diff --git a/sagemaker-core/pyproject.toml b/sagemaker-core/pyproject.toml index 2756ce0f1c..4b3f3f1739 100644 --- a/sagemaker-core/pyproject.toml +++ b/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 = [ + "sagemaker-core[torch]", +] codegen = [ "black>=24.3.0, <25.0.0", "pandas>=2.0.0, <3.0.0", @@ -61,6 +66,7 @@ test = [ "pytest>=8.0.0, <9.0.0", "pytest-cov>=4.0.0", "pytest-xdist>=3.0.0", + "sagemaker-core[torch]", ] [project.urls] diff --git a/sagemaker-core/src/sagemaker/core/deserializers/base.py b/sagemaker-core/src/sagemaker/core/deserializers/base.py index 4faae7db74..03138ed577 100644 --- a/sagemaker-core/src/sagemaker/core/deserializers/base.py +++ b/sagemaker-core/src/sagemaker/core/deserializers/base.py @@ -365,8 +365,11 @@ def __init__(self, accept="tensor/pt"): from torch import from_numpy self.convert_npy_to_tensor = from_numpy - except ImportError: - raise Exception("Unable to import pytorch.") + except ImportError as e: + raise ImportError( + "Unable to import torch. Please install torch to use TorchTensorDeserializer: " + "pip install 'sagemaker-core[torch]'" + ) from e def deserialize(self, stream, content_type="tensor/pt"): """Deserialize streamed data to TorchTensor diff --git a/sagemaker-core/src/sagemaker/core/serializers/base.py b/sagemaker-core/src/sagemaker/core/serializers/base.py index a4ecf7c1dc..84b9832c63 100644 --- a/sagemaker-core/src/sagemaker/core/serializers/base.py +++ b/sagemaker-core/src/sagemaker/core/serializers/base.py @@ -443,9 +443,16 @@ 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 + + self.torch_tensor = Tensor + except ImportError as e: + raise ImportError( + "Unable to import torch. Please install torch to use TorchTensorSerializer: " + "pip install 'sagemaker-core[torch]'" + ) from e - self.torch_tensor = Tensor self.numpy_serializer = NumpySerializer() def serialize(self, data): diff --git a/sagemaker-core/tests/unit/test_optional_torch_dependency.py b/sagemaker-core/tests/unit/test_optional_torch_dependency.py new file mode 100644 index 0000000000..5008244e27 --- /dev/null +++ b/sagemaker-core/tests/unit/test_optional_torch_dependency.py @@ -0,0 +1,152 @@ +# 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. +"""Tests to verify torch dependency is optional in sagemaker-core.""" +from __future__ import annotations + +import importlib +import io +import sys + +import numpy as np +import pytest + + +def _block_torch(): + """Block torch imports by setting sys.modules['torch'] to None. + + Returns a dict of saved torch submodule entries so they can be restored. + """ + saved = {} + torch_keys = [key for key in sys.modules if key.startswith("torch.")] + saved = {key: sys.modules.pop(key) for key in torch_keys} + saved["torch"] = sys.modules.get("torch") + sys.modules["torch"] = None + return saved + + +def _restore_torch(saved): + """Restore torch modules from saved dict.""" + original_torch = saved.pop("torch", None) + if original_torch is not None: + sys.modules["torch"] = original_torch + elif "torch" in sys.modules: + del sys.modules["torch"] + for key, val in saved.items(): + sys.modules[key] = val + + +def test_serializer_module_imports_without_torch(): + """Verify that importing non-torch serializers succeeds without torch installed.""" + saved = {} + try: + saved = _block_torch() + + # Reload the module so it re-evaluates imports with torch blocked + import sagemaker.core.serializers.base as ser_module + + importlib.reload(ser_module) + + # Verify non-torch serializers can be instantiated + assert ser_module.CSVSerializer() is not None + assert ser_module.NumpySerializer() is not None + assert ser_module.JSONSerializer() is not None + assert ser_module.IdentitySerializer() is not None + finally: + _restore_torch(saved) + + +def test_deserializer_module_imports_without_torch(): + """Verify that importing non-torch deserializers succeeds without torch installed.""" + saved = {} + try: + saved = _block_torch() + + import sagemaker.core.deserializers.base as deser_module + + importlib.reload(deser_module) + + # Verify non-torch deserializers can be instantiated + assert deser_module.StringDeserializer() is