8587 test erros on pytorch release 2508 on series 50

monai/apps/auto3dseg/bundle_gen.py

@@ -264,8 +264,7 @@ def _run_cmd(self, cmd: str, devices_info: str = "") -> subprocess.CompletedProc
                 look_up_option(self.device_setting["MN_START_METHOD"], ["bcprun"])
             except ValueError as err:
                 raise NotImplementedError(
-                    f"{self.device_setting['MN_START_METHOD']} is not supported yet."
-                    "Try modify BundleAlgo._run_cmd for your cluster."
+                    f"{self.device_setting['MN_START_METHOD']} is not supported yet. Try modify BundleAlgo._run_cmd for your cluster."
                 ) from err
 
             return _run_cmd_bcprun(cmd, n=self.device_setting["NUM_NODES"], p=self.device_setting["n_devices"])

monai/apps/detection/networks/retinanet_detector.py

@@ -342,8 +342,7 @@ def set_regular_matcher(
         """
         if fg_iou_thresh < bg_iou_thresh:
             raise ValueError(
-                "Require fg_iou_thresh >= bg_iou_thresh. "
-                f"Got fg_iou_thresh={fg_iou_thresh}, bg_iou_thresh={bg_iou_thresh}."
+                f"Require fg_iou_thresh >= bg_iou_thresh. Got fg_iou_thresh={fg_iou_thresh}, bg_iou_thresh={bg_iou_thresh}."
             )
         self.proposal_matcher = Matcher(
             fg_iou_thresh, bg_iou_thresh, allow_low_quality_matches=allow_low_quality_matches
@@ -519,7 +518,7 @@ def forward(
         else:
             if self.inferer is None:
                 raise ValueError(
-                    "`self.inferer` is not defined." "Please refer to function self.set_sliding_window_inferer(*)."
+                    "`self.inferer` is not defined. Please refer to function self.set_sliding_window_inferer(*)."
                 )
             head_outputs = predict_with_inferer(
                 images, self.network, keys=[self.cls_key, self.box_reg_key], inferer=self.inferer

monai/apps/detection/transforms/box_ops.py

@@ -267,7 +267,7 @@ def convert_box_to_mask(
             boxes_only_mask = np.ones(box_size, dtype=np.int16) * np.int16(labels_np[b])
         # apply to global mask
         slicing = [b]
-        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type:ignore
+        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type: ignore
         boxes_mask_np[tuple(slicing)] = boxes_only_mask
     return convert_to_dst_type(src=boxes_mask_np, dst=boxes, dtype=torch.int16)[0]
 

monai/auto3dseg/analyzer.py

@@ -105,7 +105,7 @@ def update_ops_nested_label(self, nested_key: str, op: Operations) -> None:
             raise ValueError("Nested_key input format is wrong. Please ensure it is like key1#0#key2")
         root: str
         child_key: str
-        (root, _, child_key) = keys
+        root, _, child_key = keys
         if root not in self.ops:
             self.ops[root] = [{}]
         self.ops[root][0].update({child_key: None})
@@ -952,8 +952,7 @@ def __call__(self, data: dict) -> dict:
             self.hist_range = nr_channels * self.hist_range
         if len(self.hist_range) != nr_channels:
             raise ValueError(
-                f"There is a mismatch between the number of channels ({nr_channels}) "
-                f"and histogram ranges ({len(self.hist_range)})."
+                f"There is a mismatch between the number of channels ({nr_channels}) and histogram ranges ({len(self.hist_range)})."
             )
 
         # perform calculation

monai/bundle/scripts.py

@@ -1948,7 +1948,7 @@ def create_workflow(
 
     """
     _args = update_kwargs(args=args_file, workflow_name=workflow_name, config_file=config_file, **kwargs)
-    (workflow_name, config_file) = _pop_args(
+    workflow_name, config_file = _pop_args(
         _args, workflow_name=ConfigWorkflow, config_file=None
     )  # the default workflow name is "ConfigWorkflow"
     if isinstance(workflow_name, str):

monai/data/dataset.py

@@ -139,7 +139,7 @@ class DatasetFunc(Dataset):
     """
 
     def __init__(self, data: Any, func: Callable, **kwargs) -> None:
-        super().__init__(data=None, transform=None)  # type:ignore
+        super().__init__(data=None, transform=None)  # type: ignore
         self.src = data
         self.func = func
         self.kwargs = kwargs
@@ -1635,7 +1635,7 @@ def _cachecheck(self, item_transformed):
                         return (_data, _meta)
                     return _data
                 else:
-                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type:ignore
+                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type: ignore
                     for i, _item in enumerate(item_transformed):
                         for k in _item:
                             meta_i_k = self._load_meta_cache(meta_hash_file_name=f"{hashfile.name}-{k}-meta-{i}")

