diff --git a/clients/python/lorax/types.py b/clients/python/lorax/types.py index 894a518e0..b0b521836 100644 --- a/clients/python/lorax/types.py +++ b/clients/python/lorax/types.py @@ -1,5 +1,5 @@ from enum import Enum -from pydantic import BaseModel, validator, Field, ConfigDict +from pydantic import BaseModel, field_validator, Field, ConfigDict from typing import Optional, List, Dict, Any, OrderedDict, Union from lorax.errors import ValidationError @@ -16,40 +16,40 @@ class MergedAdapters(BaseModel): # Weights of the adapters to merge weights: List[float] # Merge strategy - merge_strategy: Optional[str] + merge_strategy: Optional[str] = None # Density density: float # Majority sign method - majority_sign_method: Optional[str] + majority_sign_method: Optional[str] = None - @validator("ids") + @field_validator("ids") def validate_ids(cls, v): if not v: raise ValidationError("`ids` cannot be empty") return v - @validator("weights") + @field_validator("weights") def validate_weights(cls, v, values): - ids = values["ids"] + ids = values.data["ids"] if not v: raise ValidationError("`weights` cannot be empty") if len(ids) != len(v): raise ValidationError("`ids` and `weights` must have the same length") return v - @validator("merge_strategy") + @field_validator("merge_strategy") def validate_merge_strategy(cls, v): if v is not None and v not in MERGE_STRATEGIES: raise ValidationError(f"`merge_strategy` must be one of {MERGE_STRATEGIES}") return v - @validator("density") + @field_validator("density") def validate_density(cls, v): if v < 0 or v > 1.0: raise ValidationError("`density` must be >= 0.0 and <= 1.0") return v - @validator("majority_sign_method") + @field_validator("majority_sign_method") def validate_majority_sign_method(cls, v): if v is not None and v not in MAJORITY_SIGN_METHODS: raise ValidationError(f"`majority_sign_method` must be one of {MAJORITY_SIGN_METHODS}") @@ -69,13 +69,13 @@ class ResponseFormat(BaseModel): class Parameters(BaseModel): # The ID of the adapter to use - adapter_id: Optional[str] + adapter_id: Optional[str] = None # The source of the adapter to use - adapter_source: Optional[str] + adapter_source: Optional[str] = None # Adapter merge parameters - merged_adapters: Optional[MergedAdapters] + merged_adapters: Optional[MergedAdapters] = None # API token for accessing private adapters - api_token: Optional[str] + api_token: Optional[str] = None # Activate logits sampling do_sample: bool = False # Maximum number of generated tokens @@ -90,21 +90,21 @@ class Parameters(BaseModel): # Stop generating tokens if a member of `stop_sequences` is generated stop: List[str] = [] # Random sampling seed - seed: Optional[int] + seed: Optional[int] = None # The value used to module the logits distribution. - temperature: Optional[float] + temperature: Optional[float] = None # The number of highest probability vocabulary tokens to keep for top-k-filtering. - top_k: Optional[int] + 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] + top_p: Optional[float] = None # truncate inputs tokens to the given size - truncate: Optional[int] + 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] + typical_p: Optional[float] = None # Generate best_of sequences and return the one if the highest token logprobs - best_of: Optional[int] + 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 @@ -112,85 +112,85 @@ class Parameters(BaseModel): # Get decoder input token logprobs and ids decoder_input_details: bool = False # The number of highest probability vocabulary tokens to return as alternative tokens in the generation result - return_k_alternatives: Optional[int] + return_k_alternatives: Optional[int] = None # Optional response format specification to constrain the generated text - response_format: Optional[ResponseFormat] + response_format: Optional[ResponseFormat] = None - @validator("adapter_id") + @field_validator("adapter_id") def valid_adapter_id(cls, v, values): merged_adapters = values.get("merged_adapters") if v is not None and merged_adapters is not None: raise ValidationError("you must specify at most one of `adapter_id` or `merged_adapters`") return v - @validator("adapter_source") + @field_validator("adapter_source") def valid_adapter_source(cls, v): if v is not None and v not in ADAPTER_SOURCES: raise ValidationError(f"`adapter_source` must be one of {ADAPTER_SOURCES}") return v - @validator("best_of") + @field_validator("best_of") def valid_best_of(cls, field_value, values): if field_value is not None: if field_value <= 0: raise ValidationError("`best_of` must be strictly positive") - if field_value > 1 and values["seed"] is not None: + if field_value > 1 and values.data["seed"] is not None: raise ValidationError("`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) + values.data["do_sample"] + | (values.data["temperature"] is not None) + | (values.data["top_k"] is not None) + | (values.data["top_p"] is not None) + | (values.data["typical_p"] is not None) ) if field_value > 1 and not sampling: raise ValidationError("you must use sampling when `best_of` is > 1") return field_value - @validator("repetition_penalty") + @field_validator("repetition_penalty") def valid_repetition_penalty(cls, v): if v is not None and v <= 0: raise