[Refactor] Move testing utils out of src (#12238)

* update

* update

* update

* update

* update

* merge main

* Revert "merge main"

This reverts commit 65efbce.

Author: DN6

examples/conftest.py

@@ -25,20 +25,25 @@
 git_repo_path = abspath(join(dirname(dirname(dirname(__file__))), "src"))
 sys.path.insert(1, git_repo_path)
 
+# Add parent directory to path so we can import from tests
+repo_root = abspath(dirname(dirname(__file__)))
+if repo_root not in sys.path:
+    sys.path.insert(0, repo_root)
+
 
 # silence FutureWarning warnings in tests since often we can't act on them until
 # they become normal warnings - i.e. the tests still need to test the current functionality
 warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_addoption(parser):
-    from diffusers.utils.testing_utils import pytest_addoption_shared
+    from tests.testing_utils import pytest_addoption_shared
 
     pytest_addoption_shared(parser)
 
 
 def pytest_terminal_summary(terminalreporter):
-    from diffusers.utils.testing_utils import pytest_terminal_summary_main
+    from tests.testing_utils import pytest_terminal_summary_main
 
     make_reports = terminalreporter.config.getoption("--make-reports")
     if make_reports:

examples/controlnet/train_controlnet_sd3.py

@@ -24,6 +24,8 @@
 import os
 import random
 import shutil
+
+# Add repo root to path to import from tests
 from pathlib import Path
 
 import accelerate
@@ -54,8 +56,7 @@
 from diffusers.training_utils import compute_density_for_timestep_sampling, compute_loss_weighting_for_sd3, free_memory
 from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
-from diffusers.utils.testing_utils import backend_empty_cache
-from diffusers.utils.torch_utils import is_compiled_module
+from diffusers.utils.torch_utils import backend_empty_cache, is_compiled_module
 
 
 if is_wandb_available():

examples/vqgan/test_vqgan.py

@@ -24,12 +24,18 @@
 import torch
 
 from diffusers import VQModel
-from diffusers.utils.testing_utils import require_timm
 
 
+# Add parent directories to path to import from tests
 sys.path.append("..")
+repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+if repo_root not in sys.path:
+    sys.path.insert(0, repo_root)
+
 from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
 
+from tests.testing_utils import require_timm  # noqa
+
 
 logging.basicConfig(level=logging.DEBUG)
 

src/diffusers/utils/testing_utils.py

@@ -66,7 +66,10 @@
 global_rng = random.Random()
 
 logger = get_logger(__name__)
-
+logger.warning(
+    "diffusers.utils.testing_utils' is deprecated and will be removed in a future version. "
+    "Determinism and device backend utilities have been moved to `diffusers.utils.torch_utils`. "
+)
 _required_peft_version = is_peft_available() and version.parse(
     version.parse(importlib.metadata.version("peft")).base_version
 ) > version.parse("0.5")
@@ -801,10 +804,9 @@ def export_to_ply(mesh, output_ply_path: str = None):
                 f.write(format.pack(*vertex))
 
         if faces is not None:
-            format = struct.Struct("<B3I")
             for tri in faces.tolist():
                 f.write(format.pack(len(tri), *tri))
-
+            format = struct.Struct("<B3I")
     return output_ply_path
 
 
@@ -1144,23 +1146,23 @@ def enable_full_determinism():
     Helper function for reproducible behavior during distributed training. See
     - https://pytorch.org/docs/stable/notes/randomness.html for pytorch
     """
-    #  Enable PyTorch deterministic mode. This potentially requires either the environment
-    #  variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
-    # depending on the CUDA version, so we set them both here
-    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
-    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
-    torch.use_deterministic_algorithms(True)
+    from .torch_utils import enable_full_determinism as _enable_full_determinism
 
-    # Enable CUDNN deterministic mode
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-    torch.backends.cuda.matmul.allow_tf32 = False
+    logger.warning(
+        "enable_full_determinism has been moved to diffusers.utils.torch_utils. "
+        "Importing from diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _enable_full_determinism()
 
 
 def disable_full_determinism():
-    os.environ["CUDA_LAUNCH_BLOCKING"] = "0"
-    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ""
-    torch.use_deterministic_algorithms(False)
+    from .torch_utils import disable_full_determinism as _disable_full_determinism
+
+    logger.warning(
+        "disable_full_determinism has been moved to diffusers.utils.torch_utils. "
+        "Importing from diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _disable_full_determinism()
 
