mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
feat: bump outlines and pydantic logic
This commit is contained in:
drbh
parent
03003d1eaf
commit
09f2e8ed13
@ -1,5 +1,5 @@
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from enum import Enum
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from pydantic import BaseModel, validator
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from pydantic import BaseModel, field_validator
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from typing import Optional, List, Union, Any
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from text_generation.errors import ValidationError
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@ -32,7 +32,7 @@ class Message(BaseModel):
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# Role of the message sender
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role: str
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# Content of the message
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content: Optional[str]
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content: Optional[str] = None
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# Optional name of the message sender
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name: Optional[str] = None
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# Tool calls associated with the chat completion
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@ -56,7 +56,7 @@ class ChatCompletionComplete(BaseModel):
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# Reason for completion
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finish_reason: str
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# Usage details of the chat completion
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usage: Any
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usage: Optional[Any] = None
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class Function(BaseModel):
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@ -73,7 +73,7 @@ class ChoiceDeltaToolCall(BaseModel):
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class ChoiceDelta(BaseModel):
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role: str
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content: Optional[str]
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content: Optional[str] = None
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tool_calls: Optional[ChoiceDeltaToolCall]
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@ -176,7 +176,7 @@ class Parameters(BaseModel):
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# grammar to use for generation
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grammar: Optional[Grammar] = None
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@validator("best_of")
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@field_validator("best_of")
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def valid_best_of(cls, field_value, values):
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if field_value is not None:
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if field_value <= 0:
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@ -195,55 +195,55 @@ class Parameters(BaseModel):
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return field_value
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@validator("repetition_penalty")
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@field_validator("repetition_penalty")
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def valid_repetition_penalty(cls, v):
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if v is not None and v <= 0:
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raise ValidationError("`repetition_penalty` must be strictly positive")
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return v
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@validator("seed")
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@field_validator("seed")
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def valid_seed(cls, v):
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if v is not None and v < 0:
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raise ValidationError("`seed` must be positive")
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return v
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@validator("temperature")
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@field_validator("temperature")
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def valid_temp(cls, v):
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if v is not None and v <= 0:
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raise ValidationError("`temperature` must be strictly positive")
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return v
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@validator("top_k")
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@field_validator("top_k")
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def valid_top_k(cls, v):
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if v is not None and v <= 0:
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raise ValidationError("`top_k` must be strictly positive")
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return v
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@validator("top_p")
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@field_validator("top_p")
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def valid_top_p(cls, v):
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if v is not None and (v <= 0 or v >= 1.0):
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raise ValidationError("`top_p` must be > 0.0 and < 1.0")
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return v
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@validator("truncate")
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@field_validator("truncate")
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def valid_truncate(cls, v):
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if v is not None and v <= 0:
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raise ValidationError("`truncate` must be strictly positive")
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return v
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@validator("typical_p")
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@field_validator("typical_p")
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def valid_typical_p(cls, v):
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if v is not None and (v <= 0 or v >= 1.0):
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raise ValidationError("`typical_p` must be > 0.0 and < 1.0")
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return v
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@validator("top_n_tokens")
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@field_validator("top_n_tokens")
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def valid_top_n_tokens(cls, v):
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if v is not None and v <= 0:
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raise ValidationError("`top_n_tokens` must be strictly positive")
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return v
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@validator("grammar")
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@field_validator("grammar")
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def valid_grammar(cls, v):
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if v is not None:
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if v.type == GrammarType.Regex and not v.value:
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@ -261,15 +261,15 @@ class Request(BaseModel):
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# Whether to stream output tokens
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stream: bool = False
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@validator("inputs")
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@field_validator("inputs")
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def valid_input(cls, v):
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if not v:
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raise ValidationError("`inputs` cannot be empty")
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return v
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@validator("stream")
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@field_validator("stream")
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def valid_best_of_stream(cls, field_value, values):
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parameters = values["parameters"]
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parameters = values.data["parameters"]
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if (
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parameters is not None
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and parameters.best_of is not None
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@ -25,6 +25,7 @@ from text_generation.types import (
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Grammar,
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ChatComplete,
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ChatCompletionChunk,
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ChatCompletionComplete,
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)
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DOCKER_IMAGE = os.getenv("DOCKER_IMAGE", None)
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@ -42,11 +43,16 @@ class ResponseComparator(JSONSnapshotExtension):
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exclude=None,
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matcher=None,
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):
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if isinstance(data, Response):
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data = data.dict()
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if (
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isinstance(data, Response)
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or isinstance(data, ChatComplete)
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or isinstance(data, ChatCompletionChunk)
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or isinstance(data, ChatCompletionComplete)
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):
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data = data.model_dump()
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if isinstance(data, List):
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data = [d.dict() for d in data]
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data = [d.model_dump() for d in data]
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data = self._filter(
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data=data, depth=0, path=(), exclude=exclude, matcher=matcher
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@ -13,7 +13,7 @@
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"usage": null
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}
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],
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"created": 1708957015,
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"created": 1710795556,
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"id": "",
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"object": "text_completion",
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@ -8,24 +8,26 @@
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"content": null,
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"name": null,
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"role": "assistant",
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"tool_calls": {
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14
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}
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},
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"id": 0,
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"type": "function"
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}
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"tool_calls": [
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{
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14
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}
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},
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"id": 0,
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"type": "function"
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}
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]
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},
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"usage": null
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}
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],
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"created": 1709079417,
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"created": 1710795556,
