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

Author: aviruthen

sagemaker-core/pyproject.toml

@@ -32,7 +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",
@@ -51,6 +50,12 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
+torch = [
+    "torch>=1.9.0",
+]
+all = [
+    "torch>=1.9.0",
+]
 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(
+                "Unable to import torch. Please install torch to use TorchTensorDeserializer: "
+                "'pip install torch' or '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(
+                "Unable to import torch. Please install torch to use TorchTensorSerializer: "
+                "'pip install torch' or 'pip install sagemaker-core[torch]'"
+            )
 
         self.torch_tensor = Tensor
         self.numpy_serializer = NumpySerializer()

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

@@ -0,0 +1,91 @@
+# 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.mock import patch, MagicMock
+
+import pytest
+import numpy as np
+
+
+def test_torch_tensor_serializer_raises_import_error_when_torch_missing():
+    """Verify TorchTensorSerializer() raises ImportError with helpful install message
+    when torch is not installed."""
+    import sagemaker.core.serializers.base as base_module
+
+    with patch.dict(sys.modules, {"torch": None}):
+        with pytest.raises(ImportError, match="pip install.*torch"):
+            base_module.TorchTensorSerializer()
+
+
+def test_torch_tensor_deserializer_raises_import_error_when_torch_missing():
+    """Verify TorchTensorDeserializer() raises ImportError with helpful install message
+    when torch is not installed."""
+    import sagemaker.core.deserializers.base as base_module
+
+    with patch.dict(sys.modules, {"torch": None}):
+        with pytest.raises(ImportError, match="pip install.*torch"):
+            base_module.TorchTensorDeserializer()
+
+
+def test_non_torch_serializers_work_without_torch():
+    """Verify CSVSerializer, JSONSerializer, NumpySerializer etc. all work fine
+    even if torch is not available."""
+    from sagemaker.core.serializers.base import (
+        CSVSerializer,
+        JSONSerializer,
+        NumpySerializer,
+        IdentitySerializer,
+    )
+
+    csv_ser = CSVSerializer()
+    assert csv_ser.serialize([1, 2, 3]) == "1,2,3"
+
+    json_ser = JSONSerializer()
+    assert json_ser.serialize({"a": 1}) == '{"a": 1}'
+
+    numpy_ser = NumpySerializer()
+    result = numpy_ser.serialize(np.array([1, 2, 3]))
+    assert result is not None
+
+    identity_ser = IdentitySerializer()
+    assert identity_ser.serialize(b"hello") == b"hello"
+
+
+def test_torch_tensor_serializer_works_when_torch_available():
+    """Verify TorchTensorSerializer works normally when torch is installed."""
+    try:
+        import torch
+    except ImportError:
+        pytest.skip("torch not installed")
+
+    from sagemaker.core.serializers.base import TorchTensorSerializer
+
+    serializer = TorchTensorSerializer()
+    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 works normally when torch is installed."""
+    try:
+        import torch
+    except ImportError:
+        pytest.skip("torch not installed")
+
+    from sagemaker.core.deserializers.base import TorchTensorDeserializer
+
+    deserializer = TorchTensorDeserializer()
+    assert deserializer is not None