ValidationError("`repetition_penalty` must be strictly positive") return v - @validator("seed") + @field_validator("seed") def valid_seed(cls, v): if v is not None and v < 0: raise ValidationError("`seed` must be positive") return v - @validator("temperature") + @field_validator("temperature") def valid_temp(cls, v): if v is not None and v <= 0: raise ValidationError("`temperature` must be strictly positive") return v - @validator("top_k") + @field_validator("top_k") def valid_top_k(cls, v): if v is not None and v <= 0: raise ValidationError("`top_k` must be strictly positive") return v - @validator("top_p") + @field_validator("top_p") def valid_top_p(cls, v): if v is not None and (v <= 0 or v >= 1.0): raise ValidationError("`top_p` must be > 0.0 and < 1.0") return v - @validator("truncate") + @field_validator("truncate") def valid_truncate(cls, v): if v is not None and v <= 0: raise ValidationError("`truncate` must be strictly positive") return v - @validator("typical_p") + @field_validator("typical_p") def valid_typical_p(cls, v): if v is not None and (v <= 0 or v >= 1.0): raise ValidationError("`typical_p` must be > 0.0 and < 1.0") return v - @validator("return_k_alternatives") + @field_validator("return_k_alternatives") def valid_return_k_alternatives(cls, v): if v is not None and v <= 0: raise ValidationError("`return_k_alternatives` must be strictly positive") @@ -201,19 +201,19 @@ class Request(BaseModel): # Prompt inputs: str # Generation parameters - parameters: Optional[Parameters] + parameters: Optional[Parameters] = None # Whether to stream output tokens stream: bool = False - @validator("inputs") + @field_validator("inputs") def valid_input(cls, v): if not v: raise ValidationError("`inputs` cannot be empty") return v - @validator("stream") + @field_validator("stream") def valid_best_of_stream(cls, field_value, values): - parameters = values["parameters"] + parameters = values.data["parameters"] if parameters is not None and parameters.best_of is not None and parameters.best_of > 1 and field_value: raise ValidationError("`best_of` != 1 is not supported when `stream` == True") return field_value @@ -227,7 +227,7 @@ class InputToken(BaseModel): text: str # Logprob # Optional since the logprob of the first token cannot be computed - logprob: Optional[float] + logprob: Optional[float] = None # Alternative Tokens @@ -252,7 +252,7 @@ class Token(BaseModel): # Can be used to ignore tokens when concatenating special: bool # Alternative tokens - alternative_tokens: Optional[List[AlternativeToken]] + alternative_tokens: Optional[List[AlternativeToken]] = None # Generation finish reason @@ -274,7 +274,7 @@ class BestOfSequence(BaseModel): # Number of generated tokens generated_tokens: int # Sampling seed if sampling was activated - seed: Optional[int] + seed: Optional[int] = None # Decoder input tokens, empty if decoder_input_details is False prefill: List[InputToken] # Generated tokens @@ -290,13 +290,13 @@ class Details(BaseModel): # Number of generated tokens generated_tokens: int # Sampling seed if sampling was activated - seed: Optional[int] + seed: Optional[int] = None # Decoder input tokens, empty if decoder_input_details is False prefill: List[InputToken] # Generated tokens tokens: List[Token] # Additional sequences when using the `best_of` parameter - best_of_sequences: Optional[List[BestOfSequence]] + best_of_sequences: Optional[List[BestOfSequence]] = None # `generate` return value @@ -304,7 +304,7 @@ class Response(BaseModel): # Generated text generated_text: str # Generation details - details: Optional[Details] + details: Optional[Details] = None # `generate_stream` details @@ -316,7 +316,7 @@ class StreamDetails(BaseModel): # Number of generated tokens generated_tokens: int # Sampling seed if sampling was activated - seed: Optional[int] + seed: Optional[int] = None # `generate_stream` return value @@ -325,10 +325,10 @@ class StreamResponse(BaseModel): token: Token # Complete generated text # Only available when the generation is finished - generated_text: Optional[str] + generated_text: Optional[str] = None # Generation details # Only available when the generation is finished - details: Optional[StreamDetails] + details: Optional[StreamDetails] = None # Inference API currently deployed model diff --git a/clients/python/poetry.lock b/clients/python/poetry.lock index 1503c40eb..985fc9233 100644 --- a/clients/python/poetry.lock +++ b/clients/python/poetry.lock @@ -1,416 +1,12 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + [[package]] name = "aiohttp" version = "3.8.4" description = "Async http client/server framework (asyncio)" -category = "main" -optional = false -python-versions = ">=3.6" - 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"jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.7" +content-hash = "d4beb9054d2f36285427160c279049982d2dc295a85f3191fb48f5ea04533f6d" diff --git a/clients/python/pyproject.toml b/clients/python/pyproject.toml index fb2a5994d..d2f2af059 100644 --- a/clients/python/pyproject.toml +++ b/clients/python/pyproject.toml @@ -15,7 +15,7 @@ repository = "https://github.com/predibase/lorax" [tool.poetry.dependencies] python = "^3.7" -pydantic = "^1.10" +pydantic = "> 2, < 3" aiohttp = "^3.8" huggingface-hub = ">= 0.12, < 1.0"