code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
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_fr... | Extract a function object from a downloaded file. | extract_function_from_file | python | dottxt-ai/outlines | outlines/function.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/function.py | Apache-2.0 |
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() | Read grammar file from default grammar path | read_grammar | python | dottxt-ai/outlines | outlines/grammars.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/grammars.py | Apache-2.0 |
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,... | 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 repr... | __call__ | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
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
----------
... | 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... | __call__ | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
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, ... | 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`.
| keep_top_k_logits | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
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 w... | 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
... | keep_top_p_logits | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
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 tempe... | Build a function that rescales the token probabilities exponentially.
Parameters
----------
temperature
The value by which we rescale the logits.
| rescale_logits | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
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_log... | 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... | __call__ | python | dottxt-ai/outlines | outlines/samplers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/samplers.py | Apache-2.0 |
def __call__(self, *args, **kwargs) -> str:
"""Render and return the template.
Returns
-------
The rendered template as a Python ``str``.
"""
if self.signature is not None:
bound_arguments = self.signature.bind(*args, **kwargs)
bound_arguments.ap... | Render and return the template.
Returns
-------
The rendered template as a Python ``str``.
| __call__ | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
def from_file(cls, path: Path, filters: Dict[str, Callable] = {}):
"""Create a `Template` instance from a file containing a Jinja template.
Note: This method does not allow to include and inheritance to reference files
that are outside the folder or subfolders of the file given to `from_file`.
... | Create a `Template` instance from a file containing a Jinja template.
Note: This method does not allow to include and inheritance to reference files
that are outside the folder or subfolders of the file given to `from_file`.
Parameters
----------
path : Path
The pat... | from_file | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
def prompt(
fn: Optional[Callable] = None,
filters: Dict[str, Callable] = {},
) -> Callable:
"""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 `r... | 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.
```pycon
>>> import outlines
>>>
>>> @o... | prompt | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
def create_jinja_env(
loader: Optional[jinja2.BaseLoader], filters: Dict[str, Callable]
) -> jinja2.Environment:
"""Create a new Jinja environment.
The Jinja environment is loaded with a set of pre-defined filters:
- `name`: get the name of a function
- `description`: get a function's docstring
... | Create a new Jinja environment.
The Jinja environment is loaded with a set of pre-defined filters:
- `name`: get the name of a function
- `description`: get a function's docstring
- `source`: get a function's source code
- `signature`: get a function's signature
- `args`: get a function's argum... | create_jinja_env | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
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 p... | Returns the arguments of a function with annotations and default values if provided. | get_fn_args | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
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 =... | Returns the first line of a callable's docstring. | get_fn_description | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
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_sear... | Return the source code of a callable. | get_fn_source | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
def get_schema_pydantic(model: Type[pydantic.BaseModel]):
"""Return the schema of a Pydantic model."""
if not isinstance(model, type(pydantic.BaseModel)):
raise TypeError("The `schema` filter only applies to Pydantic models.")
if hasattr(model, "model_json_schema"):
def_key = "$defs"
... | Return the schema of a Pydantic model. | get_schema_pydantic | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
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.
"""
... | 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.
| parse_pydantic_schema | python | dottxt-ai/outlines | outlines/templates.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/templates.py | Apache-2.0 |
def __init__(self, tokenizer: "Tokenizer"):
"""Initialize the generation guide.
model
The logit generator used to generate the next token.
"""
self.eos_token_id = tokenizer.eos_token_id
self.vocabulary = tokenizer.vocabulary.values() | Initialize the generation guide.
model
The logit generator used to generate the next token.
| __init__ | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def __init__(self, cfg_string: str, tokenizer):
"""
Construct the PartialLark parser and set the empty initial_state (PartialParserState)
"""
warnings.warn(
"Outlines' public *community-contributed* CFG structured generation is experimental. "
"Please review https... |
Construct the PartialLark parser and set the empty initial_state (PartialParserState)
| __init__ | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def get_next_instruction(self, state: CFGState) -> Instruction:
"""Return the next instruction for guided generation.
Current lazy approach:
- For each token in the vocabulary
- create a copy of the parsers state
- add the tokens to the parsers input text
- if vali... | Return the next instruction for guided generation.
Current lazy approach:
- For each token in the vocabulary
- create a copy of the parsers state
- add the tokens to the parsers input text
- if valid, add token to returned tokens
Further refinements are necessary ... | get_next_instruction | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def iter_valid_token_ids(
self, state: CFGState, candidate_token_ids: list
) -> Generator[int, None, None]:
"""
Iterate over the given token_ids and yield those that are valid for the current parser state.
