern for LLMs](https://til.simonwillison.net/llms/python-react-pattern) by [Simon Willison](https://simonwillison.net/). - -The ReAct pattern (for Reason+Act) is described in the paper [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629). It's a pattern where you implement additional actions that an LLM can take - searching Wikipedia or running calculations for example - and then teach it how to request the execution of those actions, and then feed their results back into the LLM. - -Additionally, we give the LLM the possibility of using a scratchpad described in the paper [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) which improves the ability of LLMs to perform multi-step computations. - -We use [llama.cpp](https://github.com/ggerganov/llama.cpp) using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) library. Outlines supports llama-cpp-python, but we need to install it ourselves: - -```bash -pip install llama-cpp-python -``` - -We download the model weights by passing the name of the repository on the HuggingFace Hub, and the filenames (or glob pattern): -```python -import llama_cpp -from outlines import generate, models - -model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", - "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False) -``` - -??? note "(Optional) Store the model weights in a custom folder" - - By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/): - - ```bash - wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf - ``` - - We initialize the model: - - ```python - import llama_cpp - from llama_cpp import Llama - from outlines import generate, models - - llm = Llama( - "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf", - tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "NousResearch/Hermes-2-Pro-Llama-3-8B" - ), - n_gpu_layers=-1, - flash_attn=True, - n_ctx=8192, - verbose=False - ) - ``` - -## Build a ReAct agent - -In this example, we use two tools: - -- wikipedia: \ - search Wikipedia and returns the snippet of the first result -- calculate: \ - evaluate an expression using Python's eval() function - -```python -import httpx - -def wikipedia(q): - return httpx.get("https://en.wikipedia.org/w/api.php", params={ - "action": "query", - "list": "search", - "srsearch": q, - "format": "json" - }).json()["query"]["search"][0]["snippet"] - - -def calculate(numexp): - return eval(numexp) -``` - -We define the logic of the agent through a Pydantic class. First, we want the LLM to decide only between the two previously defined tools: - -```python -from enum import Enum - -class Action(str, Enum): - wikipedia = "wikipedia" - calculate = "calculate" -``` - -Our agent will loop through Thought and Action. We explicitly give the Action Input field so it doesn't forget to add the arguments of the Action. We also add a scratchpad (optional). - -```python -from pydantic import BaseModel, Field - -class Reason_and_Act(BaseModel): - Scratchpad: str = Field(..., description="Information from the Observation useful to answer the question") - Thought: str = Field(..., description="It describes your thoughts about the question you have been asked") - Action: Action - Action_Input: str = Field(..., description="The arguments of the Action.") -``` - -Our agent will reach a Final Answer. We also add a scratchpad (optional). - -```python -class Final_Answer(BaseModel): - Scratchpad: str = Field(..., description="Information from the Observation useful to answer the question") - Final_Answer: str = Field(..., description="Answer to the question grounded on the Observation") -``` - -Our agent will decide when it has reached a Final Answer and therefore to stop the loop of Thought and Action. - -```python -from typing import Union - -class Decision(BaseModel): - Decision: Union[Reason_and_Act, Final_Answer] -``` - -We could generate a response using the json schema but we will use the regex and check that everything is working as expected: - -```python -from outlines.integrations.utils import convert_json_schema_to_str -from outlines.fsm.json_schema import build_regex_from_schema - -json_schema = Decision.model_json_schema() -schema_str = convert_json_schema_to_str(json_schema=json_schema) -regex_str = build_regex_from_schema(schema_str) -print(regex_str) -# '\\{[ ]?"Decision"[ ]?:[ ]?(\\{[ ]?"Scratchpad"[ ]?:[ ]?"([^"\\\\\\x00-\\x1F\\x7F-\\x9F]|\\\\["\\\\])*"[ ]?,[ ]?"Thought"[ ]?:[ ]?"([^"\\\\\\x00-\\x1F\\x7F-\\x9F]|\\\\["\\\\])*"[ ]?,[ ]?"Action"[ ]?:[ ]?("wikipedia"|"calculate")[ ]?,[ ]?"Action_Input"[ ]?:[ ]?"([^"\\\\\\x00-\\x1F\\x7F-\\x9F]|\\\\["\\\\])*"[ ]?\\}|\\{[ ]?"Scratchpad"[ ]?:[ ]?"([^"\\\\\\x00-\\x1F\\x7F-\\x9F]|\\\\["\\\\])*"[ ]?,[ ]?"Final_Answer"[ ]?:[ ]?"([^"\\\\\\x00-\\x1F\\x7F-\\x9F]|\\\\["\\\\])*"[ ]?\\})[ ]?\\}' -``` - -We then need to adapt our prompt to the [Hermes prompt format for JSON schema](https://github.com/NousResearch/Hermes-Function-Calling?tab=readme-ov-file#prompt-format-for-json-mode--structured-outputs) and explain the agent logic: - -```python -import datetime - -def generate_hermes_prompt(question, schema=""): - return ( - "<|im_start|>system\n" - "You are a world class AI model who answers questions in JSON with correct Pydantic schema. " - f"Here's the json schema you must adhere to:\n\n{schema}\n\n" - "Today is " + datetime.datetime.today().strftime('%Y-%m-%d') + ".\n" + - "You run in a loop of Scratchpad, Thought, Action, Action Input, PAUSE, Observation. " - "At the end of the loop you output a Final Answer. " - "Use Scratchpad to store the information from the Observation useful to answer the question " - "Use Thought to describe your thoughts about the question you have been asked " - "and reflect carefully about the Observation if it exists. " - "Use Action to run one of the actions available to you. " - "Use Action Input to input the arguments of the selected action - then return PAUSE. " - "Observation will be the result of running those actions. " - "Your available actions are:\n" - "calculate:\n" - "e.g. calulate: 4**2 / 3\n" - "Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary\n" - "wikipedia:\n" - "e.g. wikipedia: Django\n" - "Returns a summary from searching Wikipedia\n" - "DO NOT TRY TO GUESS THE ANSWER. Begin! <|im_end|>" - "\n<|im_start|>user\n" + question + "<|im_end|>" - "\n<|im_start|>assistant\n" - ) -``` - -We define a ChatBot class - -```python -class ChatBot: - def __init__(self, prompt=""): - self.prompt = prompt - - def __call__(self, user_prompt): - self.prompt += user_prompt - result = self.execute() - return result - - def execute(self): - generator = generate.regex(model, regex_str) - result = generator(self.prompt, max_tokens=1024, temperature=0, seed=42) - return result -``` - -We define a query function: - -```python -import json - -def query(question, max_turns=5): - i = 0 - next_prompt = ( - "\n<|im_start|>user\n" + question + "<|im_end|>" - "\n<|im_start|>assistant\n" - ) - previous_actions = [] - while i < max_turns: - i += 1 - prompt = generate_hermes_prompt(question=question, schema=Decision.model_json_schema()) - bot = ChatBot(prompt=prompt) - result = bot(next_prompt) - json_result = json.loads(result)['Decision'] - if "Final_Answer" not in list(json_result.keys()): - scratchpad = json_result['Scratchpad'] if i == 0 else "" - thought = json_result['Thought'] - action = json_result['Action'] - action_input = json_result['Action_Input'] - print(f"\x1b[34m Scratchpad: {scratchpad} \x1b[0m") - print(f"\x1b[34m Thought: {thought} \x1b[0m") - print(f"\x1b[36m -- running {action}: {str(action_input)}\x1b[0m") - if action + ": " + str(action_input) in previous_actions: - observation = "You already run that action. **TRY A DIFFERENT ACTION INPUT.**" - else: - if action=="calculate": - try: - observation = eval(str(action_input)) - except Exception as e: - observation = f"{e}" - elif action=="wikipedia": - try: - observation = wikipedia(str(action_input)) - except Exception as e: - observation = f"{e}" - print() - print(f"\x1b[33m Observation: {observation} \x1b[0m") - print() - previous_actions.append(action + ": " + str(action_input)) - next_prompt += ( - "\nScratchpad: " + scratchpad + - "\nThought: " + thought + - "\nAction: " + action + - "\nAction Input: " + action_input + - "\nObservation: " + str(observation) - ) - else: - scratchpad = json_result["Scratchpad"] - final_answer = json_result["Final_Answer"] - print(f"\x1b[34m Scratchpad: {scratchpad} \x1b[0m") - print(f"\x1b[34m Final Answer: {final_answer} \x1b[0m") - return final_answer - print(f"\nFinal Answer: I am sorry, but I am unable to answer your question. Please provide more information or a different question.") - return "No answer found" -``` - -We can now test our ReAct agent: - -```python -print(query("What's 2 to the power of 10?")) -# Scratchpad: -# Thought: I need to perform a mathematical calculation to find the result of 2 to the power of 10. -# -- running calculate: 2**10 -# -# Observation: 1024 -# -# Scratchpad: 2 to the power of 10 is 1024. -# Final Answer: 2 to the power of 10 is 1024. -# 2 to the power of 10 is 1024. -``` - -```python -print(query("What does England share borders with?")) -# Scratchpad: -# Thought: To answer this question, I will use the 'wikipedia' action to gather information about England's geographical location and its borders. -# -- running wikipedia: England borders -# -# Observation: Anglo-Scottish border (Scottish Gaelic: Crìochan Anglo-Albannach) is an internal border of the United Kingdom separating Scotland and England which runs for -# -# Scratchpad: Anglo-Scottish border (Scottish Gaelic: Crìochan Anglo-Albannach) is an internal border of the United Kingdom separating Scotland and England which runs for -# Final Answer: England shares a border with Scotland. -# England shares a border with Scotland. -``` - -As mentioned in Simon's blog post, this is not a very robust implementation at all and there's a ton of room for improvement. But it is lovely how simple it is with a few lines of Python to make these extra capabilities available to the LLM. And now you can run it locally with an open weights LLM. - -This example was originally contributed by [Alonso Silva](https://github.com/alonsosilvaallende). diff --git a/docs/cookbook/simtom.md b/docs/cookbook/simtom.md deleted file mode 100644 index aa96005b..00000000 --- a/docs/cookbook/simtom.md +++ /dev/null @@ -1,129 +0,0 @@ -# Build perspective-taking agents with SimToM - -Prompting strategies like Chain-of-Thought (CoT) can improve LLMs' reasoning capabilities. However, they underwhelm in tasks that require keeping track of inconsistent world states. [SimToM](https://arxiv.org/abs/2311.10227) proposes a simple, two-stage prompting framework for LLMs inspired by Simulation Theory. The authors showed that this approach outperforms zero-shot prompting and CoT on ToMI and BigToM, two benchmarks with Theory of Mind questions. - -In this example, we will implement SimToM with a few lines of code using Outlines' prompt templating and structured generation capabilities. - -## How SimToM works - -SimToM calls an LLM with two consecutive prompts: - -1. **Perspective-taking**: The first prompt receives a `story` and a `character`. The goal is to understand the situation based on the character's point of view and filter out the rest of the story. -2. **Question-Answering**: The second prompt receives the character's point of view from the previous step and tasks the LLM to answer a question using that context. - -![Figure 2 in the paper](./images/simtom.png) - -## Outlines implementation - -To implement SimToM with Outlines, we will need to: - -1. Write the prompts with [prompt functions](https://outlines-dev.github.io/outlines/reference/prompting/). -2. Define the JSON object each prompt will return using Pydantic. -3. Generate responses with a Mistral model using the [transformers integration](https://outlines-dev.github.io/outlines/reference/models/transformers/). - -Let's dive into it! - -### Using Prompt Functions - -With Outlines, you can write your prompts as Python functions by adding the `@outlines.prompt` decorator. The prompt template is contained in their docstring, and their arguments correspond to variables used in the prompt. - -The authors have shared their code, prompts and data in [this GitHub repository](https://github.com/shawnsihyunlee/simulatedtom). Below, we define in Outlines the prompts they used for the ToMI dataset: - -```python -import outlines - - -@outlines.prompt -def perspective_taking(story: str, character: str) -> None: - """[INST] The following is a sequence of events about some characters, that takes place in multiple locations. - Your job is to output only the events that the specified character, {{character}}, knows about. - - Here are a few rules: - 1. A character knows about all events that they do. - 2. If a character is in a certain room/location, that character knows about all other events that happens in the room. This includes other characters leaving or exiting the location, the locations of objects in that location, and whether somebody moves an object to another place. - 3. If a character leaves a location, and is NOT in that location, they no longer know about any events that happen within that location. However, they can re-enter the location. - - Story: {{story}} - What events does {{character}} know about? Only output the events according to the above rules, do not provide