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"""
Schemas for fine-tuning pipeline configuration and management.
This module contains Pydantic models for training jobs, configurations,
and evaluation results.
"""
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional
from uuid import UUID
from pydantic import BaseModel, Field, field_validator
class TrainingStatus(str, Enum):
"""Status of a training job."""
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
CANCELLED = "cancelled"
class TrainingStrategy(str, Enum):
"""Training strategy type."""
SUPERVISED = "supervised" # Supervised fine-tuning on good responses
RLHF = "rlhf" # Reinforcement Learning from Human Feedback
DPO = "dpo" # Direct Preference Optimization
class ModelType(str, Enum):
"""Type of model to train."""
LLM = "llm" # Language model for generation
MODERATION = "moderation" # Toxicity/moderation model
class DatasetSplit(BaseModel):
"""Dataset split configuration."""
train_ratio: float = Field(
default=0.8,
ge=0.1,
le=0.9,
description="Ratio of data for training",
)
validation_ratio: float = Field(
default=0.1,
ge=0.05,
le=0.3,
description="Ratio of data for validation",
)
test_ratio: float = Field(
default=0.1,
ge=0.05,
le=0.3,
description="Ratio of data for testing",
)
@field_validator("test_ratio")
@classmethod
def validate_ratios_sum_to_one(cls, v, info):
"""Validate that all ratios sum to 1.0."""
if hasattr(info, "data"):
train_ratio = info.data.get("train_ratio", 0.8)
validation_ratio = info.data.get("validation_ratio", 0.1)
total = train_ratio + validation_ratio + v
if abs(total - 1.0) > 0.001:
raise ValueError("Train, validation, and test ratios must sum to 1.0")
return v
class TrainingConfig(BaseModel):
"""Configuration for training job."""
# Model configuration
model_name: str = Field(
...,
description="Base model name or path",
min_length=1,
max_length=200,
)
model_type: ModelType = Field(
default=ModelType.LLM,
description="Type of model to train",
)
# Training strategy
strategy: TrainingStrategy = Field(
default=TrainingStrategy.SUPERVISED,
description="Training strategy to use",
)
# Dataset configuration
min_quality_score: float = Field(
default=0.7,
ge=0.0,
le=1.0,
description="Minimum quality score for training data",
)
require_feedback: bool = Field(
default=True,
description="Only use responses with human feedback",
)
feedback_types: List[str] = Field(
default=["good"],
description="Feedback types to include in training",
)
max_toxicity_score: float = Field(
default=0.3,
ge=0.0,
le=1.0,
description="Maximum toxicity score for training data",
)
dataset_split: DatasetSplit = Field(
default_factory=DatasetSplit,
description="Dataset split configuration",
)
# Training hyperparameters
learning_rate: float = Field(
default=2e-5,
ge=1e-6,
le=1e-3,
description="Learning rate for training",
)
batch_size: int = Field(
default=8,
ge=1,
le=128,
description="Training batch size",
)
gradient_accumulation_steps: int = Field(
default=4,
ge=1,
le=32,
description="Gradient accumulation steps",
)
num_epochs: int = Field(
default=3,
ge=1,
le=20,
description="Number of training epochs",
)
max_length: int = Field(
default=512,
ge=128,
le=2048,
description="Maximum sequence length",
)
warmup_steps: int = Field(
default=100,
ge=0,
le=1000,
description="Number of warmup steps",
)
weight_decay: float = Field(
default=0.01,
ge=0.0,
le=0.1,
description="Weight decay for regularization",
)
# Training options
use_lora: bool = Field(
default=True,
description="Use LoRA (Low-Rank Adaptation) for efficient fine-tuning",
)
lora_rank: int = Field(
default=16,
ge=4,
le=128,
description="LoRA rank parameter",
)
lora_alpha: int = Field(
default=32,
ge=8,
le=256,
description="LoRA alpha parameter",
)
use_mixed_precision: bool = Field(
default=True,
description="Use mixed precision training",
)
save_steps: int = Field(
default=500,
ge=50,
le=5000,
description="Save checkpoint every N steps",
)
eval_steps: int = Field(
default=100,
ge=10,
le=1000,
description="Evaluate every N steps",
)
# Experiment tracking
experiment_name: Optional[str] = Field(
None,
max_length=100,
description="Name for experiment tracking",
)
tags: List[str] = Field(
default_factory=list,
description="Tags for organizing experiments",
)
model_config = {
"json_schema_extra": {
"example": {
"model_name": "microsoft/DialoGPT-small",
"model_type": "llm",
"strategy": "supervised",
"min_quality_score": 0.8,
"require_feedback": True,
"feedback_types": ["good"],
"learning_rate": 2e-5,
"batch_size": 8,
"num_epochs": 3,
"use_lora": True,
"experiment_name": "quality-improvement-v1",
}
}
}
class TrainingJobRequest(BaseModel):
"""Request to start a training job."""
config: TrainingConfig = Field(..., description="Training configuration")
description: Optional[str] = Field(
None,
max_length=500,
description="Description of the training job",
)
model_config = {
"json_schema_extra": {
"example": {
"config": {
"model_name": "microsoft/DialoGPT-small",
"strategy": "supervised",
"learning_rate": 2e-5,
"batch_size": 8,
"num_epochs": 3,
},
"description": "Fine-tune model on high-quality responses",
}
}
}
class TrainingMetrics(BaseModel):
"""Training metrics and statistics."""