Parameters
----------
parser_state
The current st... |
Iterate over the given token_ids and yield those that are valid for the current parser state.
Parameters
----------
parser_state
The current state of the parser, or None if complete.
token_ids
The list of token ids to check for validity.
Yields
... | iter_valid_token_ids | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def get_next_state(self, state: CFGState, token_id: int) -> CFGState:
"""
Update the state of the guide.
Decode the token_id, and calculate the new parser_state with the token applied.
Parameters
----------
state
The guides current PartialParserState, or None... |
Update the state of the guide.
Decode the token_id, and calculate the new parser_state with the token applied.
Parameters
----------
state
The guides current PartialParserState, or None if complete
token_id
The id of the token that was just gener... | get_next_state | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def _get_parser_state_token_applied(
self, state: CFGState, token_id: int
) -> PartialParserState:
"""
Don't mutate `parser_state`, copy to protect
Get the token string
- if first token in generation: tokenizer.decode (no leading whitespace)
- else: normalized (w... |
Don't mutate `parser_state`, copy to protect
Get the token string
- if first token in generation: tokenizer.decode (no leading whitespace)
- else: normalized (with possibly leading whitespace)
Don't allow empty ("") tokens, raise ValueError
| _get_parser_state_token_applied | python | dottxt-ai/outlines | outlines/fsm/guide.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/guide.py | Apache-2.0 |
def convert_json_schema_to_str(json_schema: Union[dict, str, Type[BaseModel]]) -> str:
"""Convert a JSON schema to a string.
Parameters
----------
json_schema
The JSON schema.
Returns
-------
str
The JSON schema converted to a string.
Raises
------
ValueError
... | Convert a JSON schema to a string.
Parameters
----------
json_schema
The JSON schema.
Returns
-------
str
The JSON schema converted to a string.
Raises
------
ValueError
If the schema is not a dictionary, a string or a Pydantic class.
| convert_json_schema_to_str | python | dottxt-ai/outlines | outlines/fsm/json_schema.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/json_schema.py | Apache-2.0 |
def get_schema_from_signature(fn: Callable) -> dict:
"""Turn a function signature into a JSON schema.
Every JSON object valid to the output JSON Schema can be passed
to `fn` using the ** unpacking syntax.
"""
signature = inspect.signature(fn)
arguments = {}
for name, arg in signature.param... | Turn a function signature into a JSON schema.
Every JSON object valid to the output JSON Schema can be passed
to `fn` using the ** unpacking syntax.
| get_schema_from_signature | python | dottxt-ai/outlines | outlines/fsm/json_schema.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/json_schema.py | Apache-2.0 |
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 s... | Compute state transition and symbols-to-states maps. | _compute_maps | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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 ter... | 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
... | _compute_termset_fsm_info | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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.
... |
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.
| feed_token_no_stack | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
def accepts(self):
"""
Adapted from https://github.com/lark-parser/lark/blob/be542c2ff6d968817df019b8bf03f37b3111c08c/lark/parsers/lalr_interactive_parser.py#L95
Returns the set of possible tokens that will advance the parser into a new valid state.
"""
accepts = set()
co... |
Adapted from https://github.com/lark-parser/lark/blob/be542c2ff6d968817df019b8bf03f37b3111c08c/lark/parsers/lalr_interactive_parser.py#L95
Returns the set of possible tokens that will advance the parser into a new valid state.
| accepts | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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[Partia... | Get the possible terminal symbols for an FSM state sequence. | get_terminals_info | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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(l... | Determine an FSM match over `text` starting at `pos` and continuing `last_fsm_state_seq`. | match | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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... | Construct a ``dict`` mapping terminal symbol names to their finite state machines. | terminals_to_fsms | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
def fsm_union(
fsms: Sequence[FSM],
) -> Tuple[FSM, Dict[int, Tuple[Set[Tuple[int, int]], Set[int], Dict[int, Set[int]]]]]:
"""Construct an FSM representing the union of the FSMs in `fsms`.
This is an updated version of `interegular.fsm.FSM.union` made to return an
extra map of component FSMs to the se... | Construct an FSM representing the union of the FSMs in `fsms`.