an explanation. [/INST]""" # noqa - -@outlines.prompt -def simulation(events: list, name: str, question: str) -> None: - """[INST] {% for event in events %} - {{event}} - {% endfor %} - You are {{name}}. - Based on the above information, answer the following question: - {{question}} - You must choose one of the above choices, do not say there is not enough information. Answer with a single word, do not output anything else. [/INST]""" # noqa -``` - -### JSON Structured Generation - -Outlines guarantees that the LLM will return a valid JSON object, which we can specify as a Pydantic model. - -We will need two Pydantic models for SimToM, one for each prompt: - -```python -from pydantic import BaseModel, Field -from typing import List - - -class PerspectiveTaking(BaseModel): - """This is for the first prompt.""" - character: str = Field(description="The character we extract the events for.") - events: List[str] = Field(description="All events that the character knows about.") - - -class Simulation(BaseModel): - """This is for the second prompt.""" - answer: str -``` - -### Calling an LLM - -Let's try SimToM with an example from the ToMI dataset: - -```python -story = """ -1 Aria entered the front_yard. -2 Aiden entered the front_yard. -3 The grapefruit is in the green_bucket. -4 Aria moved the grapefruit to the blue_container. -5 Aiden exited the front_yard. -6 Noah entered the playroom. -""" -question = "7 Where was the grapefruit at the beginning?" -character = "Aria" -``` - -We load `Mistral-7B-Instruct-v0.3`, create the prompt using the template we defined earlier, and generate a structured response. As a reminder, the goal of the first call is to get all the events a character, `Aria`, knows about. - -```python -# Load an LLM from Hugging Face -MODEL_NAME = "mistral-community/Mistral-7B-Instruct-v0.3" -model = outlines.models.transformers(MODEL_NAME, device="cuda") - -perspective_prompt = perspective_taking(story=story, character=character) - -# Call Mistral 7B with the first prompt -generator = outlines.generate.json(model, PerspectiveTaking) -perspective = generator(perspective_prompt) - -print(perspective.model_dump()) -# {'character': 'Aria', 'events': ['1 Aria entered the front_yard.', '3 The grapefruit is in the green_bucket.', '4 Aria moved the grapefruit to the blue_container.']} -``` - -Not bad! We will now generate the second prompt with those events. - -```python -sim_prompt = simulation(events=perspective.events, name=character, question=question) - -# Call Mistral 7B with the second prompt -generator = outlines.generate.json(model, Simulation) -result = generator(sim_prompt) - -print(result.model_dump()) -# {'answer': 'green_bucket'} -``` - -And this is it! SimToM could be useful in agentic workflows, where agents must act based on what they know, not all available information. One caveat of SimToM is that the perspective-taking step may remove important information, leading to wrong results. As the authors note in their paper, it can feature as a simple and effective baseline for evaluating LLMs on Theory of Mind reasoning tasks. diff --git a/docs/cookbook/structured_generation_workflow.md b/docs/cookbook/structured_generation_workflow.md deleted file mode 100644 index b9d7c28a..00000000 --- a/docs/cookbook/structured_generation_workflow.md +++ /dev/null @@ -1,215 +0,0 @@ -# Structured Generation Workflow: Generating Synthetic Phone Numbers - -This is a condensed version of [Coding for Structured Generation with LLMs](https://blog.dottxt.co/coding-for-structured-generation.html). - -For this example we're going to be building an LLM program to generate **synthetic data** in the form of realistic looking phone numbers for Washington State. Using an LLM for this task *is a bit overkill* since we could just as easily accomplish this -with a tool like [Faker](https://fakerjs.dev/), but this example still serves as a useful way to demonstrate a workflow for using structured generation. - -## Unstructured approach - -Before diving into how to use structure generation for this task let's start with an unstructured example. We begin by loading our model: - -```python -import outlines - -model_name = 'microsoft/Phi-3-mini-4k-instruct' -model = outlines.models.transformers(model_name) -``` - -Next we need a prompt for this model. Since we're focusing on structured generation, we won't be engaging in any form of "prompt hacking" and will be leaving this prompt untouched for the rest of this example. - -```python -tokenizer = AutoTokenizer.from_pretrained(model_name) - -messages_phone = [ - {"role": "user", "content": """ - Please generate a realistic phone number for Washington State in the following format - - (555) 555-5555 - - """} -] - -# This allows us to properly format our prompt for -# Phi-3 Mini's 'Instruct' interface. -prompt_phone = tokenizer.apply_chat_template(messages_phone, tokenize=False) -``` - -With our prompt ready we can now generate 10 example phone numbers - -```python -phone_generator_unstruct = outlines.generate.text(model) -for _ in range(10): - print(phone_generator_unstruct(prompt_phone,max_tokens=12)) -``` - -> I'd be happy to help you generate a realistic phone\ -I cannot generate a real phone number as I'm just\ -I'm an AI and don't have the ability\ -Sure! Here is a randomly generated phone number in the format\ -Here's a phone number that fits the format for a\ -In Washington State, phone numbers typically have a three-dig\ -Here are a few examples of phone numbers that could be considered\ -I'd be happy to help generate a realistic phone number\ -I'd be happy to help you generate a random phone\ -Based on the format you provided, a realistic phone number for\ - -As we can see, none of these outputs are even phone numbers! - -Let's see if we can improve this using structured generation. - -## The Structured Generation Workflow - -In order to solve this problem we're going to introduce a *Structured Generation Workflow* outlined in this image: - -!["Visual of Structured Generation Workflow"](./images/coding_structure_diagram.png) - -Let's step through this: - -### Real example - -We start with a real example phone number, in this case for the Seattle Public Library, that we can use to verify the structure we are creating. - -```python -phone_number = "(206) 386-4636" -``` - -For a simple example like this, we'll just be using a single phone number, for more complex examples it can be helpful to have more examples. - -### Draft Structure - -The next step in the process is for use to define a simple regex that we feel correctly models our real data. - -```python -phone_regex_1 = r'\([0-9]{3}\) [0-9]{3}-[0-9]{4}' -``` - -Next we need to validate this regex against our real data. - -### Validate by matching examples - -Whenever writing non-trivial code with structured generation it is *essential* that you first validate the code against your real data example(s). - -We'll start with a simple method of validation: just checking that our regex matches the data. - -``` -import re -re.match(phone_regex_1, phone_number) - -# - -``` - -Now that we have a match, we can move on to generating structured output! - -### Generate Structure - -We're ready to see if structured generation can make an improvement over our initial unstructured approach: - -```python -phone_generator_v1 = outlines.generate.regex(model, phone_regex_1) -for _ in range(10): - print(phone_generator_v1(prompt_phone)) -``` -> (206) 555-1234\ -(206) 555-1234\ -(206) 555-1234\ -(206) 555-1234\ -(206) 555-1234\ -(206) 555-1234\ -(206) 123-4567\ -(206) 555-1234\ -(206) 555-1234\ -(206) 555-1234 - -At least we have phone numbers! But I think we can do better! - -### Inspect output - -In this case the model *did* create phone numbers and, impressively, got the area code correct. So using structured generation did improve things. However these numbers are pretty boring. Let's improve that structure! - -## Iteration - -We've walked through the loop once, so we can go quickly now through each iteration. - -We start by improving our structure: - -```python -phone_regex_2 = r'\([0-9]{3}\) [2-46-9]{3}-[02-9]{4}' -``` - -Before rushing to another round of generation, let's validate this new regex. We'll add just a bit more sophistication over our last check: - -```python -re.match(phone_regex_2, phone_number)[0] == phone_number -# True -``` -Now that we've validated, let's generate with this new regex! - -```python -phone_generator_v2 = outlines.generate.regex(model, - phone_regex_2) -for _ in range(10): - print(phone_generator_v2(prompt_phone)) -``` - -> (206) 867-5309\ -(206) 666-7777\ -(206) 444-3333\ -(206) 444-3333\ -(206) 943-2222\ -(206) 323-6789\ -(206) 444-3333\ -(206) 867-5309\ -(206) 466-2255\ -(206) 222-3333 - -Better, but I don't like those repeated sequences. Like good software developers, let's iterate again! - -## Reiteration - with debugging - -Here's a fancier regex that should give us more interesting results: - -```python -phone_regex_3_error = r'\([0-9]{3}\) [2-4][7-9][4-6]-[3-6][2-8][1-4]' -``` - -This looks good to me, but there's a subtle bug, that's why we *always* need to validate our structure against real data. This time we'll make our validator do a bit more work to verify the correct string is matched: - -```python -if not re.match(phone_regex_3_error, phone_number): - print("Regex fails match") -else: - matched_string = re.match(phone_regex_3_error, phone_number)[0] - if matched_string == phone_number: - print("Successful match") - else: - print(f"Error {matched_string} != {phone_number}") -``` -This prints out: -> Error (206) 386-463 != (206) 386-4636 - -Ah! We were missing the last digit, let's fix that and regenerate: - -```python -phone_regex_3_fixed = r'\([0-9]{3}\) [2-4][7-9][4-6]-[3-6][2-8][1-4][6-9]' -phone_generator_v3 = outlines.generate.regex(model, - phone_regex_3_fixed) -for _ in range(10): - print(phone_generator_v3(prompt_phone)) -``` - ->(206) 494-3216\ -(206) 374-6218\ -(206) 494-3337\ -(206) 476-3216\ -(206) 484-3548\ -(206) 495-3218\ -(206) 494-5517\ -(206) 375-4636\ -(206) 384-6216\ -(206) 385-6218 - -Much better! - -Now you've seen a quick example of the structured generation workflow that can be used at the basis for building and iteration on much larger structured generation tasks! diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index b19ebd57..00000000 --- a/docs/index.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -title: Outlines -hide: - - navigation - - toc - - feedback ---- - -# - -

- ![Image title](assets/images/logo.png){ width="300" } -
- -
-

Generate text with LLMs

-

Robust prompting & (structured) text generation

- [:fontawesome-solid-bolt: Get started](welcome.md){ .md-button .md-button--primary } - [:fontawesome-brands-discord: Join the Community](https://discord.gg/ZxBxyWmW5n){ .md-button } - -
-```bash -pip install outlines -``` -
-
diff --git a/docs/installation.md b/docs/installation.md deleted file mode 100644 index 1017b627..00000000 --- a/docs/installation.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: installation ---- - -# Installation - -You can install Outlines with `pip`: - -```python -pip install outlines -``` - -Outlines supports OpenAI, transformers, Mamba, llama.cpp and exllama2 but **you will need to install them manually**: - -```python -pip install openai -pip install transformers datasets accelerate torch -pip install llama-cpp-python -pip install exllamav2 transformers torch -pip install mamba_ssm transformers torch -pip install vllm -``` - -If you encounter any problem using Outlines with these libraries, take a look at their installation instructions. The installation of `openai` and `transformers` should be straightforward, but other libraries have specific hardware requirements. - -## Bleeding edge - -You can install the latest version of Outlines on the repository's `main` branch: - -```python -pip install git+https://github.com/outlines-dev/outlines.git@main -``` - -This can be useful, for instance, when a fix has been merged but not yet released. - -## Installing for development - -See the [contributing documentation](community/contribute.md) for instructions on how to install Outlines for development. diff --git a/docs/licence.md b/docs/licence.md deleted file mode 100644 index a74661fa..00000000 --- a/docs/licence.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Licence ---- - -# Licence and citations - -Outlines is licenced under the Apache 2.0 licence. To comply with the licence you need to add the following notice at the top every file that uses part of Outlines' code: - -``` -Copyright 2023- The Outlines developers - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -``` - -If you use Outlines in your work you can use the following citation: - -``` -@article{willard2023efficient, - title={Efficient Guided Generation for LLMs}, - author={Willard, Brandon T and Louf, R{\'e}mi}, - journal={arXiv preprint arXiv:2307.09702}, - year={2023} -} -``` diff --git 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zYtkqiSP}$hvkF82_xfdbBT*;HUt_&=jxrh1BEd4^ zuUEefX%)CJh$8-G3EJvLQP6XPDhQ{&g1A_v2e?PzWk7H|lE>fsB=zcsgr3GBVr*_YR|t_`r<5)wFKenDk%OjQ zdBvAY%_^B=Nuua+oel3g3pY(zVgSxXGC*XdlyeV{j<`J!