 
 # Utils for custom and alternative accelerator devices
@@ -1282,43 +1284,85 @@ def _device_agnostic_dispatch(device: str, dispatch_table: Dict[str, Callable],
 
 # These are callables which automatically dispatch the function specific to the accelerator
 def backend_manual_seed(device: str, seed: int):
-    return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)
+    from .torch_utils import backend_manual_seed as _backend_manual_seed
+
+    logger.warning(
+        "backend_manual_seed has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_manual_seed(device, seed)
 
 
 def backend_synchronize(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_SYNCHRONIZE)
+    from .torch_utils import backend_synchronize as _backend_synchronize
+
+    logger.warning(
+        "backend_synchronize has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_synchronize(device)
 
 
 def backend_empty_cache(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)
+    from .torch_utils import backend_empty_cache as _backend_empty_cache
+
+    logger.warning(
+        "backend_empty_cache has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_empty_cache(device)
 
 
 def backend_device_count(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)
+    from .torch_utils import backend_device_count as _backend_device_count
+
+    logger.warning(
+        "backend_device_count has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_device_count(device)
 
 
 def backend_reset_peak_memory_stats(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_RESET_PEAK_MEMORY_STATS)
+    from .torch_utils import backend_reset_peak_memory_stats as _backend_reset_peak_memory_stats
+
+    logger.warning(
+        "backend_reset_peak_memory_stats has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_reset_peak_memory_stats(device)
 
 
 def backend_reset_max_memory_allocated(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_RESET_MAX_MEMORY_ALLOCATED)
+    from .torch_utils import backend_reset_max_memory_allocated as _backend_reset_max_memory_allocated
+
+    logger.warning(
+        "backend_reset_max_memory_allocated has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_reset_max_memory_allocated(device)
 
 
 def backend_max_memory_allocated(device: str):
-    return _device_agnostic_dispatch(device, BACKEND_MAX_MEMORY_ALLOCATED)
+    from .torch_utils import backend_max_memory_allocated as _backend_max_memory_allocated
+
+    logger.warning(
+        "backend_max_memory_allocated has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_max_memory_allocated(device)
 
 
 # These are callables which return boolean behaviour flags and can be used to specify some
 # device agnostic alternative where the feature is unsupported.
 def backend_supports_training(device: str):
-    if not is_torch_available():
-        return False
-
-    if device not in BACKEND_SUPPORTS_TRAINING:
-        device = "default"
+    from .torch_utils import backend_supports_training as _backend_supports_training
 
-    return BACKEND_SUPPORTS_TRAINING[device]
+    logger.warning(
+        "backend_supports_training has been moved to diffusers.utils.torch_utils. "
+        "diffusers.utils.testing_utils is deprecated and will be removed in a future version."
+    )
+    return _backend_supports_training(device)
 
 
 # Guard for when Torch is not available

src/diffusers/utils/torch_utils.py

@@ -16,7 +16,8 @@
 """
 
 import functools
-from typing import List, Optional, Tuple, Union
+import os
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 from . import logging
 from .import_utils import is_torch_available, is_torch_npu_available, is_torch_version
@@ -26,6 +27,56 @@
     import torch
     from torch.fft import fftn, fftshift, ifftn, ifftshift
 
