🐛 Fix Multi-GPU Support with torch.compile

tests/models/test_feature_extractor.py

@@ -115,3 +115,48 @@ def test_full_inference(
     # ! else the output values will not exactly be the same (still < 1.0e-4
     # ! of epsilon though)
     assert np.mean(np.abs(features[:4] - _features)) < 1.0e-1
+
+
+@pytest.mark.skipif(
+    toolbox_env.running_on_ci() or not ON_GPU,
+    reason="Local test on machine with GPU.",
+)
+def test_multi_gpu_feature_extraction(remote_sample: Callable, tmp_path: Path) -> None:
+    """Local functionality test for feature extraction using multiple GPUs."""
+    save_dir = tmp_path / "output"
+    mini_wsi_svs = Path(remote_sample("wsi4_1k_1k_svs"))
+    shutil.rmtree(save_dir, ignore_errors=True)
+
+    # Use multiple GPUs
+    device = select_device(on_gpu=ON_GPU)
+
+    wsi_ioconfig = IOSegmentorConfig(
+        input_resolutions=[{"units": "mpp", "resolution": 0.5}],
+        patch_input_shape=[224, 224],
+        output_resolutions=[{"units": "mpp", "resolution": 0.5}],
+        patch_output_shape=[224, 224],
+        stride_shape=[224, 224],
+    )
+
+    model = TimmBackbone(backbone="UNI", pretrained=True)
+    extractor = DeepFeatureExtractor(
+        model=model,
+        auto_generate_mask=True,
+        batch_size=32,
+        num_loader_workers=4,
+        num_postproc_workers=4,
+    )
+
+    output_list = extractor.predict(
+        [mini_wsi_svs],
+        mode="wsi",
+        device=device,
+        ioconfig=wsi_ioconfig,
+        crash_on_exception=True,
+        save_dir=save_dir,
+    )
+    wsi_0_root_path = output_list[0][1]
+    positions = np.load(f"{wsi_0_root_path}.position.npy")
+    features = np.load(f"{wsi_0_root_path}.features.0.npy")
+    assert len(positions.shape) == 2
+    assert len(features.shape) == 2

tiatoolbox/models/engine/semantic_segmentor.py

@@ -13,6 +13,7 @@
 import joblib
 import numpy as np
 import torch
+import torch.distributed as dist
 import torch.multiprocessing as torch_mp
 import torch.utils.data as torch_data
 import tqdm
@@ -1421,6 +1422,14 @@ def predict(  # noqa: PLR0913
             logger.warning("Unable to remove %s", self._cache_dir)
 
         self._memory_cleanup()
+        from tiatoolbox.models.architecture.utils import is_torch_compile_compatible
+
+        if (
+            device == "cuda"
+            and torch.cuda.device_count() > 1
+            and is_torch_compile_compatible()
+        ):  # pragma: no cover
+            dist.destroy_process_group()
 
         return self._outputs
 

tiatoolbox/models/models_abc.py

@@ -2,11 +2,16 @@
 
 from __future__ import annotations
 
+import os
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any, Callable
 
 import torch
 import torch._dynamo
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel
+
+from tiatoolbox.models.architecture.utils import is_torch_compile_compatible
 
 torch._dynamo.config.suppress_errors = True  # skipcq: PYL-W0212  # noqa: SLF001
 
@@ -51,12 +56,30 @@ def model_to(model: torch.nn.Module, device: str = "cpu") -> torch.nn.Module:
             The model after being moved to specified device.
 
     """
-    if device != "cpu":
+    torch_device = torch.device(device)
+
+    # Use DDP if multiple GPUs and not on CPU
+    if (
+        device == "cuda"
+        and torch.cuda.device_count() > 1
+        and is_torch_compile_compatible()
+    ):  # pragma: no cover
+        # This assumes a single-process DDP setup for inference
+        model = model.to(torch_device)
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = "12355"
+        dist.init_process_group(backend="nccl", rank=0, world_size=1)
+        model = DistributedDataParallel(model, device_ids=[torch_device.index])
+
+    elif device != "cpu":
         # DataParallel work only for cuda
         model = torch.nn.DataParallel(model)
+        model = model.to(torch_device)
 
-    torch_device = torch.device(device)
-    return model.to(torch_device)
+    else:
+        model = model.to(torch_device)
+
+    return model
 
 
 class ModelABC(ABC, torch.nn.Module):