# Training progress
current_epoch: int = Field(..., description="Current training epoch")
total_epochs: int = Field(..., description="Total number of epochs")
current_step: int = Field(..., description="Current training step")
total_steps: int = Field(..., description="Total number of steps")
progress_percentage: float = Field(
..., ge=0.0, le=100.0, description="Training progress percentage"
)
# Loss metrics
train_loss: Optional[float] = Field(None, description="Current training loss")
eval_loss: Optional[float] = Field(None, description="Current evaluation loss")
best_eval_loss: Optional[float] = Field(None, description="Best evaluation loss so far")
# Performance metrics
learning_rate: Optional[float] = Field(None, description="Current learning rate")
grad_norm: Optional[float] = Field(None, description="Gradient norm")
examples_per_second: Optional[float] = Field(None, description="Training speed")
# Time metrics
elapsed_time: Optional[float] = Field(None, description="Elapsed time in seconds")
estimated_remaining: Optional[float] = Field(
None, description="Estimated remaining time in seconds"
)
model_config = {
"json_schema_extra": {
"example": {
"current_epoch": 2,
"total_epochs": 3,
"current_step": 450,
"total_steps": 600,
"progress_percentage": 75.0,
"train_loss": 0.85,
"eval_loss": 0.92,
"best_eval_loss": 0.89,
"learning_rate": 1.5e-5,
"examples_per_second": 12.5,
"elapsed_time": 1800.0,
"estimated_remaining": 600.0,
}
}
}
class EvaluationResult(BaseModel):
"""Results from model evaluation."""
# Standard metrics
perplexity: Optional[float] = Field(None, description="Model perplexity")
bleu_score: Optional[float] = Field(None, description="BLEU score")
rouge_l: Optional[float] = Field(None, description="ROUGE-L score")
# Custom metrics
avg_quality_score: Optional[float] = Field(None, description="Average quality score")
avg_toxicity_score: Optional[float] = Field(None, description="Average toxicity score")
response_length_avg: Optional[float] = Field(None, description="Average response length")
# Sample evaluations
sample_inputs: List[str] = Field(default_factory=list, description="Sample input messages")
sample_outputs: List[str] = Field(default_factory=list, description="Sample generated outputs")
sample_scores: List[float] = Field(default_factory=list, description="Sample quality scores")
model_config = {
"json_schema_extra": {
"example": {
"perplexity": 15.2,
"bleu_score": 0.65,
"rouge_l": 0.72,
"avg_quality_score": 0.83,
"avg_toxicity_score": 0.05,
"response_length_avg": 45.2,
"sample_inputs": ["How to set up a bot?"],
"sample_outputs": ["To set up a bot, follow these steps..."],
"sample_scores": [0.9],
}
}
}
class TrainingJob(BaseModel):
"""Training job information."""
id: UUID = Field(..., description="Training job ID")
status: TrainingStatus = Field(..., description="Current job status")
config: TrainingConfig = Field(..., description="Training configuration")
description: Optional[str] = Field(None, description="Job description")
# Timestamps
created_at: datetime = Field(..., description="Job creation time")
started_at: Optional[datetime] = Field(None, description="Job start time")
completed_at: Optional[datetime] = Field(None, description="Job completion time")
# Progress and metrics
metrics: Optional[TrainingMetrics] = Field(None, description="Training metrics")
evaluation: Optional[EvaluationResult] = Field(None, description="Evaluation results")
# Output information
model_path: Optional[str] = Field(None, description="Path to trained model")
model_version: Optional[str] = Field(None, description="Model version identifier")
logs_path: Optional[str] = Field(None, description="Path to training logs")
# Error information
error_message: Optional[str] = Field(None, description="Error message if failed")
error_details: Optional[Dict[str, Any]] = Field(None, description="Detailed error information")
model_config = {
"from_attributes": True,
"json_schema_extra": {
"example": {
"id": "123e4567-e89b-12d3-a456-426614174000",
"status": "running",
"config": {
"model_name": "microsoft/DialoGPT-small",
"strategy": "supervised",
"learning_rate": 2e-5,
},
"description": "Quality improvement training",
"created_at": "2025-01-07T10:00:00Z",
"started_at": "2025-01-07T10:05:00Z",
"model_version": "v1.2.0",
}
},
}
class TrainingJobList(BaseModel):
"""List of training jobs."""
jobs: List[TrainingJob] = Field(..., description="List of training jobs")
total: int = Field(..., description="Total number of jobs")
limit: int = Field(..., description="Limit used in query")
offset: int = Field(..., description="Offset used in query")
model_config = {
"json_schema_extra": {
"example": {
"jobs": [],
"total": 15,
"limit": 10,
"offset": 0,
}
}
}
class ModelDeployRequest(BaseModel):
"""Request to deploy a trained model."""
job_id: UUID = Field(..., description="Training job ID")
model_type: ModelType = Field(..., description="Type of model to deploy")
set_as_default: bool = Field(default=True, description="Set as default model for the service")
backup_current: bool = Field(default=True, description="Backup current model before deployment")
model_config = {
"json_schema_extra": {
"example": {
"job_id": "123e4567-e89b-12d3-a456-426614174000",
"model_type": "llm",
"set_as_default": True,
"backup_current": True,
}
}
}
class ModelDeployResponse(BaseModel):
"""Response from model deployment."""
success: bool = Field(..., description="Whether deployment was successful")
model_version: str = Field(..., description="Deployed model version")
previous_version: Optional[str] = Field(None, description="Previous model version")
backup_path: Optional[str] = Field(None, description="Path to backup model")
message: str = Field(..., description="Deployment status message")
model_config = {
"json_schema_extra": {
"example": {
"success": True,
"model_version": "v1.2.0",
"previous_version": "v1.1.5",
"backup_path": "/models/backups/llm_v1.1.5",
"message": "Model deployed successfully",
}
}
}
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