FusionAGI/tests/test_gpu_training.py

"""Tests for fusionagi.gpu.training and self_improvement.gpu_training."""

import pytest

from fusionagi.gpu.backend import get_backend, reset_backend
from fusionagi.gpu.training import (
    TrainingConfig,
    optimize_heuristic_weights,
    prepare_training_pairs,
    run_gpu_training,
)
from fusionagi.self_improvement.gpu_training import (
    can_gpu_train,
    run_gpu_enhanced_training,
)


@pytest.fixture(autouse=True)
def _use_numpy():
    reset_backend()
    get_backend(force="numpy")
    yield
    reset_backend()


class FakeReflectiveMemory:
    """Fake reflective memory for testing."""

    def __init__(self, lessons: list | None = None):
        self._lessons = lessons or []
        self._heuristics: dict = {}

    def get_lessons(self, limit: int = 50) -> list:
        return self._lessons[:limit]

    def get_all_heuristics(self) -> dict:
        return dict(self._heuristics)

    def set_heuristic(self, key: str, value) -> None:
        self._heuristics[key] = value


class TestPrepareTrainingPairs:
    def test_empty(self):
        be = get_backend()
        inputs, targets = prepare_training_pairs([], backend=be)
        assert be.to_numpy(inputs).shape[0] == 0

    def test_basic(self):
        be = get_backend()
        lessons = [
            {"task_id": "t1", "outcome": "success", "evaluation": {"score": 0.9}},
            {"task_id": "t2", "outcome": "failed", "evaluation": {"score": 0.2}},
        ]
        inputs, targets = prepare_training_pairs(lessons, backend=be)
        inputs_np = be.to_numpy(inputs)
        targets_np = be.to_numpy(targets)
        assert inputs_np.shape[0] == 2
        assert targets_np.shape == (2,)
        assert abs(targets_np[0] - 0.9) < 1e-5
        assert abs(targets_np[1] - 0.2) < 1e-5


class TestOptimizeHeuristicWeights:
    def test_empty_data(self):
        be = get_backend()
        import numpy as np
        inputs = be.from_numpy(np.zeros((0, 256), dtype=np.float32))
        targets = be.from_numpy(np.zeros(0, dtype=np.float32))
        result = optimize_heuristic_weights(inputs, targets, backend=be)
        assert result.metadata.get("reason") == "no training data"

    def test_basic_training(self):
        be = get_backend()
        import numpy as np
        np.random.seed(42)
        inputs = be.from_numpy(np.random.randn(10, 256).astype(np.float32))
        targets = be.from_numpy(np.random.rand(10).astype(np.float32))
        config = TrainingConfig(epochs=5, learning_rate=0.001)
        result = optimize_heuristic_weights(inputs, targets, config=config, backend=be)
        assert result.epochs_run == 5
        assert result.weights_updated == 256
        assert result.metadata["backend"] == "numpy"

    def test_loss_decreases(self):
        be = get_backend()
        import numpy as np
        np.random.seed(42)
        inputs = be.from_numpy(np.random.randn(50, 256).astype(np.float32))
        targets = be.from_numpy(np.random.rand(50).astype(np.float32))
        config = TrainingConfig(epochs=20, learning_rate=0.01)
        result = optimize_heuristic_weights(inputs, targets, config=config, backend=be)
        # Loss should generally decrease with training
        assert result.final_loss <= result.initial_loss + 0.5


class TestRunGPUTraining:
    def test_no_lessons(self):
        mem = FakeReflectiveMemory(lessons=[])
        result = run_gpu_training(mem)
        assert result.metadata.get("reason") == "no lessons available"

    def test_with_lessons(self):
        lessons = [
            {"task_id": f"t{i}", "outcome": "ok", "evaluation": {"score": 0.5 + i * 0.1}}
            for i in range(5)
        ]
        mem = FakeReflectiveMemory(lessons=lessons)
        config = TrainingConfig(epochs=3)
        result = run_gpu_training(mem, config=config)
        assert result.epochs_run == 3


class TestSelfImprovementGPUTraining:
    def test_can_gpu_train(self):
        assert can_gpu_train() is True

    def test_run_enhanced_training_empty(self):
        mem = FakeReflectiveMemory(lessons=[])
        result = run_gpu_enhanced_training(mem, epochs=3)
        assert result.get("gpu_accelerated") is True or "reason" in result

    def test_run_enhanced_training_with_data(self):
        lessons = [
            {"task_id": "t1", "outcome": "ok", "evaluation": {"score": 0.8}},
            {"task_id": "t2", "outcome": "fail", "evaluation": {"score": 0.3}},
        ]
        mem = FakeReflectiveMemory(lessons=lessons)
        result = run_gpu_enhanced_training(mem, epochs=3)
        assert result.get("gpu_accelerated") is True
        assert "gpu_training_last_loss" in mem.get_all_heuristics()