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"id": "",
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"object": "text_completion",
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@ -8,24 +8,26 @@
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"content": null,
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"name": null,
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"role": "assistant",
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"tool_calls": {
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14
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}
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},
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"id": 0,
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"type": "function"
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}
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"tool_calls": [
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{
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14
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}
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},
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"id": 0,
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"type": "function"
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}
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]
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},
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"usage": null
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}
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],
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"created": 1709079492,
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"created": 1710795557,
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"id": "",
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"object": "text_completion",
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@ -8,23 +8,25 @@
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"content": null,
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"name": null,
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"role": "assistant",
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"tool_calls": {
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY"
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}
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},
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"id": 0,
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"type": "function"
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}
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"tool_calls": [
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{
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"function": {
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"description": null,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY"
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}
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},
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"id": 0,
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"type": "function"
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}
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]
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},
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"usage": null
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}
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],
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"created": 1709079493,
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"created": 1710795557,
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"id": "",
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"object": "text_completion",
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@ -10,16 +10,16 @@
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"name": null
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},
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"id": "",
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"index": 20,
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"index": 0,
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"type": "function"
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}
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},
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"finish_reason": "eos_token",
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"index": 20,
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"index": 0,
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"logprobs": null
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}
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],
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"created": 1709087088,
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"created": 1710795499,
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"id": "",
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"object": "text_completion",
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@ -98,7 +98,7 @@ async def test_flash_llama_grammar_no_tools(
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assert response == response_snapshot
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@pytest.mark.skip
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# @pytest.mark.skip
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_snapshot):
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@ -119,23 +119,25 @@ async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_sna
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],
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)
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assert response.choices[0].message.content == None
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assert response.choices[0].message.tool_calls == {
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14,
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assert response.choices[0].message.tool_calls == [
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{
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14,
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},
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},
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},
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"id": 0,
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"type": "function",
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}
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"id": 0,
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"type": "function",
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}
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]
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assert response == response_snapshot
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@pytest.mark.skip
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# @pytest.mark.skip
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_grammar_tools_auto(
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@ -159,23 +161,25 @@ async def test_flash_llama_grammar_tools_auto(
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],
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)
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assert response.choices[0].message.content == None
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assert response.choices[0].message.tool_calls == {
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14,
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assert response.choices[0].message.tool_calls == [
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{
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {
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"format": "celsius",
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"location": "New York, NY",
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"num_days": 14,
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},
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},
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},
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"id": 0,
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"type": "function",
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}
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"id": 0,
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"type": "function",
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}
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]
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assert response == response_snapshot
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@pytest.mark.skip
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# @pytest.mark.skip
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_grammar_tools_choice(
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@ -199,19 +203,21 @@ async def test_flash_llama_grammar_tools_choice(
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],
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)
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assert response.choices[0].message.content == None
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assert response.choices[0].message.tool_calls == {
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"id": 0,
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"type": "function",
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {"format": "celsius", "location": "New York, NY"},
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},
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}
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assert response.choices[0].message.tool_calls == [
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{
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"id": 0,
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"type": "function",
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"function": {
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"description": None,
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"name": "tools",
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"parameters": {"format": "celsius", "location": "New York, NY"},
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},
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}
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]
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assert response == response_snapshot
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@pytest.mark.skip
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# @pytest.mark.skip
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_grammar_tools_stream(
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@ -34,7 +34,7 @@ peft = { version = "^0.9.0", optional = true }
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torch = { version = "^2.1.1", optional = true }
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scipy = "^1.11.1"
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pillow = "^10.0.0"
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outlines= { version = "^0.0.27", optional = true }
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outlines= { version = "0.0.36", optional = true }
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[tool.poetry.extras]
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torch = ["torch"]
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@ -6,7 +6,7 @@ from typing import Dict, Union
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from text_generation_server.pb.generate_pb2 import GrammarType
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from outlines.fsm.fsm import RegexFSM
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from outlines.fsm.json_schema import build_regex_from_object
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from outlines.fsm.json_schema import build_regex_from_schema
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from functools import lru_cache
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from typing import List, Optional, DefaultDict
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import time
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@ -512,7 +512,7 @@ class GrammarLogitProcessor(LogitsProcessor):
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def _cached_compile_fsm(grammar_type, schema, tokenizer):
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start_time = time.time()
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if grammar_type == GrammarType.GRAMMAR_TYPE_JSON:
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schema = build_regex_from_object(schema)
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schema = build_regex_from_schema(schema)
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elif grammar_type == GrammarType.GRAMMAR_TYPE_REGEX:
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pass # schema is already a regex just here for clarity
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fsm = RegexFSM(schema, tokenizer)
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