-Gos%dxM`_?z}t&*D~zU z|NmP@{UzdW<4e}Jsh>e05FGcFjqlr7-M5vx<6#T`5JCb%LVN^2V$T2L1J15Cj&{EP`v?5m<452F2z6y`r7{J}(EkJXLX}(q diff --git a/docs/overrides/index.html b/docs/overrides/index.html deleted file mode 100644 index 74a4987f..00000000 --- a/docs/overrides/index.html +++ /dev/null @@ -1,11 +0,0 @@ -{% extends "base.html" %} - -{% block announce %} - For updates follow @remilouf on - - - Twitter - -{% endblock %} diff --git a/docs/overrides/main.html b/docs/overrides/main.html deleted file mode 100644 index b4183d71..00000000 --- a/docs/overrides/main.html +++ /dev/null @@ -1,22 +0,0 @@ -{% extends "base.html" %} - -{% block announce %} - For updates follow @dottxtai on - - - Twitter - - and - - {% include ".icons/fontawesome/solid/star.svg" %} - - the repo on - - - {% include ".icons/fontawesome/brands/github.svg" %} - - Github - -{% endblock %} diff --git a/docs/quickstart.md b/docs/quickstart.md deleted file mode 100644 index 2e1f9a6b..00000000 --- a/docs/quickstart.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -title: Quickstart ---- - -# Quickstart - -After [installing Outlines](installation.md), the fastest way to get to up to speed with the library is to get acquainted with its few core elements. We advise you to take a quick look at this page to see everything Outlines has to offer before diving in the [documentation](reference/index.md). - - -## Core elements - -### Models - -The first step when writing a program with Outlines is to initialize a model. Weights will be loaded on the device at this step: - -```python -import outlines - -model = outlines.models.transformers( - "microsoft/Phi-3-mini-4k-instruct", - device="cuda" # optional device argument, default is cpu -) -``` - -Outlines supports a wide variety of inference engines and model weight types. More details on different models can be found in the [Outlines Models](./reference/models/models.md) documentation page. - -### Generation - -Once the model is initialized you can build an `outlines.generate` generator. This generator can be called with a prompt directly. - -([Outlines Structured Generation Full Documentation](./reference/generation/generation.md)) - -=== "Text" - - ```python - generator = outlines.generate.text(model) - - result = generator("Question: What's 2+2? Answer:", max_tokens=100) - print(result) - # The answer is 4 - - # Outlines also supports streaming output - stream = generator.stream("What's 2+2?", max_tokens=4) - for i in range(5): - token = next(stream) - print(repr(token)) - # '2' - # '+' - # '2' - # ' equals' - # '4' - ``` - -=== "Structured" - - Along with typical language model generation behavior via, `outlines.generate.text`, Outlines supports structured generation, which guarantees the tokens generated by the model will follow a predefined structure. Structures can be defined by a regex pattern, JSON schema, python object type, or a Lark grammar defining a parsable language such as SQL or Python. - - Example: using pydantic to enforce a JSON schema - - ```python - from enum import Enum - from pydantic import BaseModel, constr, conint - - class Character(BaseModel): - name: constr(max_length=10) - age: conint(gt=18, lt=99) - armor: (Enum('Armor', {'leather': 'leather', 'chainmail': 'chainmail', 'plate': 'plate'})) - strength: conint(gt=1, lt=100) - - generator = outlines.generate.json(model, Character) - - character = generator( - "Generate a new character for my awesome game: " - + "name, age (between 1 and 99), armor and strength. " - ) - print(character) - # Character(name='Zara', age=25, armor=, strength=85) - ``` - -## [Deploy using vLLM and FastAPI](./reference/serve/vllm.md) - -Outlines can be deployed as a LLM service using [vLLM][vllm]{:target="_blank"} and [FastAPI][fastapi]{:target="_blank"}. The server supports asynchronous processing of incoming requests, and benefits from the performance of vLLM. - -First start the server: - -```python -python -m outlines.serve.serve --model="microsoft/Phi-3-mini-4k-instruct" -``` - -Or you can start the server with Outlines' official Docker image: - -```bash -docker run -p 8000:8000 outlinesdev/outlines --model="microsoft/Phi-3-mini-4k-instruct" -``` - -This will by default start a server at `http://127.0.0.1:8000` (check what the console says, though). Without the `--model` argument set, the OPT-125M model is used. - - -You can then query the model in shell by passing a prompt and a [JSON Schema][jsonschema]{:target="_blank"} specification for the structure of the output: - -```bash -curl http://127.0.0.1:8000/generate \ - -d '{ - "prompt": "Question: What is a language model? Answer:", - "schema": {"type": "string"} - }' -``` - -Or use the [requests][requests]{:target="_blank"} library from another python program. You can read the [vLLM documentation][vllm]{:target="_blank"} for more details. - -## Utilities - -### [Prompt templates](./reference/prompting.md) - -Prompting can lead to messy code. Outlines' prompt functions are python functions that contain a template for the prompt in their docstring. We use a powerful templating language to allow you to loop over lists, dictionaries, add conditionals, etc. directly from the prompt. When called, a prompt function returns the rendered template: - -```python -import outlines - -@outlines.prompt -def few_shots(instructions, examples, question): - """{{ instructions }} - - Examples - -------- - - {% for example in examples %} - Q: {{ example.question }} - A: {{ example.answer }} - - {% endfor %} - Question - -------- - - Q: {{ question }} - A: - """ - -instructions = "Please answer the following question following the examples" -examples = [ - {"question": "2+2=?", "answer":4}, - {"question": "3+3=?", "answer":6} -] -question = "4+4 = ?" - -prompt = few_shots(instructions, examples, question) -print(prompt) -# Please answer the following question following the examples - -# Examples -# -------- - -# Q: 2+2=? -# A: 4 - -# Q: 3+3=? -# A: 6 - -# Question -# -------- - -# Q: 4+4 = ? -# A: -``` - -### Outlines functions - -Once you are done experimenting with a prompt and an output structure, it is useful to be able to encapsulate all of these in a single function that can be called from other parts of the program. This is what `outlines.Function` allows you to do: - -=== "function.py" - - ```python - from pydantic import BaseModel - - import outlines - - - @outlines.prompt - def tell_a_joke(topic): - """Tell me a joke about {{ topic }}.""" - - class Joke(BaseModel): - setup: str - punchline: str - - generate_joke = outlines.Function( - tell_a_joke, - Joke, - "microsoft/Phi-3-mini-4k-instruct" - ) - ``` - -=== "Call a function" - - ```python - from .function import generate_joke - - response = generate_joke("baseball") - - # haha - # Joke(setup='Why was the baseball in a bad mood?', punchline='Because it got hit around a lot.') - ``` - -=== "Call a function stored on GitHub" - - You can load a function that is stored on a repository on GitHub directly from Outlines. Say `Someone` stores a function in `joke.py` at the root of the `TheirRepo` repository: - - ```python - import outlines - - joke = outlines.Function.from_github("Someone/TheirRepo/joke") - response = joke("baseball") - ``` - It make it easier for the community to collaborate on the infinite number of use cases enabled by these models! - - -## Going further - -If you need more inspiration you can take a look at the [cookbook](cookbook/index.md) or watch [Remi Louf's AI Engineer World’s Fair Presentation on Outlines](https://www.youtube.com/live/R0X7mPagRiE?t=775s). If you have any question, or requests for documentation please reach out to us on [GitHub](https://github.com/outlines-dev/outlines/discussions), [Twitter](https://twitter.com/remilouf) or [Discord](https://discord.gg/UppQmhEpe8). - - -[pydantic]: https://docs.pydantic.dev/latest -[jsonschema]: https://json-schema.org/ -[fastapi]: https://fastapi.tiangolo.com/ -[cfg]: https://en.wikipedia.org/wiki/Context-free_grammar -[ebnf]: https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form -[requests]: https://requests.readthedocs.io/en/latest/ -[vllm]: https://docs.vllm.ai/en/latest/index.html diff --git a/docs/reference/functions.md b/docs/reference/functions.md deleted file mode 100644 index d29213a8..00000000 --- a/docs/reference/functions.md +++ /dev/null @@ -1 +0,0 @@ -# Outlines functions diff --git a/docs/reference/generation/cfg.md b/docs/reference/generation/cfg.md deleted file mode 100644 index 4f0285c1..00000000 --- a/docs/reference/generation/cfg.md +++ /dev/null @@ -1,149 +0,0 @@ -# Grammar-structured generation - -You can pass any context-free grammar in the EBNF format and Outlines will generate an output that is valid to this grammar: - -```python -from outlines import models, generate - -arithmetic_grammar = """ - ?start: expression - - ?expression: term (("+" | "-") term)* - - ?term: factor (("*" | "/") factor)* - - ?factor: NUMBER - | "-" factor - | "(" expression ")" - - %import common.NUMBER -""" - -model = models.transformers("WizardLM/WizardMath-7B-V1.1") -generator = generate.cfg(model, arithmetic_grammar) -sequence = generator( - "Alice had 4 apples and Bob ate 2. " - + "Write an expression for Alice's apples:" -) - -print(sequence) -# (8-2) -``` - -!!! Note "Performance" - - The implementation of grammar-structured generation in Outlines is very naive. This does not reflect the performance of [.txt](https://dottxt.co)'s product, where we made grammar-structured generation as fast as regex-structured generation. - - -## Ready-to-use grammars - -Outlines contains a (small) library of grammars that can be imported and use directly. We can rewrite the previous example as: - -```python -from outlines import models, generate - -arithmetic_grammar = outlines.grammars.arithmetic - -model = models.transformers("WizardLM/WizardMath-7B-V1.1") -generator = generate.cfg(model, arithmetic_grammar) -sequence = generator( - "Alice had 4 apples and Bob ate 2. " - + "Write an expression for Alice's apples:" -) - -print(sequence) -# (8-2) -``` - -The following grammars are currently available: - -- Arithmetic grammar via `outlines.grammars.arithmetic` -- JSON grammar via `outlines.grammars.json` - -If you would like more grammars to be added to the repository, please open an [issue](https://github.com/outlines-dev/outlines/issues) or a [pull request](https://github.com/outlines-dev/outlines/pulls). - - -## Grammar guide - -A grammar is a list of rules and terminals that define a *language*: - -- Terminals define the vocabulary of the language; they may be a string, regular expression or combination of these and other terminals. -- Rules define the structure of that language; they are a list of terminals and rules. - -Outlines uses the [Lark library](https://github.com/lark-parser/lark) to make Large Language Models generate text in a language of a grammar, it thus uses grammars defined in a format that Lark understands, based on the [EBNF syntax](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form). Read the [Lark documentation](https://lark-parser.readthedocs.io/en/stable/grammar.html) for more details on grammar, the following is a small primer that should help get your started. - -In the following we will define a [LOGO-like toy language](https://github.com/lark-parser/lark/blob/master/examples/turtle_dsl.py) for python's [turtle](https://docs.python.org/3/library/turtle.html) library. - -### Terminals - -A turtle can take 4 different `MOVEMENT` move instructions: forward (`f`), backward (`b`), turn right (`r`) and turn left (`l`). It can take `NUMBER` number of steps in each direction, and draw lines in a specified `COLOR`. These define the vocabulary of our language: - -```ebnf -MOVEMENT: "f"|"b"|"r"|"l" -COLOR: LETTER+ - -%import common.LETTER -%import common.INT -> NUMBER -%import common.WS -%ignore WS -``` - -The lines that start with `%` are called "directive". They allow to import pre-defined terminals and rules, such as `LETTER` and `NUMBER`. `LETTER+` is a regular expressions, and indicates that a `COLOR` is made of at least one `LETTER`. The last two lines specify that we will ignore white spaces (`WS`) in the grammar. - -### Rules - -We now need to define our rules, by decomposing instructions we can send to the turtle via our python program. At each line of the program, we can either choose a direction and execute a given number of steps, change the color used to draw the pattern. We can also choose to start filling, make a series of moves, and stop filling. We can also choose to repeat a series of move. - -We can easily write the first two rules: - -```ebnf -instruction: MOVEMENT NUMBER -> movement - | "c" COLOR [COLOR] -> change_color -``` - -where `movement` and `change_color` represent aliases for the rules. A whitespace implied concatenating the elements, and `|` choosing either of the elements. The `fill` and `repeat` rules are slightly more complex, since they apply to a code block, which is made of instructions. We thus define a new `code_block` rule that refers to `instruction` and finish implementing our rules: - -```ebnf -instruction: MOVEMENT NUMBER -> movement - | "c" COLOR [COLOR] -> change_color - | "fill" code_block -> fill - | "repeat" NUMBER code_block -> repeat - -code_block: "{" instruction "}" -``` - -We can now write the full grammar: - -```ebnf -start: instruction+ - -instruction: MOVEMENT NUMBER -> movement - | "c" COLOR [COLOR] -> change_color - | "fill" code_block -> fill - | "repeat" NUMBER code_block -> repeat - -code_block: "{" instruction+ "}" - -MOVEMENT: "f"|"b"|"l"|"r" -COLOR: LETTER+ - -%import common.LETTER -%import common.INT -> NUMBER -%import common.WS -%ignore WS -``` - -Notice the `start` rule, which defines the starting point of the grammar, i.e. the rule with which a program must start. This full grammars allows us to parse programs such as: - -```python -c red yellow - fill { repeat 36 { - f200 l170 - }} -``` - -The result of the parse, the parse tree, can then easily be translated into a Python program that uses the `turtle` library to draw a pattern. - -### Next steps - -This section provides a very brief overview of grammars and their possibilities. Check out the [Lark documentation](https://lark-parser.readthedocs.io/en/stable/index.html) for more thorough explanations and more examples. diff --git a/docs/reference/generation/choices.md b/docs/reference/generation/choices.md deleted file mode 100644 index aed5af5a..00000000 --- a/docs/reference/generation/choices.md +++ /dev/null @@ -1,16 +0,0 @@ -# Multiple choices - -Oultines allows you to make sure the generated text is chosen between different options: - -```python -from outlines import models, generate - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.choice(model, ["skirt", "dress", "pen", "jacket"]) -answer = generator("Pick the odd word out: skirt, dress, pen, jacket") - -``` - -!!! Note "Performance" - - `generation.choice` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate from the same list of choices several times make sure that you only call `generate.choice` once. diff --git a/docs/reference/generation/custom_fsm_ops.md b/docs/reference/generation/custom_fsm_ops.md deleted file mode 100644 index 5c4be96f..00000000 --- a/docs/reference/generation/custom_fsm_ops.md +++ /dev/null @@ -1,37 +0,0 @@ -# Custom FSM Operations - -Outlines is fast because it compiles regular expressions into an index ahead of inference. To do so we use the equivalence between regular expressions and Finite State Machines (FSMs), and the library [interegular](https://github.com/MegaIng/interegular) to perform the translation. - -Alternatively, one can pass a FSM built using `integular` directly to structure the generation. - -## Example - -### Using the `difference` operation - -In the following example we build a fsm which recognizes only the strings valid to the first regular expression but not the second. In particular, it will prevent the words "pink" and "elephant" from being generated: - -```python -import interegular -from outlines import models, generate - - -list_of_strings_pattern = """\["[^"\s]*"(?:,"[^"\s]*")*\]""" -pink_elephant_pattern = """.*(pink|elephant).*""" - -list_of_strings_fsm = interegular.parse_pattern(list_of_strings_pattern).to_fsm() -pink_elephant_fsm = interegular.parse_pattern(pink_elephant_pattern).to_fsm() - -difference_fsm = list_of_strings_fsm - pink_elephant_fsm - -difference_fsm_fsm.accepts('["a","pink","elephant"]') -# False -difference_fsm_fsm.accepts('["a","blue","donkey"]') -# True - - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.fsm(model, difference_fsm) -response = generator("Don't talk about pink elephants") -``` - -To see the other operations available, consult [interegular's documentation](https://github.com/MegaIng/interegular/blob/master/interegular/fsm.py). diff --git a/docs/reference/generation/format.md b/docs/reference/generation/format.md deleted file mode 100644 index 749baa8b..00000000 --- a/docs/reference/generation/format.md +++ /dev/null @@ -1,23 +0,0 @@ -# Type constraints - -We can ask completions to be restricted to valid python types: - -```python -from outlines import models, generate - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.format(model, int) -answer = generator("When I was 6 my sister was half my age. Now I’m 70 how old is my sister?") -print(answer) -# 67 -``` - -The following types are currently available: - -- int -- float -- bool -- datetime.date -- datetime.time -- datetime.datetime -- We also provide [custom types](types.md) diff --git a/docs/reference/generation/generation.md b/docs/reference/generation/generation.md deleted file mode 100644 index 0c090f8a..00000000 --- a/docs/reference/generation/generation.md +++ /dev/null @@ -1,216 +0,0 @@ ---- -title: Generation ---- - -# Generation - -Once an [Outlines model](../models) is constructed you can use `outlines.generate` to generate text. Standard LLM generation is possible via `outlines.generate.text`, along with a variety of structured generation methods described below. (For a detailed technical explanation of how structured generation works, you may review the [Structured Generation Explanation](./structured_generation_explanation.md) page) - -Before generating text, you must construct an `outlines.model`. Example: - -```python -import outlines - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct", device="cuda") -``` -### Text generator - -```python -generator = outlines.generate.text(model) - -result = generator("Question: What's 2+2? Answer:", max_tokens=100) -print(result) -# The answer is 4 - -# Outlines also supports streaming output -stream = generator.stream("What's 2+2?", max_tokens=4) -for i in range(5): - token = next(stream) - print(repr(token)) -# '2' -# '+' -# '2' -# ' equals' -# '4' -``` - -### [Multi-label classification](./choices.md) - -Outlines allows you to do multi-label classification by guiding the model so it can only output either of the specified choices: - -```python -import outlines - -model = outlines.models.transformers("microsoft/Phi-3-mini-128k-instruct") -generator = outlines.generate.choice(model, ["Blue", "Red", "Yellow"]) - -color = generator("What is the closest color to Indigo? ") -print(color) -# Blue -``` - -### [JSON-structured generation](./json.md) - -Outlines can guide models so that they output valid JSON **100%** of the time. You can either specify the structure using [Pydantic][pydantic]{:target="_blank"} or a string that contains a [JSON Schema][jsonschema]{:target="_blank"}: - -=== "Pydantic" - - ```python - from enum import Enum - from pydantic import BaseModel, constr, conint - - import outlines - - class Armor(str, Enum): - leather = "leather" - chainmail = "chainmail" - plate = "plate" - - - class Character(BaseModel): - name: constr(max_length=10) - age: conint(gt=18, lt=99) - armor: Armor - strength: conint(gt=1, lt=100) - - model = outlines.models.transformers("microsoft/Phi-3-mini-128k-instruct") - generator = outlines.generate.json(model, Character) - - character = generator( - "Generate a new character for my awesome game: " - + "name, age (between 1 and 99), armor and strength. " - ) - print(character) - # name='Orla' age=21 armor= strength=8 - ``` - -=== "JSON Schema" - - ```python - import outlines - - schema = """{ - "$defs": { - "Armor": { - "enum": ["leather", "chainmail", "plate"], - "title": "Armor", - "type": "string" - } - }, - "properties": { - "name": {"maxLength": 10, "title": "Name", "type": "string"}, - "age": {"title": "Age", "type": "integer"}, - "armor": {"$ref": "#/$defs/Armor"}, - "strength": {"title": "Strength", "type": "integer"}\ - }, - "required": ["name", "age", "armor", "strength"], - "title": "Character", - "type": "object" - }""" - - model = outlines.models.transformers("microsoft/Phi-3-mini-128k-instruct") - generator = outlines.generate.json(model, schema) - character = generator( - "Generate a new character for my awesome game: " - + "name, age (between 1 and 99), armor and strength. " - ) - print(character) - # {'name': 'Yuki', 'age': 24, 'armor': 'plate', 'strength': 3} - ``` - -!!! Note - - We advise you to constrain the length of the strings fields when first testing your schema, especially with small models. - -### [Grammar-structured generation](./cfg.md) - -Outlines also allows to generate text that is valid to any [context-free grammar][cfg]{:target="_blank"} (CFG) in the [EBNF format][ebnf]{:target="_blank"}. Grammars can be intimidating, but they are a very powerful tool! Indeed, they determine the syntax of every programming language, valid chess moves, molecule structure, can help with procedural graphics generation, etc. - -Here we show a simple example of a grammar that defines arithmetic operations: - -```python -from outlines import models, generate - -arithmetic_grammar = """ - ?start: sum - - ?sum: product - | sum "+" product -> add - | sum "-" product -> sub - - ?product: atom - | product "*" atom -> mul - | product "/" atom -> div - - ?atom: NUMBER -> number - | "-" atom -> neg - | "(" sum ")" - - %import common.NUMBER - %import common.WS_INLINE - - %ignore WS_INLINE -""" - -model = models.transformers("microsoft/Phi-3-mini-128k-instruct") -generator = generate.cfg(model, arithmetic_grammar, max_tokens=100) - -result = generator("Question: How can you write 5*5 using addition?\nAnswer:") -print(result) -# 5+5+5+5+5 -``` - - -EBNF grammars can be cumbersome to write. This is why Outlines provides grammar definitions in the `outlines.grammars.` module - -```python -from outlines import models, generate, grammars - -model = models.transformers("microsoft/Phi-3-mini-128k-instruct") -generator = generate.cfg(model, grammars.arithmetic, max_tokens=100) - -result = generator("Question: How can you write 5*5 using addition?\nAnswer:") -print(result) -# 5+5+5+5+5 -``` - -The available grammars are listed [here](https://github.com/outlines-dev/outlines/tree/main/outlines/grammars). - - -### [Regex-structured generation](./regex.md) - -Slightly simpler, but no less useful, Outlines can generate text that is in the language of a [regular expression](https://www.regular-expressions.info/tutorial.html). For instance to force the model to generate IP addresses: - -```python -from outlines import models, generate - -model = models.transformers("microsoft/Phi-3-mini-128k-instruct") - -regex_str = r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)" -generator = generate.regex(model, regex_str) - -result = generator("What is the IP address of localhost?\nIP: ") -print(result) -# 127.0.0.100 -``` - -### [Generate a given Python type](./types.md) - -We provide a shortcut to regex-structured generation for simple use cases. Pass a Python type to the `outlines.generate.format` function and the LLM will output text that matches this type: - -```python -from outlines import models, generate - -model = models.transformers("microsoft/Phi-3-mini-128k-instruct") -generator = generate.format(model, int) - -result = generator("What is 2+2?") -print(result) -# 4 -``` - - -[jsonschema]: https://json-schema.org/learn/getting-started-step-by-step -[pydantic]: https://docs.pydantic.dev/latest -[cfg]: https://en.wikipedia.org/wiki/Context-free_grammar -[ebnf]: https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form diff --git a/docs/reference/generation/json.md b/docs/reference/generation/json.md deleted file mode 100644 index da9f1472..00000000 --- a/docs/reference/generation/json.md +++ /dev/null @@ -1,105 +0,0 @@ -# JSON structured generation - -Outlines can make any open source model return a JSON object that follows a structure that is specified by the user. This is useful whenever we want the output of the model to be processed by code downstream: code does not understand natural language but rather the structured language it has been programmed to understand. - -There are mostly two reasons why someone would want to get an output formatted as JSON from a LLM: - -1. Parse the answer (e.g. with Pydantic), store it somewhere, return it to a user, etc. -2. Call a function with the result - -Outlines has you covered in both cases! Indeed, to define the structure of the JSON you want the model to follow you can either provide a Pydantic model, or a function. No need to duplicate code! - -## Using Pydantic - -Outlines can infer the structure of the output from a Pydantic model. The result is an instance of the model that contains the values returned by the LLM: - -```python -from pydantic import BaseModel - -from outlines import models, generate - - -class User(BaseModel): - name: str - last_name: str - id: int - - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.json(model, User) -result = generator( - "Create a user profile with the fields name, last_name and id" -) -print(result) -# User(name="John", last_name="Doe", id=11) -``` - -!!! Note "JSON and whitespaces" - - By default Outlines prevents the model from generating json with syntactic newlines, tabs, or multiple spaces. The default `whitespace_pattern` is `r"[ ]?"