monai/data/wsi_reader.py

@@ -416,8 +416,7 @@ def get_data(
             # Check if there are three color channels for RGB
             elif mode in "RGB" and patch.shape[self.channel_dim] != 3:
                 raise ValueError(
-                    f"The image is expected to have three color channels in '{mode}' mode but has "
-                    f"{patch.shape[self.channel_dim]}. "
+                    f"The image is expected to have three color channels in '{mode}' mode but has {patch.shape[self.channel_dim]}. "
                 )
             # Get patch-related metadata
             metadata: dict = self._get_metadata(wsi=each_wsi, patch=patch, location=location, size=size, level=level)

monai/handlers/utils.py

@@ -48,7 +48,7 @@ def stopping_fn_from_loss() -> Callable[[Engine], Any]:
     """
 
     def stopping_fn(engine: Engine) -> Any:
-        return -engine.state.output  # type:ignore
+        return -engine.state.output  # type: ignore
 
     return stopping_fn
 

monai/metrics/utils.py

@@ -320,7 +320,7 @@ def get_edge_surface_distance(
     edges_spacing = None
     if use_subvoxels:
         edges_spacing = spacing if spacing is not None else ([1] * len(y_pred.shape))
-    (edges_pred, edges_gt, *areas) = get_mask_edges(
+    edges_pred, edges_gt, *areas = get_mask_edges(
         y_pred, y, crop=True, spacing=edges_spacing, always_return_as_numpy=False
     )
     if not edges_gt.any():

monai/networks/blocks/warp.py

@@ -20,6 +20,7 @@
 from monai.config.deviceconfig import USE_COMPILED
 from monai.networks.layers.spatial_transforms import grid_pull
 from monai.networks.utils import meshgrid_ij
+from monai.transforms.spatial.functional import _compiled_unsupported
 from monai.utils import GridSampleMode, GridSamplePadMode, optional_import
 
 _C, _ = optional_import("monai._C")
@@ -138,7 +139,9 @@ def forward(self, image: torch.Tensor, ddf: torch.Tensor):
         grid = self.get_reference_grid(ddf, jitter=self.jitter) + ddf
         grid = grid.permute([0] + list(range(2, 2 + spatial_dims)) + [1])  # (batch, ..., spatial_dims)
 
-        if not USE_COMPILED:  # pytorch native grid_sample
+        _use_compiled = USE_COMPILED and not _compiled_unsupported(image.device)
+
+        if not _use_compiled:  # pytorch native grid_sample
             for i, dim in enumerate(grid.shape[1:-1]):
                 grid[..., i] = grid[..., i] * 2 / (dim - 1) - 1
             index_ordering: list[int] = list(range(spatial_dims - 1, -1, -1))

monai/transforms/io/array.py

@@ -11,6 +11,7 @@
 """
 A collection of "vanilla" transforms for IO functions.
 """
+
 from __future__ import annotations
 
 import inspect

monai/transforms/regularization/array.py

@@ -24,7 +24,6 @@
 
 
 class Mixer(RandomizableTransform):
-
     def __init__(self, batch_size: int, alpha: float = 1.0) -> None:
         """
         Mixer is a base class providing the basic logic for the mixup-class of

monai/transforms/spatial/array.py

@@ -34,6 +34,7 @@
 from monai.transforms.croppad.array import CenterSpatialCrop, ResizeWithPadOrCrop
 from monai.transforms.inverse import InvertibleTransform
 from monai.transforms.spatial.functional import (
+    _compiled_unsupported,
     affine_func,
     convert_box_to_points,
     convert_points_to_box,
@@ -2104,14 +2105,15 @@ def __call__(
         _align_corners = self.align_corners if align_corners is None else align_corners
         img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype, device=_device)
         sr = min(len(img_t.peek_pending_shape() if isinstance(img_t, MetaTensor) else img_t.shape[1:]), 3)
+        _use_compiled = USE_COMPILED and not _compiled_unsupported(img_t.device)
         backend, _interp_mode, _padding_mode, _ = resolves_modes(
             self.mode if mode is None else mode,
             self.padding_mode if padding_mode is None else padding_mode,
             backend=None,
-            use_compiled=USE_COMPILED,
+            use_compiled=_use_compiled,
         )
 