not None + assert deser_module.BytesDeserializer() is not None + assert deser_module.CSVDeserializer() is not None + assert deser_module.NumpyDeserializer() is not None + assert deser_module.JSONDeserializer() is not None + finally: + _restore_torch(saved) + + +def test_torch_tensor_serializer_raises_import_error_without_torch(): + """Verify TorchTensorSerializer raises ImportError when torch is not installed.""" + import sagemaker.core.serializers.base as ser_module + + saved = {} + try: + saved = _block_torch() + + with pytest.raises(ImportError, match="Unable to import torch"): + ser_module.TorchTensorSerializer() + finally: + _restore_torch(saved) + + +def test_torch_tensor_deserializer_raises_import_error_without_torch(): + """Verify TorchTensorDeserializer raises ImportError when torch is not installed.""" + import sagemaker.core.deserializers.base as deser_module + + saved = {} + try: + saved = _block_torch() + + with pytest.raises(ImportError, match="Unable to import torch"): + deser_module.TorchTensorDeserializer() + finally: + _restore_torch(saved) + + +def test_torch_tensor_serializer_works_with_torch(): + """Verify TorchTensorSerializer works when torch is available.""" + try: + import torch + except ImportError: + pytest.skip("torch is 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 + # Verify the result can be loaded back as numpy + array = np.load(io.BytesIO(result)) + assert np.array_equal(array, np.array([1.0, 2.0, 3.0])) + + +def test_torch_tensor_deserializer_works_with_torch(): + """Verify TorchTensorDeserializer works when torch is available.""" + try: + import torch + except ImportError: + pytest.skip("torch is not installed") + + from sagemaker.core.deserializers.base import TorchTensorDeserializer + + deserializer = TorchTensorDeserializer() + # Create a numpy array, save it, and deserialize to tensor + array = np.array([1.0, 2.0, 3.0]) + buffer = io.BytesIO() + np.save(buffer, array) + buffer.seek(0) + + result = deserializer.deserialize(buffer, "tensor/pt") + assert isinstance(result, torch.Tensor) + assert torch.equal(result, torch.tensor([1.0, 2.0, 3.0])) diff --git a/sagemaker-core/tests/unit/test_serializer_implementations.py b/sagemaker-core/tests/unit/test_serializer_implementations.py index 60d7d62b0b..b12471cbf2 100644 --- a/sagemaker-core/tests/unit/test_serializer_implementations.py +++ b/sagemaker-core/tests/unit/test_serializer_implementations.py @@ -11,7 +11,7 @@ # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Unit tests for sagemaker.core.serializers.implementations module.""" -from __future__ import absolute_import +from __future__ import annotations import pytest from unittest.mock import Mock, patch @@ -162,3 +162,25 @@ def test_numpy_serializer_import(self): def test_record_serializer_deprecated(self): """Test that numpy_to_record_serializer is available as deprecated.""" assert hasattr(implementations, "numpy_to_record_serializer") + + +class TestTorchSerializerWithOptionalDependency: + """Test torch serializer/deserializer with optional torch dependency.""" + + def test_torch_tensor_serializer_instantiation(self): + """Test that TorchTensorSerializer can be instantiated when torch is available.""" + torch = pytest.importorskip("torch") + from sagemaker.core.serializers.base import TorchTensorSerializer + + serializer = TorchTensorSerializer() + assert serializer is not None + assert serializer.content_type == "tensor/pt" + + def test_torch_tensor_deserializer_instantiation(self): + """Test that TorchTensorDeserializer can be instantiated when torch is available.""" + torch = pytest.importorskip("torch") + from sagemaker.core.deserializers.base import TorchTensorDeserializer + + deserializer = TorchTensorDeserializer() + assert deserializer is not None + assert deserializer.accept == "tensor/pt" diff --git a/sagemaker-core/tox.ini b/sagemaker-core/tox.ini index 136b99c69c..0e31a74d80 100644 --- a/sagemaker-core/tox.ini +++ b/sagemaker-core/tox.ini @@ -6,7 +6,6 @@ [tox] isolated_build = true envlist = black-format,flake8,pylint,docstyle,sphinx,doc8,twine,py39,py310,py311,py312 - skip_missing_interpreters = False [flake8]