`. Small models tend to enter an infinite repetition loop if the `whitespace_pattern` allows infinite spacing. If you would like to allow the model to generate multiple tabs, newlines, and spaces, you can set the whitespace pattern as follows: - - ```python - generator = generate.json(model, User, whitespace_pattern=r"[\n\t ]*") - ``` - -!!! Note "Performance" - - `generation.json` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate several times with the same schema make sure that you only call `generate.json` once. - -!!! Tip "Custom types" - - Outlines provides [custom Pydantic types](types.md) so you do not have to write regular expressions for common types, such as phone numbers or zip codes. - -## Using a JSON Schema - -Instead of a Pydantic model you can pass a string that represents a [JSON Schema](https://json-schema.org/) specification to `generate.json`: - -```python -from pydantic import BaseModel - -from outlines import models -from outlines import generate - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") - -schema = """ -{ - "title": "User", - "type": "object", - "properties": { - "name": {"type": "string"}, - "last_name": {"type": "string"}, - "id": {"type": "integer"} - }, - "required": ["name", "last_name", "id"] -} -""" - -generator = generate.json(model, schema) -result = generator( - "Create a user profile with the fields name, last_name and id" -) -print(result) -# User(name="John", last_name="Doe", id=11) -``` - -## From a function's signature - -Outlines can infer the structure of the output from the signature of a function. The result is a dictionary, and can be passed directly to the function using the usual dictionary expansion syntax `**`: - -```python -from outlines import models -from outlines import generate - -def add(a: int, b: int): - return a + b - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.json(model, add) -result = generator("Return two integers named a and b respectively. a is odd and b even.") - -print(add(**result)) -# 3 -``` - -A great advantage of passing functions directly to specify the structure is that the structure of the LLM will change with the function's definition. No need to change the code at several places! diff --git a/docs/reference/generation/regex.md b/docs/reference/generation/regex.md deleted file mode 100644 index 23df3556..00000000 --- a/docs/reference/generation/regex.md +++ /dev/null @@ -1,27 +0,0 @@ -# Regular expressions - -Outlines can guarantee that the text generated by the LLM will be valid to a regular expression: - -```python -from outlines import models, generate - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") - -generator = generate.regex( - model, - r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)", -) - -prompt = "What is the IP address of the Google DNS servers? " -answer = generator(prompt, max_tokens=30) - -print(answer) -# What is the IP address of the Google DNS servers? -# 2.2.6.1 -``` - -If you find yourself using `generate.regex` to restrict the answers' type you can take a look at [type-structured generation](types.md) instead. - -!!! Note "Performance" - - `generate.regex` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate several times using the same regular expression make sure that you only call `generate.regex` once. diff --git a/docs/reference/generation/structured_generation_explanation.md b/docs/reference/generation/structured_generation_explanation.md deleted file mode 100644 index aa27a7a8..00000000 --- a/docs/reference/generation/structured_generation_explanation.md +++ /dev/null @@ -1,54 +0,0 @@ -# How does Outlines work? - - -Language models generate text token by token, using the previous token sequence as input and sampled logits as output. This document explains the structured generation process, where only legal tokens are considered for the next step based on a predefined automata, e.g. a regex-defined [finite-state machine](https://en.wikipedia.org/wiki/Finite-state_machine) (FSM) or [Lark](https://lark-parser.readthedocs.io/en/stable/) grammar.` - - -## Worked Example - -Let's consider a worked example with a pattern for whole and decimal numbers: - -`^\d*(\.\d+)?$`. - -### Creating Automata - -The pattern is first converted into an automata. Below is a brief explanation of the automata conversion and its representation. - -**Automata Diagram:** - -```mermaid -graph LR - node0("1-9") --> node1("1-9") - node1 --> node1 - node1 --> nodeEND{{END}} - node1 --> nodePeriod(".") - nodePeriod --> node2("1-9") - node2 --> node2 - node2 --> nodeEND{{END}} -``` - -### Generating a Token - -Let's assume that we're in the middle of generation, and so far "748" has been generated. Here is the automata with the current state highlighted in green, with the legal next characters being another number (1-9), a dot (.), or end of sequence. - -```mermaid -graph LR - node0("1-9") --> node1("1-9") - node1 --> node1 - node1 --> nodeEND{{END}} - node1 --> nodePeriod(".") - nodePeriod --> node2("1-9") - node2 --> node2 - node2 --> nodeEND{{END}} - - style node1 fill:#090 -``` - -Generating a token requires the following steps: - -- Feed the previous input sequence ("748") into the language model. -- Language model runs a forward pass and produces token logits. -- Outlines logits processor sets the probability of illegal tokens to 0%. -- A token is sampled from the set of legal tokens. - -![Generation and Logits Processing Flow Chart](../../assets/images/logits_processing_diagram.svg) diff --git a/docs/reference/generation/types.md b/docs/reference/generation/types.md deleted file mode 100644 index 5b83a591..00000000 --- a/docs/reference/generation/types.md +++ /dev/null @@ -1,83 +0,0 @@ -# Custom types - -Outlines provides custom Pydantic types so you can focus on your use case rather than on writing regular expressions: - -| Category | Type | Import | Description | -|:--------:|:----:|:-------|:------------| -| ISBN | 10 & 13 | `outlines.types.ISBN` | There is no guarantee that the [check digit][wiki-isbn] will be correct | -| Airport | IATA | `outlines.types.airports.IATA` | Valid [airport IATA codes][wiki-airport-iata] | -| Country | alpha-2 code | `outlines.types.airports.Alpha2` | Valid [country alpha-2 codes][wiki-country-alpha-2] | -| | alpha-3 code | `outlines.types.countries.Alpha3` | Valid [country alpha-3 codes][wiki-country-alpha-3] | -| | numeric code | `outlines.types.countries.Numeric` | Valid [country numeric codes][wiki-country-numeric] | -| | name | `outlines.types.countries.Name` | Valid country names | -| | flag | `outlines.types.countries.Flag` | Valid flag emojis | -| | email | `outlines.types.Email` | Valid email address | - -Some types require localization. We currently only support US types, but please don't hesitate to create localized versions of the different types and open a Pull Request. Localized types are specified using `types.locale` in the following way: - -```python -from outlines import types - -types.locale("us").ZipCode -types.locale("us").PhoneNumber -``` - -Here are the localized types that are currently available: - -| Category | Locale | Import | Description | -|:--------:|:----:|:-------|:------------| -| Zip code | US | `ZipCode` | Generate US Zip(+4) codes | -| Phone number | US | `PhoneNumber` | Generate valid US phone numbers | - - -You can use these types in Pydantic schemas for JSON-structured generation: - -```python -from pydantic import BaseModel - -from outlines import models, generate, types - -# Specify the locale for types -locale = types.locale("us") - -class Client(BaseModel): - name: str - phone_number: locale.PhoneNumber - zip_code: locale.ZipCode - - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.json(model, Client) -result = generator( - "Create a client profile with the fields name, phone_number and zip_code" -) -print(result) -# name='Tommy' phone_number='129-896-5501' zip_code='50766' -``` - -Or simply with `outlines.generate.format`: - -```python -from pydantic import BaseModel - -from outlines import models, generate, types - - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct") -generator = generate.format(model, types.locale("us").PhoneNumber) -result = generator( - "Return a US Phone number: " -) -print(result) -# 334-253-2630 -``` - - -We plan on adding many more custom types. If you have found yourself writing regular expressions to generate fields of a given type, or if you could benefit from more specific types don't hesite to [submit a PR](https://github.com/outlines-dev/outlines/pulls) or [open an issue](https://github.com/outlines-dev/outlines/issues/new/choose). - - -[wiki-isbn]: https://en.wikipedia.org/wiki/ISBN#Check_digits -[wiki-airport-iata]: https://en.wikipedia.org/wiki/IATA_airport_code -[wiki-country-alpha-2]: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2 -[wiki-country-alpha-3]: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3 -[wiki-country-numeric]: https://en.wikipedia.org/wiki/ISO_3166-1_numeric diff --git a/docs/reference/index.md b/docs/reference/index.md deleted file mode 100644 index a5357fd8..00000000 --- a/docs/reference/index.md +++ /dev/null @@ -1,15 +0,0 @@ -# Reference - -## Structured generation - -While LLM capabilities are increasingly impressive, we can make their output more reliable by steering the generation. Outlines thus offers mechanisms to specify high level constraints on text completions by generative language models. - -Stopping sequence -By default, language models stop generating tokens after and token was generated, or after a set maximum number of tokens. Their output can be verbose, and for practical purposes it is often necessary to stop the generation after a given sequence has been found instead. You can use the stop_at keyword argument when calling the model with a prompt: - -```python -import outlines.models as models - -complete = models.openai("gpt-3.5-turbo") -expert = complete("Name an expert in quantum gravity.", stop_at=["\n", "."]) -``` diff --git a/docs/reference/models/exllamav2.md b/docs/reference/models/exllamav2.md deleted file mode 100644 index afe54211..00000000 --- a/docs/reference/models/exllamav2.md +++ /dev/null @@ -1,7 +0,0 @@ -# ExllamaV2 - -```bash -pip install exllamav2 transformers torch -``` - -*Coming soon* diff --git a/docs/reference/models/llamacpp.md b/docs/reference/models/llamacpp.md deleted file mode 100644 index a84f0c18..00000000 --- a/docs/reference/models/llamacpp.md +++ /dev/null @@ -1,226 +0,0 @@ -# Llama.cpp - -Outlines provides an integration with [Llama.cpp](https://github.com/ggerganov/llama.cpp) using the [llama-cpp-python library][llamacpp]. Llamacpp allows to run quantized models on machines with limited compute. - -!!! Note "Installation" - - You need to install the `llama-cpp-python` library to use the llama.cpp integration. See the [installation section](#installation) for instructions to install `llama-cpp-python` with CUDA, Metal, ROCm and other backends. - -## Load the model - -You can initialize the model by passing the name of the repository on the HuggingFace Hub, and the filenames (or glob pattern): - -```python -from outlines import models - -model = models.llamacpp("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf") -``` - -This will download the model files to the hub cache folder and load the weights in memory. - -You can also initialize the model by passing the path to the weights on your machine. Assuming [Phi2's weights](https://huggingface.co/TheBloke/phi-2-GGUF) are in the current directory: - -```python -from outlines import models -from llama_cpp import Llama - -llm = Llama("./phi-2.Q4_K_M.gguf") -model = models.LlamaCpp(llm) -``` - -If you need more control, you can pass the same keyword arguments to the model as you would pass in the [llama-ccp-library][llamacpp]: - -```python -from outlines import models - -model = models.llamacpp( - "TheBloke/phi-2-GGUF", - "phi-2.Q4_K_M.gguf" - n_ctx=512, # to set the context length value -) -``` - -**Main parameters:** - -| Parameters | Type | Description | Default | -|------------|------|-------------|---------| -| `n_gpu_layers`| `int` | Number of layers to offload to GPU. If -1, all layers are offloaded | `0` | -| `split_mode` | `int` | How to split the model across GPUs. `1` for layer-wise split, `2` for row-wise split | `1` | -| `main_gpu` | `int` | Main GPU | `0` | -| `tensor_split` | `Optional[List[float]]` | How split tensors should be distributed accross GPUs. If `None` the model is not split. | `None` | -| `n_ctx` | `int` | Text context. Inference from the model if set to `0` | `0` | -| `n_threads` | `Optional[int]` | Number of threads to use for generation. All available threads if set to `None`.| `None` | -| `verbose` | `bool` | Print verbose outputs to `stderr` | `False` | - -See the [llama-cpp-python documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__) for the full list of parameters. - -### Load the model on GPU - -!!! Note - - [Make sure](#cuda) that you installed `llama-cpp-python` with GPU support. - - To load the model on GPU, pass `n_gpu_layers=-1`: - -```python -from outlines import models - -model = models.llamacpp( - "TheBloke/phi-2-GGUF", - "phi-2.Q4_K_M.gguf" - n_gpu_layers=-1, # to use GPU acceleration -) -``` - -This also works with generators built with `generate.regex`, `generate.json`, `generate.cfg`, `generate.format` and `generate.choice`. - -### Load LoRA adapters - -You can load LoRA adapters dynamically: - -```python -from outlines import models, generate - -model = models.llamacpp("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf") -generator = generate.text(model) -answer_1 = generator("prompt") - -model.load_lora("./path/to/adapter.gguf") -answer_2 = generator("prompt") -``` - -To load another adapter you need to re-initialize the model. Otherwise the adapter will be added on top of the previous one: - -```python -from outlines import models - -model = models.llamacpp("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf") -model.load_lora("./path/to/adapter1.gguf") # Load first adapter - -model = models.llamacpp("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf") -model.load_lora("./path/to/adapter2.gguf") # Load second adapter -``` - -## Generate text - -In addition to the parameters described in the [text generation section](../text.md) you can pass extra keyword arguments, for instance to set sampling parameters not exposed in Outlines' public API: - -```python -from outlines import models, generate - - -model = models.llamacpp("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf") -generator = generate.text(model) - -answer = generator("A prompt", presence_penalty=0.8) -``` - -**Extra keyword arguments:** - -The value of the keyword arguments you pass to the generator suspersede the values set when initializing the sampler or generator. All extra sampling methods and repetition penalties are disabled by default. - -| Parameters | Type | Description | Default | -|------------|------|-------------|---------| -| `suffix` | `Optional[str]` | A suffix to append to the generated text. If `None` no suffix is added. | `None` | -| `echo` | `bool` | Whether to preprend the prompt to the completion. | `False` | -| `seed` | `int` | The random seed to use for sampling. | `None` | -| `max_tokens` | `Optional[int]` | The maximum number of tokens to generate. If `None` the maximum number of tokens depends on `n_ctx`. | `16` | -| `frequence_penalty` | `float` | The penalty to apply to tokens based on their frequency in the past 64 tokens. | `0.0` | -| `presence_penalty` | `float` | The penalty to apply to tokens based on their presence in the past 64 tokens. | `0.0` | -| `repeat_penalty` | `float` | The penalty to apply to repeated tokens in the past 64 tokens. | `1.