This is an updated version of `interegular.fsm.FSM.union` made to return an
extra map of component FSMs to the sets of state transitions that
correspond to them in the new FSM.
| fsm_union | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
def get_sub_fsms_from_seq(
state_seq: Sequence[int],
fsms_to_trans_finals: Dict[
int, Tuple[Set[Tuple[int, int]], Set[int], Dict[int, Set[int]]]
],
) -> Generator[Tuple[int, bool, bool], None, None]:
"""Get the indices of the sub-FSMs in `fsm` that could have matched the state sequence `state_se... | Get the indices of the sub-FSMs in `fsm` that could have matched the state sequence `state_seq`.
Parameters
----------
state_seq
A state sequence.
fsms_to_trans_finals
A map from FSM indices to tuples containing sets of their state transitions
and sets of the final/accept states... | get_sub_fsms_from_seq | python | dottxt-ai/outlines | outlines/fsm/parsing.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/fsm/parsing.py | Apache-2.0 |
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 sequenc... | 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 g... | get_generated_token_ids | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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.
sto... | 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... | is_stop_sequence_found | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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... | 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.
| strip_stop_sequences | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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... | 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
----... | __call__ | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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... | 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
----------
... | stream | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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) | Apply formatting to every string in a completion. | _format | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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."""
genera... | Generate text from a prompt of list of prompts. | __call__ | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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."""
... | Return a text generator from a prompt or a list of prompts. | stream | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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 ... |
Generate text from a prompt of list of prompts.
Media: A URI to construct media or media object itself. Used as AutoProcessor argument.
| __call__ | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
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(p... |
Prepare media as PIL.Image and ensure for every prompt str there is one List[PIL.Image]
| _validate_prompt_media_types | python | dottxt-ai/outlines | outlines/generate/api.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/api.py | Apache-2.0 |
def cfg(
model, cfg_str: str, sampler: Sampler = multinomial()
) -> SequenceGeneratorAdapter:
"""Generate text in the language of a Context-Free Grammar
Arguments
---------
model:
An `outlines.model` instance.
sampler:
The sampling algorithm to use to generate token ids from the... | Generate text in the language of a Context-Free Grammar
Arguments
---------
model:
An `outlines.model` instance.
sampler:
The sampling algorithm to use to generate token ids from the logits
distribution.
Returns
-------
A `SequenceGeneratorAdapter` instance that gen... | cfg | python | dottxt-ai/outlines | outlines/generate/cfg.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/cfg.py | Apache-2.0 |
def choice_openai(
model: OpenAI, choices: List[str], sampler: Sampler = multinomial()
) -> Callable:
"""
Call OpenAI API with response_format of a dict:
{"result": <one of choices>}
"""
choices_schema = pyjson.dumps(
{
"type": "object",
"properties": {"result": ... |
Call OpenAI API with response_format of a dict:
{"result": <one of choices>}
| choice_openai | python | dottxt-ai/outlines | outlines/generate/choice.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/choice.py | Apache-2.0 |
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.
pyt... | 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.
... | format | python | dottxt-ai/outlines | outlines/generate/format.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/format.py | Apache-2.0 |
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 toke... | 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 wei... | sequence_generator | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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
-------
... |
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.
| get_next_fsm_states | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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 s... | 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 id... | get_allowed_tokens | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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 t... | 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 toke... | update_token_ids | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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 nee... | 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.
| update_attention_masks | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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
... | 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 batc... | reorder_kv_cache | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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 ten... | 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
... | bias_logits | python | dottxt-ai/outlines | outlines/generate/generator.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/generator.py | Apache-2.0 |
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
----------
... |
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 s... | json | python | dottxt-ai/outlines | outlines/generate/json.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/json.py | Apache-2.0 |
def regex(model, regex_str: str | Regex, 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 ... | 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 a... | regex | python | dottxt-ai/outlines | outlines/generate/regex.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/generate/regex.py | Apache-2.0 |
def prepare_generation_parameters(
self,
prompts: Union[str, List[str]],
generation_parameters: GenerationParameters,
sampling_parameters: SamplingParameters,
structure_logits_processor,
**exllamav2_params: Unpack[ExllamaV2Params],
) -> Tuple[ExllamaV2Params, Union[st... | Prepare the generation parameters.
`exllamav2` uses different default values
| prepare_generation_parameters | python | dottxt-ai/outlines | outlines/models/exllamav2.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/exllamav2.py | Apache-2.0 |
def reformat_output(
self, output: Union[str, List[str]], sampling_parameters: SamplingParameters
):
"""
The purpose of this function is to reformat the output from exllamav2's output format to outline's output format.
For exllamav2, it mainly accepts only a list or a string(they al... |
The purpose of this function is to reformat the output from exllamav2's output format to outline's output format.
