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# Copyright (c) 2022 GT4SD team
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"""HuggingFace generation algorithm."""
import logging
from dataclasses import field
from typing import ClassVar, Dict, Optional, Set, TypeVar
from ...core import (
AlgorithmConfiguration,
GeneratorAlgorithm,
Untargeted,
get_configuration_class_with_attributes,
)
from ...registry import ApplicationsRegistry
from .implementation import MODEL_TYPES, Generator
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
T = type(None)
S = TypeVar("S", bound=str)
[docs]class HuggingFaceGenerationAlgorithm(GeneratorAlgorithm[S, T]):
[docs] def __init__(
self, configuration: AlgorithmConfiguration, target: Optional[T] = None
):
"""HuggingFace generation algorithm.
Args:
configuration: domain and application
specification, defining types and validations.
target: unused since it is not a conditional generator.
Example:
An example for using a generative algorithm from HuggingFace::
configuration = HuggingFaceXLMGenerator()
algorithm = HuggingFaceGenerationAlgorithm(configuration=configuration)
items = list(algorithm.sample(1))
print(items)
"""
configuration = self.validate_configuration(configuration)
# TODO there might also be a validation/check on the target input
super().__init__(
configuration=configuration,
target=None, # type:ignore
)
[docs] def get_generator(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T],
) -> Untargeted:
"""Get the function to sample batches.
Args:
configuration: helps to set up the application.
target: context or condition for the generation. Unused in the algorithm.
Returns:
callable generating a batch of items.
"""
logger.info("ensure artifacts for the application are present.")
self.local_artifacts = configuration.ensure_artifacts()
implementation: Generator = configuration.get_conditional_generator( # type: ignore
self.local_artifacts
)
return implementation.sample
[docs] def validate_configuration(
self, configuration: AlgorithmConfiguration
) -> AlgorithmConfiguration:
# TODO raise InvalidAlgorithmConfiguration
assert isinstance(configuration, AlgorithmConfiguration)
return configuration
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceConfiguration(AlgorithmConfiguration[str, None]):
"""Basic configuration for an hugging face algorithm."""
algorithm_type: ClassVar[str] = "generation"
domain: ClassVar[str] = "nlp"
model_type: str = field(
default="",
metadata=dict(
description=f"Type of the model. Supported: {', '.join(MODEL_TYPES.keys())}"
),
)
prompt: str = field(
default="I'm a stochastic parrot.",
metadata=dict(description="Prompt for text generation."),
)
length: int = field(
default=20, metadata=dict(description="Length of the generated text.")
)
stop_token: str = field(
default="", metadata=dict(description="Stop token for text generation.")
)
num_beams: int = field(
default=1, metadata=dict(description="Number of beams for beam search.")
)
do_sample: bool = field(
default=True,
metadata=dict(
description="Whether or not to use sampling; use greedy decoding otherwise."
),
)
temperature: float = field(
default=1.0,
metadata=dict(
description="Temperature for sampling, the lower the greedier the sampling."
),
)
repetition_penalty: float = field(
default=1.0,
metadata=dict(
description="Primarily useful for CTRL model, where 1.2 should be used."
),
)
k: int = field(
default=50,
metadata=dict(description="Number of top-k probability tokens to keep."),
)
p: float = field(
default=1.0,
metadata=dict(
description="Only tokens with cumulative probabilities summing up to this value are kept."
),
)
prefix: str = field(
default="",
metadata=dict(
description="Text defining context provided prior to the prompt."
),
)
number_of_sequences: int = field(
default=8,
metadata=dict(description="Number of text sequences to generate."),
)
[docs] def get_target_description(self) -> Optional[Dict[str, str]]:
"""Get description of the target for generation.
Returns:
target description, returns None in case no target is used.
"""
return None
[docs] def get_conditional_generator(self, resources_path: str, **kwargs) -> Generator:
return Generator(
resources_path=resources_path,
model_type=self.model_type,
model_name=self.algorithm_version,
prompt=self.prompt,
length=self.length,
stop_token=self.stop_token,
num_beams=self.num_beams,
do_sample=self.do_sample,
temperature=self.temperature,
repetition_penalty=self.repetition_penalty,
k=self.k,
p=self.p,
prefix=self.prefix,
number_of_sequences=self.number_of_sequences,
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceXLMGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using XLM."""
algorithm_version: str = "xlm-mlm-en-2048"
model_type: str = "xlm"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceCTRLGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using CTRL."""
algorithm_version: str = "ctrl"
model_type: str = "ctrl"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceGPT2Generator(HuggingFaceConfiguration):
"""Configuration to generate text using GPT2."""
algorithm_version: str = "gpt2"
model_type: str = "gpt2"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceOpenAIGPTGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using OpenAIGPT."""
algorithm_version: str = "openai-gpt"
model_type: str = "openai-gpt"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceXLNetGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using XLNet."""
algorithm_version: str = "xlnet-large-cased"
model_type: str = "xlnet"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceTransfoXLGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using TransfoXL."""
algorithm_version: str = "transfo-xl-wt103"
model_type: str = "transfo-xl"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)
[docs]@ApplicationsRegistry.register_algorithm_application(HuggingFaceGenerationAlgorithm)
class HuggingFaceSeq2SeqGenerator(HuggingFaceConfiguration):
"""Configuration to generate text using Seq2Seq LMs."""
algorithm_version: str = "t5-small"
model_type: str = "auto-seq2seq-lm"
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
Standard S3 and cache search adding the version used in the configuration.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
logger.warning(
"more algorithm versions can be found on https://huggingface.co/models"
)
return (
get_configuration_class_with_attributes(cls)
.list_versions()
.union({cls.algorithm_version})
)