chore: auto-commit (95 archivos)

- cmd/fn/doctor.go - cmd/fn/main.go - cpp/apps/primitives_gallery/playground/tables/CMakeLists.txt - cpp/apps/primitives_gallery/playground/tables/data_table.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.h - cpp/apps/primitives_gallery/playground/tables/self_test.cpp - cpp/apps/primitives_gallery/playground/tables/tql.cpp - cpp/apps/primitives_gallery/playground/tables/viz.cpp - cpp/apps/primitives_gallery/playground/tables/viz.h - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-13 00:50:34 +02:00
parent ef60449e64
commit a802f59f55
189 changed files with 18964 additions and 330 deletions
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+"""Tests para el backend diffusers: load_pipeline, set_scheduler, generate, unload."""
+
+from __future__ import annotations
+
+import sys
+import os
+import time
+
+import pytest
+
+# Ajustar path para importar desde python/functions/ml/
+_ML_PATH = os.path.join(
+    os.path.dirname(__file__), "..", "..",
+)
+sys.path.insert(0, os.path.abspath(_ML_PATH))
+
+# Importaciones lazy de torch y diffusers — las omitimos si no estan disponibles.
+torch = pytest.importorskip("torch", reason="torch no instalado — skip tests diffusers")
+pytest.importorskip("diffusers", reason="diffusers no instalado — skip tests diffusers")
+
+from ml.model_ref import ModelRef
+from ml.generation_config import GenerationConfig
+from ml.image_gen_result import ImageGenResult
+from ml.diffusers_load_pipeline import diffusers_load_pipeline, _clear_pipeline_cache
+from ml.diffusers_set_scheduler import diffusers_set_scheduler
+from ml.diffusers_unload import diffusers_unload
+
+# diffusers_generate importa image_gen_result sin prefijo de paquete.
+# Para evitar el double-import problem (ml.image_gen_result != image_gen_result),
+# forzamos que sys.modules["image_gen_result"] apunte al modulo ya cargado
+# como ml.image_gen_result antes de importar diffusers_generate.
+import sys as _sys
+import ml.image_gen_result as _igr_module
+import ml.generation_config as _gcfg_module
+import ml.genconfig_to_diffusers_kwargs as _gkwargs_module
+for _alias, _mod in [
+    ("image_gen_result", _igr_module),
+    ("generation_config", _gcfg_module),
+    ("genconfig_to_diffusers_kwargs", _gkwargs_module),
+]:
+    if _alias not in _sys.modules:
+        _sys.modules[_alias] = _mod
+
+from ml.diffusers_generate import diffusers_generate
+
+
+# ---------------------------------------------------------------------------
+# Constantes
+# ---------------------------------------------------------------------------
+SD_TURBO_PATH = "/home/lucas/vaults/imagegen_models/diffusers/sd-turbo"
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope="session")
+def sd_turbo_model() -> ModelRef:
+    """ModelRef apuntando a SD Turbo local."""
+    if not os.path.isdir(SD_TURBO_PATH):
+        pytest.skip(f"SD Turbo no encontrado en {SD_TURBO_PATH}")
+    return ModelRef(
+        name="sd-turbo",
+        model_type="sd15",
+        quantization="fp16",
+        path=SD_TURBO_PATH,
+    )
+
+
+@pytest.fixture(scope="session")
+def loaded_pipe(sd_turbo_model: ModelRef):
+    """Pipeline SD Turbo cargado una sola vez para toda la sesion de tests."""
+    # Intentar fp16 primero; si falla (no hay variante fp16) usar fp32
+    try:
+        pipe = diffusers_load_pipeline(sd_turbo_model, device="auto", dtype="fp16")
+    except Exception:
+        _clear_pipeline_cache()
+        pipe = diffusers_load_pipeline(sd_turbo_model, device="auto", dtype="fp32")
+    yield pipe
+    # Teardown: liberar al final de la sesion
+    diffusers_unload(None)
+
+
+@pytest.fixture(scope="session")
+def sd_turbo_cfg(sd_turbo_model: ModelRef) -> GenerationConfig:
+    """GenerationConfig minimo para SD Turbo (1 step, 512x512)."""
