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# coding=utf-8
# Copyright 2023-present, the HuggingFace Inc. team.
#
# 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.
#
# Original implementation taken from the `text-generation` Python client (see https://pypi.org/project/text-generation/
# and https://github.com/huggingface/text-generation-inference/tree/main/clients/python)
#
# Changes compared to original implementation:
# - use pydantic.dataclasses instead of BaseModel
# - default to Python's dataclasses if Pydantic is not installed (same implementation but no validation)
# - added default values for all parameters (not needed in BaseModel but dataclasses yes)
# - integrated in `huggingface_hub.InferenceClient``
# - added `stream: bool` and `details: bool` in the `text_generation` method instead of having different methods for each use case
import warnings
from dataclasses import field
from enum import Enum
from typing import List, NoReturn, Optional
from requests import HTTPError
from ..utils import is_pydantic_available
if is_pydantic_available():
from pydantic import validator as pydantic_validator
from pydantic.dataclasses import dataclass
def validator(*args, **kwargs):
# Pydantic v1's `@validator` is deprecated in favor of `@field_validator`. In order to support both pydantic v1
# and v2 without changing the logic, we catch the warning message in pydantic v2 and ignore it. If we want to
# support pydantic v3 in the future, we will drop support for pydantic v1 and use `pydantic.field_validator`
# correctly.
#
# Related:
# - https://docs.pydantic.dev/latest/migration/#changes-to-validators
# - https://github.com/huggingface/huggingface_hub/pull/1837
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="Pydantic V1 style `@validator` validators are deprecated.")
return pydantic_validator(*args, **kwargs)
else:
# No validation if Pydantic is not installed
from dataclasses import dataclass # type: ignore
def validator(x): # type: ignore
return lambda y: y
@dataclass
class TextGenerationParameters:
"""
Parameters for text generation.
Args:
do_sample (`bool`, *optional*):
Activate logits sampling. Defaults to False.
max_new_tokens (`int`, *optional*):
Maximum number of generated tokens. Defaults to 20.
repetition_penalty (`Optional[float]`, *optional*):
The parameter for repetition penalty. A value of 1.0 means no penalty. See [this paper](https://arxiv.org/pdf/1909.05858.pdf)
for more details. Defaults to None.
return_full_text (`bool`, *optional*):
Whether to prepend the prompt to the generated text. Defaults to False.
stop (`List[str]`, *optional*):
Stop generating tokens if a member of `stop_sequences` is generated. Defaults to an empty list.
seed (`Optional[int]`, *optional*):
Random sampling seed. Defaults to None.
temperature (`Optional[float]`, *optional*):
The value used to modulate the logits distribution. Defaults to None.
top_k (`Optional[int]`, *optional*):
The number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to None.
top_p (`Optional[float]`, *optional*):
If set to a value less than 1, only the smallest set of most probable tokens with probabilities that add up
to `top_p` or higher are kept for generation. Defaults to None.
truncate (`Optional[int]`, *optional*):
Truncate input tokens to the given size. Defaults to None.
typical_p (`Optional[float]`, *optional*):
Typical Decoding mass. See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
for more information. Defaults to None.
best_of (`Optional[int]`, *optional*):
Generate `best_of` sequences and return the one with the highest token logprobs. Defaults to None.
watermark (`bool`, *optional*):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226). Defaults to False.
details (`bool`, *optional*):
Get generation details. Defaults to False.
decoder_input_details (`bool`, *optional*):
Get decoder input token logprobs and ids. Defaults to False.
"""
# Activate logits sampling
do_sample: bool = False
# Maximum number of generated tokens
max_new_tokens: int = 20
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float] = None
# Whether to prepend the prompt to the generated text
return_full_text: bool = False
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str] = field(default_factory=lambda: [])