` | -| `stopping_criteria` | `Optional[StoppingCriteriaList]` | A list of stopping criteria to use. | `None` -| `logits_processor` | `Optional[LogitsProcessorList]` | A list of logits processors to use. The logits processor used for structured generation will be added to this list. | `None` -| `temperature` | `float` | The temperature to use for sampling | `1.0` | -| `top_p` | `float` | The top-p value to use for [nucleus sampling][degeneration]. | `1.` | -| `min_p` | `float` | The min-p value to use for [minimum-p sampling][minimum-p]. | `0.` | -| `typical_p` | `float` | The p value to use for [locally typical sampling][locally-typical]. | `1.0` | -| `stop` | `Optional[Union[str, List[str]]]` | A list of strings that stop generation when encountered. | `[]` | -| `top_k` | `int` | The top-k value used for [top-k sampling][top-k]. Negative value to consider all logit values. | `-1.` | -| `tfs_z` | `float` | The [tail-free sampling][tail-free] parameter. | `1.0` | -| `mirostat_mode` | `int` | The [mirostat sampling][mirostat] mode. | `0` | -| `mirostat_tau` | `float` | The target cross-entropy for [mirostat sampling][mirostat].| `5.0` | -| `mirostat_eta` | `float` | The learning rate used to update `mu` in [mirostat sampling][mirostat]. | `0.1` | - -See the [llama-cpp-python documentation][llama-cpp-python-call] for the full and up-to-date list of parameters and the [llama.cpp code][llama-cpp-sampling-params] for the default values of other -sampling parameters. - -### Streaming - - -## Installation - -You need to install the `llama-cpp-python` library to use the llama.cpp integration. - -### CPU - -For a *CPU-only* installation run: - -```bash -pip install llama-cpp-python -``` - -!!! Warning - - Do not run this command if you want support for BLAS, Metal or CUDA. Follow the instructions below instead. - -### CUDA - -```bash -CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python -``` - -It is also possible to install pre-built wheels with CUDA support (Python 3.10 and above): - -```bash -pip install llama-cpp-python \ - --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/ -``` - -Where `` is one of the following, depending on the version of CUDA installed on your system: - -- `cu121` for CUDA 12.1 -- `cu122` for CUDA 12.2 -- `cu123` CUDA 12.3 - -### Metal - -```bash -CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python -``` - -It is also possible to install pre-build wheels with Metal support (Python 3.10 or above, MacOS 11.0 and above): - -```bash -pip install llama-cpp-python \ - --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal -``` - -### OpenBLAS - -```bash -CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python -``` - -### Other backend - -`llama.cpp` supports many other backends. Refer to the [llama.cpp documentation][llama-cpp-python-install] to use the following backends: - -- CLBast (OpenCL) -- hipBLAS (ROCm) -- Vulkan -- Kompute -- SYCL - - - - -[llamacpp]: https://github.com/abetlen/llama-cpp-python -[llama-cpp-python-call]: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ -[llama-cpp-python-install]: https://github.com/abetlen/llama-cpp-python/tree/08b16afe11e7b42adec2fed0a781123383476045?tab=readme-ov-file#supported-backends -[llama-cpp-sampling-params]: https://github.com/ggerganov/llama.cpp/blob/e11a8999b5690f810c2c99c14347f0834e68c524/common/sampling.h#L22 -[mirostat]: https://arxiv.org/abs/2007.14966 -[degeneration]: https://arxiv.org/abs/1904.09751 -[top-k]: https://arxiv.org/abs/1805.04833 -[minimum-p]: https://github.com/ggerganov/llama.cpp/pull/3841 -[locally-typical]: https://arxiv.org/abs/2202.00666 -[tail-free]: https://www.trentonbricken.com/Tail-Free-Sampling diff --git a/docs/reference/models/mlxlm.md b/docs/reference/models/mlxlm.md deleted file mode 100644 index cf7bb744..00000000 --- a/docs/reference/models/mlxlm.md +++ /dev/null @@ -1,89 +0,0 @@ -# mlx-lm - -Outlines provides an integration with [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms), allowing models to be run quickly on Apple Silicon via the [mlx](https://ml-explore.github.io/mlx/build/html/index.html) library. - -!!! Note "Installation" - - You need to install the `mlx` and `mlx-lm` libraries on a device which [supports Metal](https://support.apple.com/en-us/102894) to use the mlx-lm integration. - - -## Load the model - -You can initialize the model by passing the name of the repository on the HuggingFace Hub. The official repository for mlx-lm supported models is [mlx-community](https://huggingface.co/mlx-community). - -```python -from outlines import models - -model = models.mlxlm("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit") -``` - -This will download the model files to the hub cache folder and load the weights in memory. - -The arguments `model_config` and `tokenizer_config` are available to modify loading behavior. For example, per the `mlx-lm` [documentation](https://github.com/ml-explore/mlx-examples/tree/main/llms#supported-models), you must set an eos_token for `qwen/Qwen-7B`. In outlines you may do so via - -``` -model = models.mlxlm( - "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit", - tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True}, -) -``` - -**Main parameters:** - -(Subject to change. Table based on [mlx-lm.load docstring](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py#L429)) - -| Parameters | Type | Description | Default | -|--------------------|--------|--------------------------------------------------------------------------------------------------|---------| -| `tokenizer_config` | `dict` | Configuration parameters specifically for the tokenizer. Defaults to an empty dictionary. | `{}` | -| `model_config` | `dict` | Configuration parameters specifically for the model. Defaults to an empty dictionary. | `{}` | -| `adapter_path` | `str` | Path to the LoRA adapters. If provided, applies LoRA layers to the model. | `None` | -| `lazy` | `bool` | If False, evaluate the model parameters to make sure they are loaded in memory before returning. | `False` | - - -## Generate text - -You may generate text using the parameters described in the [text generation documentation](../text.md). - -With the loaded model, you can generate text or perform structured generation, e.g. - -```python -from outlines import models, generate - -model = models.mlxlm("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit") -generator = generate.text(model) - -answer = generator("A prompt", temperature=2.0) -``` - -## Streaming - -You may creating a streaming iterable with minimal changes - -```python -from outlines import models, generate - -model = models.mlxlm("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit") -generator = generate.text(model) - -for token_str in generator.text("A prompt", temperature=2.0): - print(token_str) -``` - -## Structured - -You may perform structured generation with mlxlm to guarantee your output will match a regex pattern, json schema, or lark grammar. - -Example: Phone number generation with pattern `"\\+?[1-9][0-9]{7,14}"`: - -```python -from outlines import models, generate - -model = models.mlxlm("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit") - -phone_number_pattern = "\\+?[1-9][0-9]{7,14}" -generator = generate.regex(model, phone_number_pattern) - -model_output = generator("What's Jennys Number?\n") -print(model_output) -# '8675309' -``` diff --git a/docs/reference/models/models.md b/docs/reference/models/models.md deleted file mode 100644 index 34b5be4c..00000000 --- a/docs/reference/models/models.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -title: Models ---- - -# Models - -Outlines supports generation using a number of inference engines (`outlines.models`). Loading a model using outlines follows a similar interface between inference engines: - -```python -import outlines - -model = outlines.models.transformers("microsoft/Phi-3-mini-128k-instruct") -model = outlines.models.transformers_vision("llava-hf/llava-v1.6-mistral-7b-hf") -model = outlines.models.vllm("microsoft/Phi-3-mini-128k-instruct") -model = outlines.models.llamacpp( - "microsoft/Phi-3-mini-4k-instruct-gguf", "Phi-3-mini-4k-instruct-q4.gguf" -) -model = outlines.models.exllamav2("bartowski/Phi-3-mini-128k-instruct-exl2") -model = outlines.models.mlxlm("mlx-community/Phi-3-mini-4k-instruct-4bit") - -model = outlines.models.openai( - "gpt-4o-mini", - api_key=os.environ["OPENAI_API_KEY"] -) -``` - - -# Feature Matrix -| | [Transformers](transformers.md) | [Transformers Vision](transformers_vision.md) | [vLLM](vllm.md) | [llama.cpp](llamacpp.md) | [ExLlamaV2](exllamav2.md) | [MLXLM](mlxlm.md) | [OpenAI](openai.md)* | -|-------------------|--------------|---------------------|------|-----------|-----------|-------|---------| -| **Device** | | | | | | | | -| Cuda | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | N/A | -| Apple Silicon | βœ… | βœ… | ❌ | βœ… | βœ… | βœ… | N/A | -| x86 / AMD64 | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | N/A | -| **Sampling** | | | | | | | | -| Greedy | βœ… | βœ… | βœ… | βœ…* | βœ… | βœ… | ❌ | -| Multinomial | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | -| Multiple Samples | βœ… | βœ… | | ❌ | | ❌ | βœ… | -| Beam Search | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | ❌ | -| **Generation** | | | | | | | | -| Batch | βœ… | βœ… | βœ… | ❌ | ? | ❌ | ❌ | -| Stream | βœ… | ❌ | ❌ | βœ… | ? | βœ… | ❌ | -| **`outlines.generate`** | | | | | | | | -| Text | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | -| Structured* | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | - - -## Caveats - -- OpenAI doesn't support structured generation due to limitations in their API and server implementation. -- `outlines.generate` ["Structured"](../generation/generation.md) includes methods such as `outlines.generate.regex`, `outlines.generate.json`, `outlines.generate.cfg`, etc. -- MLXLM only supports Apple Silicon. -- llama.cpp greedy sampling available via multinomial with `temperature = 0.0`. diff --git a/docs/reference/models/openai.md b/docs/reference/models/openai.md deleted file mode 100644 index 7f610c17..00000000 --- a/docs/reference/models/openai.md +++ /dev/null @@ -1,170 +0,0 @@ -# OpenAI and compatible APIs - -!!! Installation - - You need to install the `openai` and `tiktoken` libraries to be able to use the OpenAI API in Outlines. - -## OpenAI models - -Outlines supports models available via the OpenAI Chat API, e.g. ChatGPT and GPT-4. You can initialize the model by passing the model name to `outlines.models.openai`: - -```python -from outlines import models - - -model = models.openai("gpt-3.5-turbo") -model = models.openai("gpt-4-turbo") -model = models.openai("gpt-4o") -``` - -Check the [OpenAI documentation](https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4) for an up-to-date list of available models. You can pass any parameter you would pass to `openai.AsyncOpenAI` as keyword arguments: - -```python -import os -from outlines import models - - -model = models.openai( - "gpt-3.5-turbo", - api_key=os.environ["OPENAI_API_KEY"] -) -``` - -The following table enumerates the possible parameters. Refer to the [OpenAI SDK's code](https://github.com/openai/openai-python/blob/54a5911f5215148a0bdeb10e2bcfb84f635a75b9/src/openai/_client.py) for an up-to-date list. - -**Parameters:** - -| **Parameters** | **Type** | **Description** | **Default** | -|----------------|:---------|:----------------|:------------| -| `api_key` | `str` | OpenAI API key. Infered from `OPENAI_API_KEY` if not specified | `None` | -| `organization` | `str` | OpenAI organization id. Infered from `OPENAI_ORG_ID` if not specified | `None` | -| `project` | `str` | OpenAI project id. Infered from `OPENAI_PROJECT_ID` if not specified.| `None` | -| `base_url` | `str | https.URL` | Base URL for the endpoint. Infered from `OPENAI_BASE_URL` if no specified. | `None` | -| `timeout` | `float` | Request timeout.