+    return GenerationConfig(
+        prompt="a simple red circle on white background",
+        negative_prompt=None,
+        seed=42,
+        steps=1,
+        cfg_scale=0.0,
+        sampler="euler",
+        width=512,
+        height=512,
+        model=sd_turbo_model,
+        loras=[],
+    )
+
+
+# ---------------------------------------------------------------------------
+# Test: carga pipeline y retorna callable
+# ---------------------------------------------------------------------------
+
+def test_load_pipeline_returns_callable(sd_turbo_model: ModelRef) -> None:
+    """carga pipeline y retorna callable"""
+    _clear_pipeline_cache()
+    pipe = diffusers_load_pipeline(sd_turbo_model, device="auto", dtype="fp16")
+    assert callable(pipe), "El pipeline debe ser callable"
+    assert hasattr(pipe, "scheduler"), "El pipeline debe tener atributo scheduler"
+
+
+# ---------------------------------------------------------------------------
+# Test: segunda carga usa cache (< 100ms)
+# ---------------------------------------------------------------------------
+
+def test_load_pipeline_caches(sd_turbo_model: ModelRef) -> None:
+    """segunda carga usa cache (< 100ms)"""
+    # Primera carga (puede tardar varios segundos)
+    _clear_pipeline_cache()
+    _ = diffusers_load_pipeline(sd_turbo_model, device="auto", dtype="fp16")
+
+    # Segunda carga debe ser cache hit
+    t0 = time.perf_counter()
+    pipe2 = diffusers_load_pipeline(sd_turbo_model, device="auto", dtype="fp16")
+    elapsed_ms = (time.perf_counter() - t0) * 1000
+
+    assert elapsed_ms < 100, (
+        f"Segunda carga tardo {elapsed_ms:.1f}ms (esperado < 100ms — debe ser cache hit)"
+    )
+    assert pipe2 is not None
+
+
+# ---------------------------------------------------------------------------
+# Test: set_scheduler cambia la clase del scheduler
+# ---------------------------------------------------------------------------
+
+def test_set_scheduler_changes_scheduler_class(loaded_pipe) -> None:
+    """euler cambia scheduler a EulerDiscreteScheduler"""
+    pipe = diffusers_set_scheduler(loaded_pipe, "euler")
+    scheduler_name = type(pipe.scheduler).__name__
+    assert scheduler_name == "EulerDiscreteScheduler", (
+        f"Esperado EulerDiscreteScheduler, obtenido {scheduler_name}"
+    )
+
+
+def test_set_scheduler_euler_a(loaded_pipe) -> None:
+    """euler_a cambia scheduler a EulerAncestralDiscreteScheduler"""
+    pipe = diffusers_set_scheduler(loaded_pipe, "euler_a")
+    scheduler_name = type(pipe.scheduler).__name__
+    assert scheduler_name == "EulerAncestralDiscreteScheduler", (
+        f"Esperado EulerAncestralDiscreteScheduler, obtenido {scheduler_name}"
+    )
+    # Restaurar euler para no afectar otros tests
+    diffusers_set_scheduler(loaded_pipe, "euler")
+
+
+def test_set_scheduler_invalid_raises_value_error(loaded_pipe) -> None:
+    """sampler invalido lanza ValueError"""
+    with pytest.raises(ValueError, match="no soportado"):
+        diffusers_set_scheduler(loaded_pipe, "nonexistent_sampler_xyz")
+
+
+# ---------------------------------------------------------------------------
+# Test: genera imagen retorna ImageGenResult
+# ---------------------------------------------------------------------------
+
+def test_generate_returns_image_gen_result(
+    loaded_pipe, sd_turbo_cfg: GenerationConfig
+) -> None:
+    """genera imagen retorna ImageGenResult"""
+    result = diffusers_generate(loaded_pipe, sd_turbo_cfg)
+
+    assert isinstance(result, ImageGenResult), (
+        f"Esperado ImageGenResult, obtenido {type(result)}"
+    )
+    assert result.image is not None, "result.image no debe ser None"
+    assert result.duration_ms > 0, (
+        f"duration_ms debe ser positivo, obtenido {result.duration_ms}"
+    )
+    assert "backend" in result.meta, "meta debe tener key 'backend'"
+    assert result.meta["backend"] == "diffusers", (
+        f"meta['backend'] debe ser 'diffusers', obtenido {result.meta['backend']}"
+    )
+    assert "model" in result.meta, "meta debe tener key 'model'"
+
+    # Verificar que la imagen tiene las dimensiones correctas
+    w, h = result.image.size
+    assert w == sd_turbo_cfg.width and h == sd_turbo_cfg.height, (
+        f"Imagen esperada {sd_turbo_cfg.width}x{sd_turbo_cfg.height}, "
+        f"obtenida {w}x{h}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Test: unload limpia cache cuda si disponible
+# ---------------------------------------------------------------------------
+
+def test_unload_clears_cuda() -> None:
+    """unload None limpia cache cuda si disponible"""
+    cuda_available = torch.cuda.is_available()
+
+    # Limpiar cache — no debe lanzar excepcion independientemente de si hay CUDA
+    diffusers_unload(None)
+
+    if cuda_available:
+        # Despues de empty_cache, la memoria reservada por el allocator baja
+        # No podemos asumir que sea 0 (otros tensores pueden estar vivos),
+        # pero la llamada debe completarse sin error.
+        reserved = torch.cuda.memory_reserved()
+        # Solo verificamos que no lanza excepcion y que la llamada completo
+        assert reserved >= 0, "memory_reserved debe ser >= 0"