# Random sampling seed
seed: Optional[int] = None
# The value used to module the logits distribution.
temperature: Optional[float] = None
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int] = None
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float] = None
# truncate inputs tokens to the given size
truncate: Optional[int] = None
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float] = None
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int] = None
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool = False
# Get generation details
details: bool = False
# Get decoder input token logprobs and ids
decoder_input_details: bool = False
@validator("best_of")
def valid_best_of(cls, field_value, values):
if field_value is not None:
if field_value <= 0:
raise ValueError("`best_of` must be strictly positive")
if field_value > 1 and values["seed"] is not None:
raise ValueError("`seed` must not be set when `best_of` is > 1")
sampling = (
values["do_sample"]
| (values["temperature"] is not None)
| (values["top_k"] is not None)
| (values["top_p"] is not None)
| (values["typical_p"] is not None)
)
if field_value > 1 and not sampling:
raise ValueError("you must use sampling when `best_of` is > 1")
return field_value
@validator("repetition_penalty")
def valid_repetition_penalty(cls, v):
if v is not None and v <= 0:
raise ValueError("`repetition_penalty` must be strictly positive")
return v
@validator("seed")
def valid_seed(cls, v):
if v is not None and v < 0:
raise ValueError("`seed` must be positive")
return v
@validator("temperature")
def valid_temp(cls, v):
if v is not None and v <= 0:
raise ValueError("`temperature` must be strictly positive")
return v
@validator("top_k")
def valid_top_k(cls, v):
if v is not None and v <= 0:
raise ValueError("`top_k` must be strictly positive")
return v
@validator("top_p")
def valid_top_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValueError("`top_p` must be > 0.0 and < 1.0")
return v
@validator("truncate")
def valid_truncate(cls, v):
if v is not None and v <= 0:
raise ValueError("`truncate` must be strictly positive")
return v
@validator("typical_p")
def valid_typical_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValueError("`typical_p` must be > 0.0 and < 1.0")
return v
@dataclass
class TextGenerationRequest:
"""
Request object for text generation (only for internal use).
Args:
inputs (`str`):
The prompt for text generation.
parameters (`Optional[TextGenerationParameters]`, *optional*):
Generation parameters.
stream (`bool`, *optional*):
Whether to stream output tokens. Defaults to False.
"""
# Prompt
inputs: str
# Generation parameters
parameters: Optional[TextGenerationParameters] = None
# Whether to stream output tokens
stream: bool = False
@validator("inputs")
def valid_input(cls, v):
if not v:
raise ValueError("`inputs` cannot be empty")
return v
@validator("stream")
def valid_best_of_stream(cls, field_value, values):
parameters = values["parameters"]
if parameters is not None and parameters.best_of is not None and parameters.best_of > 1 and field_value:
raise ValueError("`best_of` != 1 is not supported when `stream` == True")
return field_value
def __post_init__(self):
if not is_pydantic_available():
# If pydantic is not installed, we need to instantiate the nested dataclasses manually
if self.parameters is not None and isinstance(self.parameters, dict):
self.parameters = TextGenerationParameters(**self.parameters)
# Decoder input tokens
@dataclass
class InputToken:
"""
Represents an input token.
Args:
id (`int`):
Token ID from the model tokenizer.
text (`str`):
Token text.
logprob (`float` or `None`):
Log probability of the token. Optional since the logprob of the first token cannot be computed.
"""
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float] = None
# Generated tokens
@dataclass
class Token:
"""
Represents a token.
Args:
id (`int`):
Token ID from the model tokenizer.
text (`str`):
Token text.
logprob (`float`):
Log probability of the token.
special (`bool`):
Indicates whether the token is a special token. It can be used to ignore
tokens when concatenating.
"""
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(str, Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
@dataclass
class BestOfSequence:
"""
Represents a best-of sequence generated during text generation.
Args:
generated_text (`str`):
The generated text.
finish_reason (`FinishReason`):
The reason for the generation to finish, represented by a `FinishReason` value.
generated_tokens (`int`):
The number of generated tokens in the sequence.
seed (`Optional[int]`):
The sampling seed if sampling was activated.
prefill (`List[InputToken]`):
The decoder input tokens. Empty if `decoder_input_details` is False. Defaults to an empty list.
tokens (`List[Token]`):
The generated tokens. Defaults to an empty list.
"""
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken] = field(default_factory=lambda: [])
# Generated tokens
tokens: List[Token] = field(default_factory=lambda: [])
def __post_init__(self):
if not is_pydantic_available():
# If pydantic is not installed, we need to instantiate the nested dataclasses manually
self.prefill = [
InputToken(**input_token) if isinstance(input_token, dict) else input_token
for input_token in self.prefill
]
self.tokens = [Token(**token) if isinstance(token, dict) else token for token in self.tokens]
# `generate` details
@dataclass
class Details:
"""
Represents details of a text generation.