| `NOT_GIVEN` | -| `max_retries` | `int` | Maximum number of retries for failing requests | `2` | -| `default_headers` | `Mapping[str, str]` | Default HTTP headers | `None` | -| `default_query` | `Mapping[str, str]` | Custom parameters added to the HTTP queries | `None` | -| `http_client` | `https.AsyncClient` | User-specified `httpx` client | `None` | - -## Azure OpenAI models - -Outlines also supports Azure OpenAI models: - -```python -from outlines import models - - -model = models.azure_openai( - "azure-deployment-name", - "gpt-3.5-turbo", - api_version="2023-07-01-preview", - azure_endpoint="https://example-endpoint.openai.azure.com", -) -``` - -!!! Question "Why do I need to specify model and deployment name?" - - The model name is needed to load the correct tokenizer for the model. The tokenizer is necessary for structured generation. - - -You can pass any parameter you would pass to `openai.AsyncAzureOpenAI`. You can consult the [OpenAI SDK's code](https://github.com/openai/openai-python/blob/54a5911f5215148a0bdeb10e2bcfb84f635a75b9/src/openai/lib/azure.py) for an up-to-date list. - -**Parameters:** - - -| **Parameters** | **Type** | **Description** | **Default** | -|----------------|:---------|:----------------|:------------| -| `azure_endpoint` | `str` | Azure endpoint, including the resource. Infered from `AZURE_OPENAI_ENDPOINT` if not specified | `None` | -| `api_version` | `str` | API version. Infered from `AZURE_OPENAI_API_KEY` if not specified | `None` | -| `api_key` | `str` | OpenAI API key. Infered from `OPENAI_API_KEY` if not specified | `None` | -| `azure_ad_token` | `str` | Azure active directory token. Inference from `AZURE_OPENAI_AD_TOKEN` if not specified | `None` | -| `azure_ad_token_provider` | `AzureADTokenProvider` | A function that returns an Azure Active Directory token | `None` | -| `organization` | `str` | OpenAI organization id. Infered from `OPENAI_ORG_ID` if not specified | `None` | -| `project` | `str` | OpenAI project id. Infered from `OPENAI_PROJECT_ID` if not specified.| `None` | -| `base_url` | `str | https.URL` | Base URL for the endpoint. Infered from `OPENAI_BASE_URL` if not specified. | `None` | -| `timeout` | `float` | Request timeout.| `NOT_GIVEN` | -| `max_retries` | `int` | Maximum number of retries for failing requests | `2` | -| `default_headers` | `Mapping[str, str]` | Default HTTP headers | `None` | -| `default_query` | `Mapping[str, str]` | Custom parameters added to the HTTP queries | `None` | -| `http_client` | `https.AsyncClient` | User-specified `httpx` client | `None` | - -## Models that follow the OpenAI standard - -Outlines supports models that follow the OpenAI standard. You will need to initialize the OpenAI client properly configured and pass it to `outlines.models.openai` - -```python -import os -from openai import AsyncOpenAI -from outlines import models -from outlines.models.openai import OpenAIConfig - - -client = AsyncOpenAI( - api_key=os.environ.get("PROVIDER_KEY"), - base_url="http://other.provider.server.com" -) -config = OpenAIConfig("model_name") -model = models.openai(client, config) -``` - -!!! Warning - - You need to pass the async client to be able to do batch inference. - -## Advanced configuration - -For more advanced configuration option, such as support proxy, please consult the [OpenAI SDK's documentation](https://github.com/openai/openai-python): - - -```python -from openai import AsyncOpenAI, DefaultHttpxClient -from outlines import models -from outlines.models.openai import OpenAIConfig - - -client = AsyncOpenAI( - base_url="http://my.test.server.example.com:8083", - http_client=DefaultHttpxClient( - proxies="http://my.test.proxy.example.com", - transport=httpx.HTTPTransport(local_address="0.0.0.0"), - ), -) -config = OpenAIConfig("model_name") -model = models.openai(client, config) -``` - -It is possible to specify the values for `seed`, `presence_penalty`, `frequence_penalty`, `top_p` by passing an instance of `OpenAIConfig` when initializing the model: - -```python -from outlines.models.openai import OpenAIConfig -from outlines import models - - -config = OpenAIConfig( - presence_penalty=1., - frequency_penalty=1., - top_p=.95, - seed=0, -) -model = models.openai("gpt-3.5-turbo", config) -``` - -## Monitoring API use - -It is important to be able to track your API usage when working with OpenAI's API. The number of prompt tokens and completion tokens is directly accessible via the model instance: - -```python -from openai import AsyncOpenAI -import outlines.models - - -model = models.openai("gpt-4") - -print(model.prompt_tokens) -# 0 - -print(model.completion_tokens) -# 0 -``` - -These numbers are updated every time you call the model. diff --git a/docs/reference/models/tgi.md b/docs/reference/models/tgi.md deleted file mode 100644 index 3f056810..00000000 --- a/docs/reference/models/tgi.md +++ /dev/null @@ -1,3 +0,0 @@ -# Text-generation-inference (TGI) - -TGI uses Outlines to provide structured generation, see [their documentation](https://huggingface.co/docs/text-generation-inference/en/basic_tutorials/using_guidance). diff --git a/docs/reference/models/transformers.md b/docs/reference/models/transformers.md deleted file mode 100644 index 2a13e28e..00000000 --- a/docs/reference/models/transformers.md +++ /dev/null @@ -1,148 +0,0 @@ -# transformers - - -!!! Installation - - You need to install the `transformer`, `datasets` and `torch` libraries to be able to use these models in Outlines: - - ```bash - pip install torch transformers datasets - ``` - - -Outlines provides an integration with the `torch` implementation of causal models in the [transformers][transformers] library. You can initialize the model by passing its name: - -```python -from outlines import models - -model = models.transformers("microsoft/Phi-3-mini-4k-instruct", device="cuda") -``` - -If you need more fine-grained control you can also initialize the model and tokenizer separately: - - -```python -from transformers import AutoModelForCausalLM, AutoTokenizer -from outlines import models - -llm = AutoModelForCausalLM.from_pretrained("gpt2", output_attentions=True) -tokenizer = AutoTokenizer.from_pretrained("gpt2") -model = models.Transformers(llm, tokenizer) -``` - -# Using Logits Processors - -There are two ways to use Outlines Structured Generation with HuggingFace Transformers: - -1. Use Outlines generation wrapper, `outlines.models.transformers` -2. Use `OutlinesLogitsProcessor` with `transformers.AutoModelForCausalLM` - -Outlines supports a myriad of logits processors for structured generation. In these example, we will use the `RegexLogitsProcessor` which guarantees generated text matches the specified pattern. - -## Using `outlines.models.transformers` - -```python -import outlines - -time_regex_pattern = r"(0?[1-9]|1[0-2]):[0-5]\d\s?(am|pm)?" - -model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct", device="cuda") -generator = outlines.generate.regex(model, time_regex_pattern) - -output = generator("The the best time to visit a dentist is at ") -print(output) -# 2:30 pm -``` - -## Using models initialized via the `transformers` library - -```python -import outlines -import transformers - - -model_uri = "microsoft/Phi-3-mini-4k-instruct" - -outlines_tokenizer = outlines.models.TransformerTokenizer( - transformers.AutoTokenizer.from_pretrained(model_uri) -) -phone_number_logits_processor = outlines.processors.RegexLogitsProcessor( - "\\+?[1-9][0-9]{7,14}", # phone number pattern - outlines_tokenizer, -) - -generator = transformers.pipeline('text-generation', model=model_uri) - -output = generator( - "Jenny gave me her number it's ", - logits_processor=transformers.LogitsProcessorList([phone_number_logits_processor]) -) -print(output) -# [{'generated_text': "Jenny gave me her number it's 2125550182"}] -# not quite 8675309 what we expected, but it is a valid phone number -``` - -[transformers]: https://github.com/huggingface/transformers - - -# Alternative Model Classes - -`outlines.models.transformers` defaults to `transformers.AutoModelForCausalLM`, which is the appropriate class for most standard large language models, including Llama 3, Mistral, Phi-3, etc. - -However other variants with unique behavior can be used as well by passing the appropriate class. - -### Mamba - -[Mamba](https://github.com/state-spaces/mamba) is a transformers alternative which employs memory efficient, linear-time decoding. - -To use Mamba with outlines you must first install the necessary requirements: -``` -pip install causal-conv1d>=1.2.0 mamba-ssm torch transformers -``` - -Then you can either create an Mamba-2 Outlines model via -```python -import outlines - -model = outlines.models.mamba("state-spaces/mamba-2.8b-hf") -``` - -or explicitly with -```python -import outlines -from transformers import MambaForCausalLM - -model = outlines.models.transformers( - "state-spaces/mamba-2.8b-hf", - model_class=MambaForCausalLM -) -``` - - - -Read [`transformers`'s documentation](https://huggingface.co/docs/transformers/en/model_doc/mamba) for more information. - -### Encoder-Decoder Models - -You can use encoder-decoder (seq2seq) models like T5 and BART with Outlines. - -Be cautious with model selection though, some models such as `t5-base` don't include certain characters (`{`) and you may get an error when trying to perform structured generation. - -T5 Example: -```python -import outlines -from transformers import AutoModelForSeq2SeqLM - -model_pile_t5 = models.transformers( - model_name="EleutherAI/pile-t5-large", - model_class=AutoModelForSeq2SeqLM, -) -``` - -Bart Example: -```python -model_bart = models.transformers( - model_name="facebook/bart-large", - model_class=AutoModelForSeq2SeqLM, -) -``` diff --git a/docs/reference/models/transformers_vision.md b/docs/reference/models/transformers_vision.md deleted file mode 100644 index b488bca1..00000000 --- a/docs/reference/models/transformers_vision.md +++ /dev/null @@ -1,115 +0,0 @@ -# Transformers Vision - -Outlines allows seamless use of [vision models](https://huggingface.co/learn/computer-vision-course/en/unit4/multimodal-models/tasks-models-part1). - -`outlines.models.transformers_vision` has shares interfaces with, and is based on [outlines.models.transformers](./transformers.md). - -Tasks supported include - -- image + text -> text -- video + text -> text - - - -## Example: Using [Llava-Next](https://huggingface.co/docs/transformers/en/model_doc/llava_next) Vision Models - -Install dependencies -`pip install torchvision pillow flash-attn` - -Create the model -```python -import outlines -from transformers import LlavaNextForConditionalGeneration - -model = outlines.models.transformers_vision( - "llava-hf/llava-v1.6-mistral-7b-hf", - model_class=LlavaNextForConditionalGeneration, - device="cuda", -) -``` - -Create convenience function to load a `PIL.Image` from URL -```python -from PIL import Image -from io import BytesIO -from urllib.request import urlopen - -def img_from_url(url): - img_byte_stream = BytesIO(urlopen(url).read()) - return Image.open(img_byte_stream).convert("RGB") -``` - -### Describing an image - -```python -description_generator = outlines.generate.text(model) -description_generator( - " detailed description:", - [img_from_url("https://upload.wikimedia.org/wikipedia/commons/2/25/Siam_lilacpoint.jpg")] -) -``` - -> This is a color photograph featuring a Siamese cat with striking blue eyes. The cat has a creamy coat and a light eye color, which is typical for the Siamese breed. Its features include elongated ears, a long, thin tail, and a striking coat pattern. The cat is sitting in an indoor setting, possibly on a cat tower or a similar raised platform, which is covered with a beige fabric, providing a comfortable and soft surface for the cat to rest or perch. The surface of the wall behind the cat appears to be a light-colored stucco or plaster. - -#### Multiple Images - -To include multiple images in your prompt you simply add more `` tokens to the prompt - -```python -image_urls = [ - "https://cdn1.byjus.com/wp-content/uploads/2020/08/ShapeArtboard-1-copy-3.png", # triangle - "https://cdn1.byjus.com/wp-content/uploads/2020/08/ShapeArtboard-1-copy-11.png", # hexagon -] -description_generator = outlines.generate.text(model) -description_generator( - "What shapes are present?", - list(map(img_from_url, image_urls)), -) -``` - -> There are two shapes present. One shape is a hexagon and the other shape is an triangle. ' - - -### Classifying an Image - -```python -pattern = "Mercury|Venus|Earth|Mars|Saturn|Jupiter|Neptune|Uranus|Pluto" -planet_generator = outlines.generate.regex(model, pattern) - -planet_generator( - "What planet is this: ", - [img_from_url("https://upload.wikimedia.org/wikipedia/commons/e/e3/Saturn_from_Cassini_Orbiter_%282004-10-06%29.jpg")] -) -``` - -> Saturn - - -### Extracting Structured Image data - -```python -from pydantic import BaseModel -from typing import List, Optional - -class ImageData(BaseModel): - caption: str - tags_list: List[str] - object_list: List[str] - is_photo: bool - -image_data_generator = outlines.generate.json(model, ImageData) - -image_data_generator( - " detailed JSON metadata:", - [img_from_url("https://upload.wikimedia.org/wikipedia/commons/9/98/Aldrin_Apollo_11_original.jpg")] -) -``` - -> `ImageData(caption='An astronaut on the moon', tags_list=['moon', 'space', 'nasa', 'americanflag'], object_list=['moon', 'moon_surface', 'space_suit', 'americanflag'], is_photo=True)` - - -## Resources - -### Chosing a model -- https://mmbench.opencompass.org.cn/leaderboard -- https://huggingface.co/spaces/WildVision/vision-arena diff --git a/docs/reference/models/vllm.md b/docs/reference/models/vllm.md deleted file mode 100644 index 1380d3d2..00000000 --- a/docs/reference/models/vllm.md +++ /dev/null @@ -1,231 +0,0 @@ -# vLLM - - -!!! Note "Installation" - - You need to install the `vllm` library to use the vLLM integration. See the [installation section](#installation) for instructions to install vLLM for CPU or ROCm. - -## Load the model - -Outlines supports models available via vLLM's offline batched inference interface. You can load a model using: - - -```python -from outlines import models - -model = models.vllm("microsoft/Phi-3-mini-4k-instruct") -``` - -Or alternatively: - -```python -import vllm -from outlines import models - -llm = vllm.LLM("microsoft/Phi-3-mini-4k-instruct") -model = models.VLLM(llm) -``` - - -Models are loaded from the [HuggingFace hub](https://huggingface.co/). - - -!!! Warning "Device" - - The default installation of vLLM only allows to load models on GPU. See the [installation instructions](#installation) to run models on CPU. - - -You can pass any parameter that you would normally pass to `vllm.LLM`, as keyword arguments: - -```python -from outlines import models - -model = models.vllm( - "microsoft/Phi-3-mini-4k-instruct", - trust_remote_code=True, - gpu_memory_utilization=0.7 -) -``` - -**Main parameters:** - -| **Parameters** | **Type** | **Description** | **Default** | -|----------------|:---------|:----------------|:------------| -| `tokenizer_mode`| `str` | "auto" will use the fast tokenizer if available and "slow" will always use the slow tokenizer. | `auto` -| `trust_remote_code`| `bool` | Trust remote code when downloading the model and tokenizer. | `False` | -| `tensor_parallel_size`| `int` | The number of GPUs to use for distributed execution with tensor parallelism.