-        if USE_COMPILED or backend == TransformBackends.NUMPY:
+        if _use_compiled or backend == TransformBackends.NUMPY:
             grid_t, *_ = convert_to_dst_type(grid[:sr], img_t, dtype=grid.dtype, wrap_sequence=True)
             if isinstance(grid, torch.Tensor) and grid_t.data_ptr() == grid.data_ptr():
                 grid_t = grid_t.clone(memory_format=torch.contiguous_format)
@@ -2122,7 +2124,7 @@ def __call__(
                     grid_t[i] = ((_dim - 1) / _dim) * grid_t[i] + t if _align_corners else grid_t[i] + t
                 elif _align_corners:
                     grid_t[i] = ((_dim - 1) / _dim) * (grid_t[i] + 0.5)
-            if USE_COMPILED and backend == TransformBackends.TORCH:  # compiled is using torch backend param name
+            if _use_compiled and backend == TransformBackends.TORCH:  # compiled is using torch backend param name
                 grid_t = moveaxis(grid_t, 0, -1)  # type: ignore
                 out = grid_pull(
                     img_t.unsqueeze(0),
@@ -2140,6 +2142,20 @@ def __call__(
                     [_map_coord(c, grid_np, order=_interp_mode, mode=_padding_mode) for c in img_np]
                 )
                 out = convert_to_dst_type(out, img_t)[0]
+            else:
+                # Fallback to PyTorch grid_sample when compiled extension is unsupported.
+                # Convert grid coordinates from compiled convention [0, size-1] to PyTorch [-1, 1]
+                for i, dim in enumerate(img_t.shape[1 : 1 + sr]):
+                    _dim = max(2, dim)
+                    grid_t[i] = (grid_t[i] * 2.0 / _dim) - 1.0
+                grid_t = moveaxis(grid_t, 0, -1)  # type: ignore
+                out = torch.nn.functional.grid_sample(
+                    img_t.unsqueeze(0),
+                    grid_t.unsqueeze(0),
+                    mode=_interp_mode,
+                    padding_mode=_padding_mode,
+                    align_corners=None if _align_corners == TraceKeys.NONE else _align_corners,  # type: ignore
+                )[0]
         else:
             grid_t = moveaxis(grid[list(range(sr - 1, -1, -1))], 0, -1)  # type: ignore
             grid_t = convert_to_dst_type(grid_t, img_t, wrap_sequence=True)[0].unsqueeze(0)

monai/transforms/spatial/functional.py

@@ -54,6 +54,35 @@
 __all__ = ["spatial_resample", "orientation", "flip", "resize", "rotate", "zoom", "rotate90", "affine_func"]
 
 
+def _compiled_unsupported(device: torch.device) -> bool:
+    """
+    Return True if ``monai._C`` (the compiled C extension providing ``grid_pull``) is not
+    compiled with support for the given CUDA device's compute capability.
+
+    Args:
+        device: The torch device to check for compiled extension support.
+
+    Returns:
+        True if the device is CUDA with compute capability major >= 12 (Blackwell+),
+        False otherwise. Always returns False for CPU devices.
+
+    Note:
+        ``monai._C`` is built at install time against a fixed set of CUDA architectures.
+        NVIDIA Blackwell GPUs (sm_120, compute capability 12.x) and newer were not included in
+        the default ``TORCH_CUDA_ARCH_LIST`` when the MONAI slim image was originally built,
+        so executing ``grid_pull`` on those devices produces incorrect results.  Falling back to
+        the PyTorch-native ``affine_grid`` + ``grid_sample`` path (``USE_COMPILED=False``) gives
+        correct output on all architectures.
+
+        The threshold (``major >= 12``) matches the first architecture family (Blackwell, sm_120)
+        that shipped after the highest sm supported in the current default build list (sm_90,
+        Hopper).  Adjust this constant when ``monai._C`` is rebuilt with sm_120+ support.
+    """
+    if device.type != "cuda":
+        return False
+    return torch.cuda.get_device_properties(device).major >= 12
+
+
 def _maybe_new_metatensor(img, dtype=None, device=None):
     """create a metatensor with fresh metadata if track_meta is True otherwise convert img into a torch tensor"""
     return convert_to_tensor(
@@ -158,7 +187,8 @@ def spatial_resample(
         xform_shape = [-1] + in_sp_size
         img = img.reshape(xform_shape)
     img = img.to(dtype_pt)
-    if isinstance(mode, int) or USE_COMPILED:
+    _use_compiled = USE_COMPILED and not _compiled_unsupported(img.device)
+    if isinstance(mode, int) or _use_compiled:
         dst_xform = create_translate(spatial_rank, [float(d - 1) / 2 for d in spatial_size])
         xform = xform @ convert_to_dst_type(dst_xform, xform)[0]
         affine_xform = monai.transforms.Affine(