Args:
finish_reason (`FinishReason`):
The reason for the generation to finish, represented by a `FinishReason` value.
generated_tokens (`int`):
The number of generated tokens.
seed (`Optional[int]`):
The sampling seed if sampling was activated.
prefill (`List[InputToken]`, *optional*):
The decoder input tokens. Empty if `decoder_input_details` is False. Defaults to an empty list.
tokens (`List[Token]`):
The generated tokens. Defaults to an empty list.
best_of_sequences (`Optional[List[BestOfSequence]]`):
Additional sequences when using the `best_of` parameter.
"""
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken] = field(default_factory=lambda: [])
# Generated tokens
tokens: List[Token] = field(default_factory=lambda: [])
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]] = None
def __post_init__(self):
if not is_pydantic_available():
# If pydantic is not installed, we need to instantiate the nested dataclasses manually
self.prefill = [
InputToken(**input_token) if isinstance(input_token, dict) else input_token
for input_token in self.prefill
]
self.tokens = [Token(**token) if isinstance(token, dict) else token for token in self.tokens]
if self.best_of_sequences is not None:
self.best_of_sequences = [
BestOfSequence(**best_of_sequence) if isinstance(best_of_sequence, dict) else best_of_sequence
for best_of_sequence in self.best_of_sequences
]
# `generate` return value
@dataclass
class TextGenerationResponse:
"""
Represents a response for text generation.
Only returned when `details=True`, otherwise a string is returned.
Args:
generated_text (`str`):
The generated text.
details (`Optional[Details]`):
Generation details. Returned only if `details=True` is sent to the server.
"""
# Generated text
generated_text: str
# Generation details
details: Optional[Details] = None
def __post_init__(self):
if not is_pydantic_available():
# If pydantic is not installed, we need to instantiate the nested dataclasses manually
if self.details is not None and isinstance(self.details, dict):
self.details = Details(**self.details)
# `generate_stream` details
@dataclass
class StreamDetails:
"""
Represents details of a text generation stream.
Args:
finish_reason (`FinishReason`):
The reason for the generation to finish, represented by a `FinishReason` value.
generated_tokens (`int`):
The number of generated tokens.
seed (`Optional[int]`):
The sampling seed if sampling was activated.
"""
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# `generate_stream` return value
@dataclass
class TextGenerationStreamResponse:
"""
Represents a response for streaming text generation.
Only returned when `details=True` and `stream=True`.
Args:
token (`Token`):
The generated token.
generated_text (`Optional[str]`, *optional*):
The complete generated text. Only available when the generation is finished.
details (`Optional[StreamDetails]`, *optional*):
Generation details. Only available when the generation is finished.
"""
# Generated token
token: Token
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str] = None
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails] = None
def __post_init__(self):
if not is_pydantic_available():
# If pydantic is not installed, we need to instantiate the nested dataclasses manually
if isinstance(self.token, dict):
self.token = Token(**self.token)
if self.details is not None and isinstance(self.details, dict):
self.details = StreamDetails(**self.details)
# TEXT GENERATION ERRORS
# ----------------------
# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
# inference project (https://github.com/huggingface/text-generation-inference).
# ----------------------
class TextGenerationError(HTTPError):
"""Generic error raised if text-generation went wrong."""
# Text Generation Inference Errors
class ValidationError(TextGenerationError):
"""Server-side validation error."""
class GenerationError(TextGenerationError):
pass
class OverloadedError(TextGenerationError):
pass
class IncompleteGenerationError(TextGenerationError):
pass
class UnknownError(TextGenerationError):
pass
def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
"""
Try to parse text-generation-inference error message and raise HTTPError in any case.
Args:
error (`HTTPError`):
The HTTPError that have been raised.
"""
# Try to parse a Text Generation Inference error
try:
# Hacky way to retrieve payload in case of aiohttp error
payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
error = payload.get("error")
error_type = payload.get("error_type")
except Exception: # no payload
raise http_error
# If error_type => more information than `hf_raise_for_status`
if error_type is not None:
exception = _parse_text_generation_error(error, error_type)
raise exception from http_error
# Otherwise, fallback to default error
raise http_error
def _parse_text_generation_error(error: Optional[str], error_type: Optional[str]) -> TextGenerationError:
if error_type == "generation":
return GenerationError(error) # type: ignore
if error_type == "incomplete_generation":
return IncompleteGenerationError(error) # type: ignore
if error_type == "overloaded":
return OverloadedError(error) # type: ignore
if error_type == "validation":
return ValidationError(error) # type: ignore
return UnknownError(error) # type: ignore
|