For exllamav2, it mainly accepts only a list or a string(they also do cfg sampling with tuples but we will ignore this for now).
The exllamav2's logic is:
1. If the prompt... | reformat_output | python | dottxt-ai/outlines | outlines/models/exllamav2.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/exllamav2.py | Apache-2.0 |
def exl2(
model_path: str,
draft_model_path: Optional[str] = None,
max_seq_len: Optional[int] = None,
cache_q4: bool = False,
paged: bool = True,
max_chunk_size: Optional[int] = None,
) -> ExLlamaV2Model:
"""
Load an ExLlamaV2 model.
Parameters
----------
model_path (str)
... |
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_... | exl2 | python | dottxt-ai/outlines | outlines/models/exllamav2.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/exllamav2.py | Apache-2.0 |
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),
) | Create a stable representation for outlines.caching | __getstate__ | python | dottxt-ai/outlines | outlines/models/llamacpp.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/llamacpp.py | Apache-2.0 |
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` ... | Prepare the generation parameters.
`llama-cpp-python` uses different default values
| prepare_generation_parameters | python | dottxt-ai/outlines | outlines/models/llamacpp.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/llamacpp.py | Apache-2.0 |
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... | Generate text using `llama-cpp-python`.
Parameters
----------
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
... | generate | python | dottxt-ai/outlines | outlines/models/llamacpp.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/llamacpp.py | Apache-2.0 |
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-... | Stream text using `llama-cpp-python`.
Parameters
----------
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
... | stream | python | dottxt-ai/outlines | outlines/models/llamacpp.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/llamacpp.py | Apache-2.0 |
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 t... | 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.
... | llamacpp | python | dottxt-ai/outlines | outlines/models/llamacpp.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/llamacpp.py | Apache-2.0 |
def stream(
self,
prompts: Union[str, List[str]],
generation_parameters: "GenerationParameters",
logits_processor,
sampling_parameters: "SamplingParameters",
) -> Iterator[str]:
"""Generate text using `mlx_lm`.
Parameters
----------
prompts
... | Generate text using `mlx_lm`.
Parameters
----------
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
a... | stream | python | dottxt-ai/outlines | outlines/models/mlxlm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/mlxlm.py | Apache-2.0 |
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... |
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.
Parameters
----------
prompt
The input prompt.
temp
The temperature ... | generate_step | python | dottxt-ai/outlines | outlines/models/mlxlm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/mlxlm.py | Apache-2.0 |
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... | 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.
... | mlxlm | python | dottxt-ai/outlines | outlines/models/mlxlm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/mlxlm.py | Apache-2.0 |
def __init__(
self,
client,
config,
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
... | 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 `... | __init__ | python | dottxt-ai/outlines | outlines/models/openai.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/openai.py | Apache-2.0 |
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.nd... | 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 ... | __call__ | python | dottxt-ai/outlines | outlines/models/openai.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/openai.py | Apache-2.0 |
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 t... | 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 ... | generate_chat | python | dottxt-ai/outlines | outlines/models/openai.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/openai.py | Apache-2.0 |
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,
... | Handle OpenAI API errors and missing API key. | error_handler | python | dottxt-ai/outlines | outlines/models/openai.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/openai.py | Apache-2.0 |
def encode(
self, prompt: Union[str, List[str]]
) -> Tuple[NDArray[np.int64], NDArray[np.int64]]:
"""Translate the input prompts into arrays of token ids and attention mask."""
... | Translate the input prompts into arrays of token ids and attention mask. | encode | python | dottxt-ai/outlines | outlines/models/tokenizer.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/tokenizer.py | Apache-2.0 |
def get_llama_tokenizer_types():
"""Get all the Llama tokenizer types/classes that need work-arounds.
When they can't be imported, a dummy class is created.
"""
try:
from transformers.models.llama import LlamaTokenizer
except ImportError:
class LlamaTokenizer: # type: ignore
... | Get all the Llama tokenizer types/classes that need work-arounds.
When they can't be imported, a dummy class is created.
| get_llama_tokenizer_types | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
def forward(
self,
input_ids: "torch.LongTensor",
attention_mask: "torch.LongTensor",
past_key_values: Optional[Tuple] = None,
) -> Tuple["torch.FloatTensor", Optional[KVCacheType]]:
"""Compute a forward pass through the transformer model.
Parameters
--------... | Compute a forward pass through the transformer model.