monai/transforms/utility/array.py

@@ -702,7 +702,7 @@ def __init__(
                 # if the root log level is higher than INFO, set a separate stream handler to record
                 console = logging.StreamHandler(sys.stdout)
                 console.setLevel(logging.INFO)
-                console.is_data_stats_handler = True  # type:ignore[attr-defined]
+                console.is_data_stats_handler = True  # type: ignore[attr-defined]
                 _logger.addHandler(console)
 
     def __call__(

tests/integration/test_loader_semaphore.py

@@ -10,6 +10,7 @@
 # limitations under the License.
 """this test should not generate errors or
 UserWarning: semaphore_tracker: There appear to be 1 leaked semaphores"""
+
 from __future__ import annotations
 
 import multiprocessing as mp

tests/profile_subclass/profiling.py

@@ -12,6 +12,7 @@
 Comparing torch.Tensor, SubTensor, SubWithTorchFunc, MetaTensor
 Adapted from https://github.com/pytorch/pytorch/tree/v1.11.0/benchmarks/overrides_benchmark
 """
+
 from __future__ import annotations
 
 import argparse

tests/profile_subclass/pyspy_profiling.py

@@ -12,6 +12,7 @@
 To be used with py-spy, comparing torch.Tensor, SubTensor, SubWithTorchFunc, MetaTensor
 Adapted from https://github.com/pytorch/pytorch/tree/v1.11.0/benchmarks/overrides_benchmark
 """
+
 from __future__ import annotations
 
 import argparse

tests/transforms/croppad/test_pad_nd_dtypes.py

@@ -12,6 +12,7 @@
 Tests for pad_nd dtype support and backend selection.
 Validates PyTorch padding preference and NumPy fallback behavior.
 """
+
 from __future__ import annotations
 
 import unittest

tests/transforms/test_spatial_gpu_support.py

@@ -0,0 +1,83 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Test GPU support detection and fallback paths for spatial transforms."""
+
+from __future__ import annotations
+
+import unittest
+
+import torch
+
+from monai.transforms.spatial.functional import _compiled_unsupported
+
+
+class TestCompiledUnsupported(unittest.TestCase):
+    """Test _compiled_unsupported device detection."""
+
+    def test_cpu_device_always_supported(self):
+        """CPU devices should never be marked unsupported."""
+        device = torch.device("cpu")
+        self.assertFalse(_compiled_unsupported(device))
+
+    def test_non_cuda_device_always_supported(self):
+        """Non-CUDA devices should always be supported."""
+        device = torch.device("cpu")
+        self.assertFalse(_compiled_unsupported(device))
+
+    @unittest.skipIf(not torch.cuda.is_available(), reason="CUDA not available")
+    def test_cuda_device_detection(self):
+        """Verify CUDA compute capability detection."""
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        if device.type == "cuda":
+            cc_major = torch.cuda.get_device_properties(device).major
+            unsupported = _compiled_unsupported(device)
+            # Device is unsupported if cc_major >= 12
+            if cc_major >= 12:
+                self.assertTrue(unsupported)
+            else:
+                self.assertFalse(unsupported)
+
+    def test_compiled_unsupported_return_type(self):
+        """Verify return type is bool."""
+        device = torch.device("cpu")
+        result = _compiled_unsupported(device)
+        self.assertIsInstance(result, bool)
+
+
+class TestResampleFallback(unittest.TestCase):
+    """Test Resample fallback behavior on unsupported devices."""
+
+    @unittest.skipIf(not torch.cuda.is_available(), reason="CUDA not available")
+    def test_resample_compilation_flag_respected(self):
+        """Verify Resample respects _compiled_unsupported check."""
+        # This would require internal inspection or output verification
+        # Could test with mock device properties or actual Blackwell GPU
+
+    def test_compiled_unsupported_logic(self):
+        """Test that unsupported devices are correctly detected."""
+        # CPU should be supported
+        cpu_device = torch.device("cpu")
+        self.assertFalse(_compiled_unsupported(cpu_device))
+
+        # Verify logic: return True if CUDA and cc_major >= 12
+        cuda_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        if cuda_device.type == "cuda":
+            cc_major = torch.cuda.get_device_properties(cuda_device).major
+            expected = cc_major >= 12
+            actual = _compiled_unsupported(cuda_device)
+            self.assertEqual(actual, expected)
+
+
+if __name__ == "__main__":
+    unittest.main()
+if __name__ == "__main__":
+    unittest.main()

versioneer.py

@@ -273,6 +273,7 @@
 [travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
 
 """
+
 # pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring
 # pylint:disable=missing-class-docstring,too-many-branches,too-many-statements
 # pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error