training/curriculum.py

"""
Curriculum learning for Vortex model.
Progresses through stages: Foundation → Domain → Reasoning → Integration.
"""

from typing import List, Dict, Optional
import torch


class CurriculumScheduler:
    """
    Schedules curriculum stages during training.
    Each stage has a start and end fraction of total training steps.
    """

    STAGES = ["foundation", "domain", "reasoning", "integration"]

    def __init__(
        self,
        config: Dict,
        total_steps: int,
    ):
        """
        Initialize curriculum scheduler.

        Args:
            config: Training config with curriculum_stages
            total_steps: Total number of training steps
        """
        self.config = config
        self.total_steps = total_steps
        self.stages = config.get("curriculum_stages", [
            {"name": "foundation", "start": 0.0, "end": 0.2},
            {"name": "domain", "start": 0.2, "end": 0.5},
            {"name": "reasoning", "start": 0.5, "end": 0.8},
            {"name": "integration", "start": 0.8, "end": 1.0},
        ])

        # Convert fractions to step numbers
        for stage in self.stages:
            stage["start_step"] = int(stage["start"] * total_steps)
            stage["end_step"] = int(stage["end"] * total_steps)

    def get_stage(
        self,
        current_step: int,
    ) -> Optional[Dict]:
        """
        Get current curriculum stage.

        Args:
            current_step: Current training step

        Returns:
            Stage dictionary or None if training complete
        """
        for stage in self.stages:
            if stage["start_step"] <= current_step < stage["end_step"]:
                return stage
        return None

    def get_stage_name(self, current_step: int) -> str:
        """Get current stage name."""
        stage = self.get_stage(current_step)
        return stage["name"] if stage else "complete"

    def get_stage_weight(
        self,
        current_step: int,
        base_weight: float,
    ) -> float:
        """
        Get weight for a curriculum component based on stage.

        Args:
            current_step: Current training step
            base_weight: Base weight for the component
        Returns:
            Adjusted weight (can be 0 if component not active in current stage)
        """
        stage = self.get_stage(current_step)
        if not stage:
            return 0.0

        stage_name = stage["name"]

        # Define which components are active in each stage
        stage_components = {
            "foundation": ["lm_loss"],  # Only language modeling
            "domain": ["lm_loss", "equation_loss", "domain_loss"],
            "reasoning": ["lm_loss", "equation_loss", "domain_loss", "citation_loss"],
            "integration": ["lm_loss", "equation_loss", "domain_loss", "citation_loss", "numerical_loss"],
        }

        active_components = stage_components.get(stage_name, ["lm_loss"])

        # Return base weight if component active, else 0
        # (Caller checks if their component is in active_components)
        return base_weight if "lm_loss" in active_components else 0.0

    def get_dataset_sampler(
        self,
        current_step: int,
    ):
        """
        Get dataset sampler for current stage.
        Different stages may mix datasets differently.

        Returns:
            Sampler weights for different datasets
        """
        stage = self.get_stage(current_step)
        if not stage:
            return None

        stage_name = stage["name"]

        # Dataset mixing proportions per stage
        mixing_proportions = {
            "foundation": {
                "pile_scientific": 0.3,
                "s2orc": 0.3,
                "automath": 0.2,
                "pubmed_qa": 0.2,
            },
            "domain": {
                "pile_scientific": 0.2,
                "s2orc": 0.2,
                "automath": 0.2,
                "pubmed_qa": 0.2,
                "deepmind_math": 0.2,
            },
            "reasoning": {
                "pile_scientific": 0.15,
                "s2orc": 0.15,
                "automath": 0.3,
                "deepmind_math": 0.3,
                "pubmed_qa": 0.1,
            },
            "integration": {
                "pile_scientific": 0.2,
                "s2orc": 0.2,
                "automath": 0.2,
                "deepmind_math": 0.2,
                "pubmed_qa": 0.2,
            },
        }

        return mixing_proportions.get(stage_name, {"pile_scientific": 1.0})


def test_curriculum():
    """Test curriculum scheduler."""
    config = {
        "curriculum_stages": [
            {"name": "foundation", "start": 0.0, "end": 0.2},
            {"name": "domain", "start": 0.2, "end": 0.5},
            {"name": "reasoning", "start": 0.5, "end": 0.8},
            {"name": "integration", "start": 0.8, "end": 1.0},
        ]
    }

    total_steps = 1000
    scheduler = CurriculumScheduler(config, total_steps)

    for step in [0, 100, 250, 500, 750, 999]:
        stage = scheduler.get_stage(step)
        name = scheduler.get_stage_name(step)
        print(f"Step {step}: {name}")

    print("Curriculum test passed!")


if __name__ == "__main__":
    test_curriculum()