Parameters
----------
input_ids
The input token ids. Must be one or two dimensional.
attention_mask
The attention mask. Must be one or two dimensional.
past_key_values
A tuple of tuples c... | forward | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
def generate(
self,
prompts: Union[str, List[str]],
generation_parameters: GenerationParameters,
logits_processor: Optional["OutlinesLogitsProcessor"],
sampling_parameters: SamplingParameters,
) -> Union[str, List[str], List[List[str]]]:
"""Generate text using `transf... | Generate text using `transformers`.
Parameters
----------
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
... | generate | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
def stream(
self,
prompts: Union[str, List[str]],
generation_parameters: GenerationParameters,
logits_processor: Optional["OutlinesLogitsProcessor"],
sampling_parameters: SamplingParameters,
) -> Iterator[Union[str, List[str]]]:
"""
Temporary stream stand-in w... |
Temporary stream stand-in which implements stream() signature
and equivalent behaviour but isn't yielded until generation completes.
TODO: implement following completion of https://github.com/huggingface/transformers/issues/30810
| stream | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
def _get_generation_kwargs(
self,
prompts: Union[str, List[str]],
generation_parameters: GenerationParameters,
logits_processor: Optional["OutlinesLogitsProcessor"],
sampling_parameters: SamplingParameters,
) -> dict:
"""
Convert outlines generation parameters... |
Convert outlines generation parameters into model.generate kwargs
| _get_generation_kwargs | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
def transformers(
model_name: str,
device: Optional[str] = None,
model_kwargs: dict = {},
tokenizer_kwargs: dict = {},
model_class=None,
tokenizer_class=None,
):
"""Instantiate a model from the `transformers` library and its tokenizer.
Parameters
----------
model_name
Th... | 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.
device
The device(s) on which the model should be loaded. This overrides
the `device_map` entry in `model_kwargs`... | transformers | python | dottxt-ai/outlines | outlines/models/transformers.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers.py | Apache-2.0 |
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, L... | Generate text using `transformers`.
Parameters
----------
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,
t... | generate | python | dottxt-ai/outlines | outlines/models/transformers_vision.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers_vision.py | Apache-2.0 |
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
--------... | 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`.
... | transformers_vision | python | dottxt-ai/outlines | outlines/models/transformers_vision.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/transformers_vision.py | Apache-2.0 |
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,
):
"""Gene... | Generate text using vLLM.
Parameters
----------
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
argum... | generate | python | dottxt-ai/outlines | outlines/models/vllm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/vllm.py | Apache-2.0 |
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."
) | Return a text generator.
Streaming is not yet available for `vllm.LLM`.
TODO: Implement the streaming functionality ourselves.
| stream | python | dottxt-ai/outlines | outlines/models/vllm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/vllm.py | Apache-2.0 |
def vllm(model_name: str, **vllm_model_params):
"""Load a vLLM model.
Parameters
---------
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-projec... | Load a vLLM model.
Parameters
---------
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
| vllm | python | dottxt-ai/outlines | outlines/models/vllm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/vllm.py | Apache-2.0 |
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
com... | 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... | adapt_tokenizer | python | dottxt-ai/outlines | outlines/models/vllm.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/models/vllm.py | Apache-2.0 |
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... |
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 ... | process_logits | python | dottxt-ai/outlines | outlines/processors/base_logits_processor.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/base_logits_processor.py | Apache-2.0 |
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 ->... |
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... | __call__ | python | dottxt-ai/outlines | outlines/processors/base_logits_processor.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/base_logits_processor.py | Apache-2.0 |
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_lik... | Convert various types to torch.Tensor. | _to_torch | python | dottxt-ai/outlines | outlines/processors/base_logits_processor.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/base_logits_processor.py | Apache-2.0 |
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 is list:
... | Convert torch.Tensor to the specified target type. | _from_torch | python | dottxt-ai/outlines | outlines/processors/base_logits_processor.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/base_logits_processor.py | Apache-2.0 |
def __init__(self, tokenizer: "Tokenizer", guide: Guide):
"""A Guide-based logits processor.
Parameters
----------
tokenizer
The tokenizer used to convert tokens to ids.
guide
The `outlines.fsm.Guide. which is used to bias the logits.
"""
... | A Guide-based logits processor.
Parameters
----------
tokenizer
The tokenizer used to convert tokens to ids.
guide
The `outlines.fsm.Guide. which is used to bias the logits.
| __init__ | python | dottxt-ai/outlines | outlines/processors/structured.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/structured.py | Apache-2.0 |
def process_logits(
self, input_ids: torch.LongTensor, logits: torch.FloatTensor
) -> torch.Tensor:
"""Use the Guide to bias the logits before sampling the next token.
