hooks: torch: suppress generation of symlinks for shared libs

_pyinstaller_hooks_contrib/stdhooks/hook-torch.py

@@ -73,6 +73,20 @@ def _infer_nvidia_hiddenimports():
         logger.info("hook-torch: inferred hidden imports for CUDA libraries: %r", nvidia_hiddenimports)
         hiddenimports += nvidia_hiddenimports
 
+        # On Linux, prevent binary dependency analysis from generating symbolic links for libraries from `torch/lib` to
+        # the top-level application directory. These symbolic links seem to confuse `torch` about location of its shared
+        # libraries (likely because code in one of the libraries looks up the library file's location, but does not
+        # fully resolve it), and prevent it from finding dynamically-loaded libraries in `torch/lib` directory, such as
+        # `torch/lib/libtorch_cuda_linalg.so`. The issue was observed with earlier versions of `torch` builds provided
+        # by https://download.pytorch.org/whl/torch, specifically 1.13.1+cu117, 2.0.1+cu117, and 2.1.2+cu118; later
+        # versions do not seem to be affected. The wheels provided on PyPI do not seem to be affected, either, even
+        # for torch 1.13.1, 2.01, and 2.1.2. However, these symlinks should be not necessary on linux in general, so
+        # there should be no harm in suppressing them for all versions.
+        #
+        # The `bindepend_symlink_suppression` hook attribute requires PyInstaller >= 6.11, and is no-op in earlier
+        # versions.
+        bindepend_symlink_suppression = ['**/torch/lib/*.so*']
+
     # The Windows nightly build for torch 2.3.0 added dependency on MKL. The `mkl` distribution does not provide an
     # importable package, but rather installs the DLLs in <env>/Library/bin directory. Therefore, we cannot write a
     # separate hook for it, and must collect the DLLs here. (Most of these DLLs are missed by PyInstaller's binary

news/834.update.rst

@@ -0,0 +1,7 @@
+(Linux) Update ``torch`` hook to suppress creation of symbolic links to
+the top-level application directory for the shared libraries discovered
+during binary dependency analysis in ``torch/lib`` directory. This fixes
+issues with ``libtorch_cuda_linalg.so`` not being found in spite of it
+being collected, as observed with certain ``torch`` builds provided by
+https://download.pytorch.org/whl/torch (e.g., ``1.13.1+cu117``,
+``2.0.1+cu117``, and ``2.1.2+cu118``).

tests/test_pytorch.py

@@ -12,6 +12,7 @@
 
 import pytest
 
+from PyInstaller import isolated
 from PyInstaller.utils.tests import importorskip
 
 
@@ -28,6 +29,54 @@ def test_torch(pyi_builder):
     """)
 
 
+@importorskip('torch')
+def test_torch_cuda_linalg(pyi_builder):
+    # Check that CUDA is available.
+    @isolated.decorate
+    def _check_cuda():
+        import torch
+        return torch.cuda.is_available() and torch.cuda.device_count() > 0
+
+    if not _check_cuda():
+        pytest.skip(reason="CUDA not available.")
+
+    pyi_builder.test_source("""
+        import torch
+
+        # Solve the following system of equations:
+        # x + 2y - 2z = -15
+        # 2x + y - 5z = -21
+        # x - 4y + z = 18
+        #
+        # Solution: x=-1, y=-4, z=3
+
+        cuda_device = torch.device('cuda')
+        print(f"Using device: {cuda_device}")
+
+        A = torch.tensor([
+            [1, 2, -2],
+            [2, 1, -5],
+            [1, -4, 1],
+        ], dtype=torch.float, device=cuda_device)
+
+        b = torch.tensor([
+            [-15],
+            [-21],
+            [18],
+        ], dtype=torch.float, device=cuda_device)
+
+        print(f"A={A}")
+        print(f"b={b}")
+
+        x = torch.linalg.solve(A, b)
+        print(f"x={x}")
+
+        assert x[0] == -1
+        assert x[1] == -4
+        assert x[2] == 3
+    """)
+
+
 # Test with torchaudio transform that uses torchcript, which requires
 # access to transforms' sources.
 @importorskip('torchaudio')