| `1` | -| `dtype`| `str` | The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the model config file. However, if the `torch_dtype` in the config is `float32`, we will use `float16` instead.| `auto` | -| `quantization`| `Optional[str]` | The method used to quantize the model weights. Currently, we support "awq", "gptq" and "squeezellm". If None, we first check the `quantization_config` attribute in the model config file. If that is None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights.| `None` | -| `revision`| `Optional[str]` | The specific model version to use. It can be a branch name, a tag name, or a commit id.| `None` | -| `tokenizer_revision`| `Optional[str]`| The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id.| `None` | -| `gpu_memory_utilization`| `float` | The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of-memory (OOM) errors.| `0.9` | -| `swap_space`| `int` | The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Otherwise, too small values may cause out-of-memory (OOM) errors.| 4 | -| `enforce_eager`| `bool` | Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid.| `False` | -| `enable_lora` | `bool` | Whether to enable loading LoRA adapters | `False` | - -See the [vLLM code](https://github.com/vllm-project/vllm/blob/8f44facdddcf3c704f7d6a2719b6e85efc393449/vllm/entrypoints/llm.py#L72) for a list of all the available parameters. - -### Use quantized models - -vLLM supports AWQ, GPTQ and SqueezeLLM quantized models: - - -```python -from outlines import models - -model = models.vllm("TheBloke/Llama2-7b-Chat-AWQ", quantization="awq") -model = models.vllm("TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", quantization="gptq") -model = models.vllm("https://huggingface.co/squeeze-ai-lab/sq-llama-30b-w4-s5", quantization="squeezellm") -``` - -!!! Warning "Dependencies" - - To use AWQ model you need to install the autoawq library `pip install autoawq`. - - To use GPTQ models you need to install the autoGTPQ and optimum libraries `pip install auto-gptq optimum`. - - -### Multi-GPU usage - -To run multi-GPU inference with vLLM you need to set the `tensor_parallel_size` argument to the number of GPUs available when initializing the model. For instance to run inference on 2 GPUs: - - -```python -from outlines import models - -model = models.vllm( - "microsoft/Phi-3-mini-4k-instruct" - tensor_parallel_size=2 -) -``` - -### Load LoRA adapters - -You can load LoRA adapters and alternate between them dynamically: - -```python -from outlines import models - -model = models.vllm("facebook/opt-350m", enable_lora=True) -model.load_lora("ybelkaa/opt-350m-lora") # Load LoRA adapter -model.load_lora(None) # Unload LoRA adapter -``` - -## Generate text - -In addition to the parameters described in the [text generation section](../text.md) you can pass an instance of `SamplingParams` directly to any generator via the `sampling_params` keyword argument: - -```python -from vllm.sampling_params import SamplingParams -from outlines import models, generate - - -model = models.vllm("microsoft/Phi-3-mini-4k-instruct") -generator = generate.text(model) - -params = SamplingParams(n=2, frequency_penalty=1., min_tokens=2) -answer = generator("A prompt", sampling_params=params) -``` - -This also works with generators built with `generate.regex`, `generate.json`, `generate.cfg`, `generate.format` and `generate.choice`. - -!!! Note - - The values passed via the `SamplingParams` instance supersede the other arguments to the generator or the samplers. - -**`SamplingParams` attributes:** - -| Parameters | Type | Description | Default | -|:-----------|------------------|:-----------------------|---------| -| `n` | `int` | Number of output sequences to return for the given prompt. | `1` | -| `best_of` | `Optional[int]` | Number of output sequences that are generated from the prompt. From these `best_of` sequences, the top `n` sequences are returned. `best_of` must be greater than or equal to `n`. This is treated as the beam width when `use_beam_search` is True. By default, `best_of` is set to `n`. | `None` | -| `presence_penalty` | `float` | Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.| `0.0` | -| `frequency_penalty` | `float` | Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens. | `0.0` -| `repetition_penalty` | `float` | Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens. | `1.0` | -| `temperature` | `float` | Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling. | `1.0` | -| `top_p` | `float` | Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens. | `1.0` | -| `top_k` | `int` | Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens. | `-1` | -| `min_p` |`float` | Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this. | `0.0` | -| `seed` | `Optional[int]` | Random seed to use for the generation. | `None` | -| `use_beam_search` | `bool` | Whether to use beam search instead of sampling. | `False` | -| `length_penalty` | `float` | Float that penalizes sequences based on their length. Used in beam search. | `1.0` | -| `early_stopping` | `Union[bool, str]` | Controls the stopping condition for beam search. It accepts the following values: `True`, where the generation stops as soon as there are `best_of` complete candidates; `False`, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm). | `False` | -| `stop` | `Optional[Union[str, List[str]]]` | List of strings that stop the generation when they are generated. The returned output will not contain the stop strings. | `None` | -| `stop_token_ids` | `Optional[List[int]]` | List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens. | `None` | -| `include_stop_str_in_output` | `bool` | Whether to include the stop strings in output text. Defaults to False. | `False` | -| `ignore_eos` | `bool` | Whether to ignore the EOS token and continue generating tokens after the EOS token is generated. | `False` | -| `max_tokens` | `int` | Maximum number of tokens to generate per output sequence. | `16` | -| `min_tokens` | `int` | Minimum number of tokens to generate per output sequence before EOS or stop_token_ids can be generated | `0` | -| `skip_special_tokens` | `bool` | Whether to skip special tokens in the output. | `True` | -| `spaces_between_special_tokens` | `bool` | Whether to add spaces between special tokens in the output. Defaults to True. | `True` | - -### Streaming - -!!! Warning - - Streaming is not available for the offline vLLM integration. - - -## Installation - -By default the vLLM library is installed with pre-commpiled C++ and CUDA binaries and will only run on GPU: - -```python -pip install vllm -``` - -### CPU - -You need to have the `gcc` compiler installed on your system. Then you will need to install vLLM from source. First clone the repository: - -```bash -git clone https://github.com/vllm-project/vllm.git -cd vllm -``` - -Install the Python packages needed for the installation: - -```bash -pip install --upgrade pip -pip install wheel packaging ninja setuptools>=49.4.0 numpy -pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu -``` - -and finally run: - -```bash -VLLM_TARGET_DEVICE=cpu python setup.py install -``` - -See the [vLLM documentation][vllm-install-cpu] for more details, alternative installation methods (Docker) and performance tips. - -### ROCm - - -You will need to install vLLM from source. First install Pytorch on ROCm: - -```bash -pip install torch==2.2.0.dev20231206+rocm5.7 --index-url https://download.pytorch.org/whl/nightly/rocm5.7 # tested version -``` - -You will then need to install flash attention for ROCm following [these instructions][rocm-flash-attention]. You can then install `xformers=0.0.23` and apply the patches needed to adapt Flash Attention for ROCm: - -```bash -pip install xformers==0.0.23 --no-deps -bash patch_xformers.rocm.sh -``` - -And finally build vLLM: - -```bash -cd vllm -pip install -U -r requirements-rocm.txt -python setup.py install # This may take 5-10 minutes. -``` - -See the [vLLM documentation][vllm-install-rocm] for alternative installation methods (Docker). - - -[vllm-install-cpu]: https://docs.vllm.ai/en/latest/getting_started/cpu-installation.html -[vllm-install-rocm]: https://docs.vllm.ai/en/latest/getting_started/amd-installation.html -[rocm-flash-attention]: https://github.com/ROCm/flash-attention/tree/flash_attention_for_rocm#amd-gpurocm-support diff --git a/docs/reference/prompting.md b/docs/reference/prompting.md deleted file mode 100644 index 8ea9c024..00000000 --- a/docs/reference/prompting.md +++ /dev/null @@ -1,403 +0,0 @@ -# Prompt templating - -Outlines provides a powerful domain-specific language to write and manage -prompts, via what we call *prompt functions*. Prompt functions are Python -functions that contain a template for the prompt in their docstring, and their -arguments correspond to the variables used in the prompt. When called, a prompt -function returns the template rendered with the values of the arguments. - -The aim of prompt functions is to solve several recurrent problems with prompting: - -1. **Building complex prompts quickly leads to messy code.** This problem has - already been solved in the web development community by using templating, so - why not use it here? -2. **Composing prompts is difficult.** Why not just compose functions? -3. **Separating prompts from code.** Encapsulation in functions allows a clean - separation between prompts and code. Moreover, like any function, prompt - functions can be imported from other modules. - -Outlines uses the [Jinja templating -engine](https://jinja.palletsprojects.com/en/3.1.x/) to render prompts, which -allows to easily compose complex prompts. - -!!! warning "Prompt rendering" - - Prompt functions are opinionated when it comes to prompt rendering. These opinions are meant to avoid common prompting errors, but can have unintended consequences if you are doing something unusual. We advise to always print the prompt before using it. You can also [read the - reference](#formatting-conventions) section if you want to know more. - -## Your first prompt - -The following snippet showcases a very simple prompt. The variables between -curly brackets `{{ }}` are placeholders for the values of the arguments you -will pass to the prompt function. - -=== "Code" - - ```python - import outlines - - @outlines.prompt - def greetings(name, question): - """Hello, {{ name }}! - {{ question }} - """ - - prompt = greetings("user", "How are you?") - print(prompt) - ``` - -=== "Output" - - ```text - Hello, user! - How are you? - ``` - -If a variable is missing in the function's arguments, Jinja2 will throw an `UndefinedError` exception: - -=== "Code" - - ```python - import outlines - - @outlines.prompt - def greetings(name): - """Hello, {{ surname }}!""" - - prompt = greetings("user") - ``` - -=== "Output" - - ```text - Traceback (most recent call last): - File "", line 9, in - File "/home/remi/projects/normal/outlines/outlines/prompts.py", line 38, in __call__ - return render(self.template, **bound_arguments.arguments) - File "/home/remi/projects/normal/outlines/outlines/prompts.py", line 213, in render - return jinja_template.render(**values) - File "/home/remi/micromamba/envs/outlines/lib/python3.9/site-packages/jinja2/environment.py", line 1301, in render - self.environment.handle_exception() - File "/home/remi/micromamba/envs/outlines/lib/python3.9/site-packages/jinja2/environment.py", line 936, in handle_exception - raise rewrite_traceback_stack(source=source) - File "

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