Parameters
----------
input_ids
The input token ids.
logits
The logits.
... | Use the Guide 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.
| process_logits | python | dottxt-ai/outlines | outlines/processors/structured.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/structured.py | Apache-2.0 |
def __init__(
self,
schema: Union[dict, Type[BaseModel], str],
tokenizer: "Tokenizer",
whitespace_pattern: Optional[str] = None,
):
"""Compile the Guide that drives the JSON-guided generation.
Parameters
----------
schema
A JSON schema tha... | Compile the Guide 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... | __init__ | python | dottxt-ai/outlines | outlines/processors/structured.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/structured.py | Apache-2.0 |
def process_logits(
self, input_ids: torch.LongTensor, logits: torch.Tensor
) -> torch.Tensor:
"""Same behavior as GuideLogitsProcessor, but uses rejection sampling"""
if self._seq_start_idx is None:
self._seq_start_idx = len(input_ids[0])
sequence_states: List = [] # v... | Same behavior as GuideLogitsProcessor, but uses rejection sampling | process_logits | python | dottxt-ai/outlines | outlines/processors/structured.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/processors/structured.py | Apache-2.0 |
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... | 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).
... | generate | python | dottxt-ai/outlines | outlines/serve/serve.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/serve/serve.py | Apache-2.0 |
def matches(self, value: str) -> bool:
"""Check that a given value is in the language defined by the Term.
We make the assumption that the language defined by the term can
be defined with a regular expression.
"""
pattern = to_regex(self)
compiled = re.compile(pattern)
... | Check that a given value is in the language defined by the Term.
We make the assumption that the language defined by the term can
be defined with a regular expression.
| matches | python | dottxt-ai/outlines | outlines/types/dsl.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/types/dsl.py | Apache-2.0 |
def exactly(count: int, term: Union[Term, str]) -> QuantifyExact:
"""Repeat the term exactly `count` times."""
term = String(term) if isinstance(term, str) else term
return QuantifyExact(term, count) | Repeat the term exactly `count` times. | exactly | python | dottxt-ai/outlines | outlines/types/dsl.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/types/dsl.py | Apache-2.0 |
def at_least(count: int, term: Union[Term, str]) -> QuantifyMinimum:
"""Repeat the term at least `count` times."""
term = String(term) if isinstance(term, str) else term
return QuantifyMinimum(term, count) | Repeat the term at least `count` times. | at_least | python | dottxt-ai/outlines | outlines/types/dsl.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/types/dsl.py | Apache-2.0 |
def to_regex(term: Term) -> str:
"""Convert a term to a regular expression.
We only consider self-contained terms that do not refer to another rule.
"""
match term:
case String():
return re.escape(term.value)
case Regex():
return f"({term.pattern})"
case... | Convert a term to a regular expression.
We only consider self-contained terms that do not refer to another rule.
| to_regex | python | dottxt-ai/outlines | outlines/types/dsl.py | https://github.com/dottxt-ai/outlines/blob/master/outlines/types/dsl.py | Apache-2.0 |
def refresh_environment():
"""Refresh the test environment.
This deletes any reference to `outlines` in the modules dictionary and unsets the
`OUTLINES_CACHE_DIR` environment variable if set. This is necessary because we
are using a module variable to hold the cache.
"""
import sys
for ke... | Refresh the test environment.
This deletes any reference to `outlines` in the modules dictionary and unsets the
`OUTLINES_CACHE_DIR` environment variable if set. This is necessary because we
are using a module variable to hold the cache.
| refresh_environment | python | dottxt-ai/outlines | tests/test_cache.py | https://github.com/dottxt-ai/outlines/blob/master/tests/test_cache.py | Apache-2.0 |
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
memory = outlines.get_cache()
assert memory.directory == tempdi... | Initialize a temporary cache and delete it after the test has run. | test_cache | python | dottxt-ai/outlines | tests/test_cache.py | https://github.com/dottxt-ai/outlines/blob/master/tests/test_cache.py | Apache-2.0 |
def test_disable_cache(test_cache):
"""Make sure that we can disable the cache."""
import outlines
outlines.disable_cache()
# If the cache is disabled then the size
# of `store` should increase every time
# `f` is called.
store = list()
@test_cache
def f(x):
store.append(1... | Make sure that we can disable the cache. | test_disable_cache | python | dottxt-ai/outlines | tests/test_cache.py | https://github.com/dottxt-ai/outlines/blob/master/tests/test_cache.py | Apache-2.0 |
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