fix: Torch dependency in sagameker-core to be made optional (5457)

sagemaker-core/pyproject.toml

@@ -32,8 +32,6 @@ dependencies = [
     "smdebug_rulesconfig>=1.0.1",
     "schema>=0.7.5",
     "omegaconf>=2.1.0",
-    "torch>=1.9.0",
-    "scipy>=1.5.0",
     # Remote function dependencies
     "cloudpickle>=2.0.0",
     "paramiko>=2.11.0",
@@ -52,6 +50,13 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
+torch = [
+    "torch>=1.9.0",
+    "scipy>=1.5.0",
+]
+all = [
+    "sagemaker-core[torch]",
+]
 codegen = [
     "black>=24.3.0, <25.0.0",
     "pandas>=2.0.0, <3.0.0",

sagemaker-core/src/sagemaker/core/deserializers/base.py

@@ -366,7 +366,10 @@ def __init__(self, accept="tensor/pt"):
 
             self.convert_npy_to_tensor = from_numpy
         except ImportError:
-            raise Exception("Unable to import pytorch.")
+            raise ImportError(
+                "torch is required for TorchTensorDeserializer. "
+                "Install it with: pip install 'sagemaker-core[torch]'"
+            )
 
     def deserialize(self, stream, content_type="tensor/pt"):
         """Deserialize streamed data to TorchTensor

sagemaker-core/src/sagemaker/core/serializers/base.py

@@ -443,7 +443,13 @@ class TorchTensorSerializer(SimpleBaseSerializer):
 
     def __init__(self, content_type="tensor/pt"):
         super(TorchTensorSerializer, self).__init__(content_type=content_type)
-        from torch import Tensor
+        try:
+            from torch import Tensor
+        except ImportError:
+            raise ImportError(
+                "torch is required for TorchTensorSerializer. "
+                "Install it with: pip install 'sagemaker-core[torch]'"
+            )
 
         self.torch_tensor = Tensor
         self.numpy_serializer = NumpySerializer()

sagemaker-core/tests/unit/serializers/test_torch_optional.py

@@ -0,0 +1,108 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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.
+from __future__ import absolute_import
+
+import sys
+from unittest import mock
+
+import pytest
+
+
+def test_torch_tensor_serializer_raises_import_error_when_torch_missing():
+    """Verify TorchTensorSerializer raises ImportError with helpful message when torch is missing."""
+    with mock.patch.dict(sys.modules, {"torch": None}):
+        # Need to reload the module to pick up the mocked import
+        from sagemaker.core.serializers.base import TorchTensorSerializer
+
+        with pytest.raises(ImportError, match="pip install"):
+            TorchTensorSerializer()
+
+
+def test_torch_tensor_deserializer_raises_import_error_when_torch_missing():
+    """Verify TorchTensorDeserializer raises ImportError with helpful message when torch is missing."""
+    with mock.patch.dict(sys.modules, {"torch": None}):
+        from sagemaker.core.deserializers.base import TorchTensorDeserializer
+
+        with pytest.raises(ImportError, match="pip install"):
+            TorchTensorDeserializer()
+
+
+def test_torch_tensor_serializer_works_when_torch_available():
+    """Verify TorchTensorSerializer can be instantiated when torch is available."""
+    torch = pytest.importorskip("torch")
+    from sagemaker.core.serializers.base import TorchTensorSerializer
+
+    serializer = TorchTensorSerializer()
+    assert serializer.CONTENT_TYPE == "tensor/pt"
+
+    # Test serialization of a simple tensor
+    tensor = torch.tensor([1.0, 2.0, 3.0])
+    result = serializer.serialize(tensor)
+    assert result is not None
+
+
+def test_torch_tensor_deserializer_works_when_torch_available():
+    """Verify TorchTensorDeserializer can be instantiated when torch is available."""
+    pytest.importorskip("torch")
+    from sagemaker.core.deserializers.base import TorchTensorDeserializer
+
+    deserializer = TorchTensorDeserializer()
+    assert deserializer.ACCEPT == ("tensor/pt",)
+
+
+def test_base_serializers_importable_without_torch():
+    """Verify non-torch serializers can be imported and used without torch."""
+    from sagemaker.core.serializers.base import (
+        CSVSerializer,
+        NumpySerializer,
+        JSONSerializer,
+        IdentitySerializer,
+        JSONLinesSerializer,
+        LibSVMSerializer,
+        DataSerializer,
+        StringSerializer,
+    )
+
+    # Verify they can be instantiated
+    assert CSVSerializer() is not None
+    assert NumpySerializer() is not None
+    assert JSONSerializer() is not None
+    assert IdentitySerializer() is not None
+    assert JSONLinesSerializer() is not None
+    assert LibSVMSerializer() is not None
+    assert DataSerializer() is not None
+    assert StringSerializer() is not None
+
+
+def test_base_deserializers_importable_without_torch():
+    """Verify non-torch deserializers can be imported and used without torch."""
+    from sagemaker.core.deserializers.base import (
+        StringDeserializer,
+        BytesDeserializer,
+        CSVDeserializer,
+        StreamDeserializer,
+        NumpyDeserializer,
+        JSONDeserializer,
+        PandasDeserializer,
+        JSONLinesDeserializer,
+    )
+
+    # Verify they can be instantiated
+    assert StringDeserializer() is not None
+    assert BytesDeserializer() is not None
+    assert CSVDeserializer() is not None
+    assert StreamDeserializer() is not None
+    assert NumpyDeserializer() is not None
+    assert JSONDeserializer() is not None
+    assert PandasDeserializer() is not None
+    assert JSONLinesDeserializer() is not None

sagemaker-core/tox.ini

@@ -94,7 +94,7 @@ commands =
     pytest {posargs}
 deps =
     -r ../requirements/extras/test_requirements.txt
-    ../sagemaker-core
+    ../sagemaker-core[torch]
     .[test]
     mock
 depends =