torch: Check status of torch numpy bindings instead of specific version

Assert that torch is usable if installed during testing.

Signed-off-by: Jan Vesely <[email protected]>

conftest.py

@@ -7,23 +7,17 @@
 import re
 import sys
 
-from psyneulink import clear_registry, primary_registries
+from psyneulink import clear_registry, primary_registries, torch_available
 from psyneulink.core import llvm as pnlvm
 from psyneulink.core.globals.utilities import set_global_seed
 
-
 try:
     import torch
-
-    # If we are on windows and using Python 3.10, despite it importing correctly, PyTorch is currently broken,
-    # see https://pytorch.org/get-started/locally/ showing lack of support.
-    if sys.platform.startswith("win32") and sys.version_info >= (3, 10):
-        pytorch_available = False
-    else:
-        pytorch_available = True
-
 except ImportError:
-    pytorch_available = False
+    pass
+else:
+    # Check that torch is usable if installed
+    assert torch_available, "Torch module is available, but not usable by PNL"
 
 # def pytest_addoption(parser):
 #     parser.addoption(
@@ -57,7 +51,7 @@ def pytest_runtest_setup(item):
     if 'cuda' in item.keywords and not pnlvm.ptx_enabled:
         pytest.skip('PTX engine not enabled/available')
 
-    if 'pytorch' in item.keywords and not pytorch_available:
+    if 'pytorch' in item.keywords and not torch_available:
         pytest.skip('pytorch not available')
 
     doctest.ELLIPSIS_MARKER = "[...]"
@@ -122,7 +116,7 @@ def pytest_runtest_call(item):
     set_global_seed(seed)
 
     if 'pytorch' in item.keywords:
-        assert pytorch_available
+        assert torch_available
         torch.manual_seed(seed)
 
 

psyneulink/library/compositions/__init__.py

@@ -4,3 +4,17 @@
 __all__ = list(regressioncfa.__all__)
 __all__.extend(compositionrunner.__all__)
 __all__.extend(autodiffcomposition.__all__)
+
+try:
+    import torch
+    from torch import nn
+
+    # Some torch releases have silent dependency on a more recent numpy than the one curently required by PNL.
+    # This breaks torch numpy bindings, see e.g:  https://github.com/pytorch/pytorch/issues/100690
+    torch.tensor([1,2,3]).numpy()
+
+    torch_available = True
+except (ImportError, RuntimeError):
+    torch_available = False
+
+__all__.append('torch_available')

psyneulink/library/compositions/pytorchmodelcreator.py

@@ -1,4 +1,6 @@
 import graph_scheduler
+import torch
+import torch.nn as nn
 
 from psyneulink.core.components.component import Component, ComponentsMeta
 from psyneulink.core.compositions.composition import NodeRole
@@ -11,23 +13,12 @@
 from psyneulink.core.globals.utilities import get_deepcopy_with_shared
 from .pytorchcomponents import *
 
-try:
-    import torch
-    from torch import nn
-    torch_available = True
-except ImportError:
-    torch_available = False
-
 __all__ = ['PytorchModelCreator']
 
 class PytorchModelCreator(torch.nn.Module):
     # sets up parameters of model & the information required for forward computation
     def __init__(self, composition, device, context=None):
 
-        if not torch_available:
-            raise Exception('Pytorch python module (torch) is not installed. Please install it with '
-                            '`pip install torch` or `pip3 install torch`')
-
         super(PytorchModelCreator, self).__init__()
 
         # Maps Mechanism -> PytorchMechanismWrapper