+    BACKEND_SUPPORTS_TRAINING = {"cuda": True, "xpu": True, "cpu": True, "mps": False, "default": True}
+    BACKEND_EMPTY_CACHE = {
+        "cuda": torch.cuda.empty_cache,
+        "xpu": torch.xpu.empty_cache,
+        "cpu": None,
+        "mps": torch.mps.empty_cache,
+        "default": None,
+    }
+    BACKEND_DEVICE_COUNT = {
+        "cuda": torch.cuda.device_count,
+        "xpu": torch.xpu.device_count,
+        "cpu": lambda: 0,
+        "mps": lambda: 0,
+        "default": 0,
+    }
+    BACKEND_MANUAL_SEED = {
+        "cuda": torch.cuda.manual_seed,
+        "xpu": torch.xpu.manual_seed,
+        "cpu": torch.manual_seed,
+        "mps": torch.mps.manual_seed,
+        "default": torch.manual_seed,
+    }
+    BACKEND_RESET_PEAK_MEMORY_STATS = {
+        "cuda": torch.cuda.reset_peak_memory_stats,
+        "xpu": getattr(torch.xpu, "reset_peak_memory_stats", None),
+        "cpu": None,
+        "mps": None,
+        "default": None,
+    }
+    BACKEND_RESET_MAX_MEMORY_ALLOCATED = {
+        "cuda": torch.cuda.reset_max_memory_allocated,
+        "xpu": getattr(torch.xpu, "reset_peak_memory_stats", None),
+        "cpu": None,
+        "mps": None,
+        "default": None,
+    }
+    BACKEND_MAX_MEMORY_ALLOCATED = {
+        "cuda": torch.cuda.max_memory_allocated,
+        "xpu": getattr(torch.xpu, "max_memory_allocated", None),
+        "cpu": 0,
+        "mps": 0,
+        "default": 0,
+    }
+    BACKEND_SYNCHRONIZE = {
+        "cuda": torch.cuda.synchronize,
+        "xpu": getattr(torch.xpu, "synchronize", None),
+        "cpu": None,
+        "mps": None,
+        "default": None,
+    }
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 try:
@@ -36,6 +87,62 @@ def maybe_allow_in_graph(cls):
         return cls
 
 
+# This dispatches a defined function according to the accelerator from the function definitions.
+def _device_agnostic_dispatch(device: str, dispatch_table: Dict[str, Callable], *args, **kwargs):
+    if device not in dispatch_table:
+        return dispatch_table["default"](*args, **kwargs)
+
+    fn = dispatch_table[device]
+
+    # Some device agnostic functions return values. Need to guard against 'None' instead at
+    # user level
+    if not callable(fn):
+        return fn
+
+    return fn(*args, **kwargs)
+
+
+# These are callables which automatically dispatch the function specific to the accelerator
+def backend_manual_seed(device: str, seed: int):
+    return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)
+
+
+def backend_synchronize(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_SYNCHRONIZE)
+
+
+def backend_empty_cache(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)
+
+
+def backend_device_count(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)
+
+
+def backend_reset_peak_memory_stats(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_RESET_PEAK_MEMORY_STATS)
+
+
+def backend_reset_max_memory_allocated(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_RESET_MAX_MEMORY_ALLOCATED)
+
+
+def backend_max_memory_allocated(device: str):
+    return _device_agnostic_dispatch(device, BACKEND_MAX_MEMORY_ALLOCATED)
+
+
+# These are callables which return boolean behaviour flags and can be used to specify some
+# device agnostic alternative where the feature is unsupported.
+def backend_supports_training(device: str):
+    if not is_torch_available():
+        return False
+
+    if device not in BACKEND_SUPPORTS_TRAINING:
+        device = "default"
+
+    return BACKEND_SUPPORTS_TRAINING[device]
+
+
 def randn_tensor(
     shape: Union[Tuple, List],
     generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
@@ -197,3 +304,31 @@ def device_synchronize(device_type: Optional[str] = None):
         device_type = get_device()
     device_mod = getattr(torch, device_type, torch.cuda)
     device_mod.synchronize()
+
+
+def enable_full_determinism():
+    """
+    Helper function for reproducible behavior during distributed training. See
+    - https://pytorch.org/docs/stable/notes/randomness.html for pytorch
+    """
+    #  Enable PyTorch deterministic mode. This potentially requires either the environment
+    #  variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
+    # depending on the CUDA version, so we set them both here
+    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
+    torch.use_deterministic_algorithms(True)
+
+    # Enable CUDNN deterministic mode
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cuda.matmul.allow_tf32 = False
+
+
+def disable_full_determinism():
+    os.environ["CUDA_LAUNCH_BLOCKING"] = "0"
+    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ""
+    torch.use_deterministic_algorithms(False)
+
+
+if is_torch_available():
+    torch_device = get_device()