[ONNX] Remove legacy Dort (#158258)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/158258
Approved by: https://github.com/justinchuby, https://github.com/malfet

docs/source/onnx.md

@@ -87,7 +87,6 @@ also be interested in reading our [development wiki](https://github.com/pytorch/
     onnx_dynamo
     onnx_ops
     onnx_verification
-    onnx_dynamo_onnxruntime_backend
     onnx_torchscript
 ```
 

docs/source/onnx_dynamo_onnxruntime_backend.md

@@ -1,11 +0,0 @@
-# ONNX Backend for TorchDynamo
-
-For a quick overview of `torch.compiler`, see {ref}`torch.compiler_overview`.
-
-```{warning}
-  The ONNX backend for torch.compile is a rapidly evolving beta technology.
-```
-
-```{eval-rst}
-.. autofunction:: torch.onnx.is_onnxrt_backend_supported
-```

docs/source/torch.compiler.md

@@ -56,8 +56,6 @@ Some of the most commonly used backends include:
      - CUDA graphs with AOT Autograd. `Read more <https://github.com/pytorch/torchdynamo/pull/757>`__
    * - ``torch.compile(m, backend="ipex")``
      - Uses IPEX on CPU. `Read more <https://github.com/intel/intel-extension-for-pytorch>`__
-   * - ``torch.compile(m, backend="onnxrt")``
-     - Uses ONNX Runtime for training on CPU/GPU. :doc:`Read more <onnx_dynamo_onnxruntime_backend>`
 ```
 
 **Inference-only backends**

test/dynamo/test_backends.py

@@ -8,7 +8,6 @@ import torch._dynamo
 import torch._dynamo.backends
 import torch._dynamo.test_case
 from torch._dynamo.backends.debugging import ExplainWithBackend
-from torch._dynamo.backends.onnxrt import has_onnxruntime
 from torch._dynamo.backends.tvm import has_tvm
 from torch._dynamo.testing import same
 from torch.fx._lazy_graph_module import _force_skip_lazy_graph_module
@@ -138,10 +137,6 @@ class TestOptimizations(torch._dynamo.test_case.TestCase):
     def test_aot_cudagraphs(self, device):
         self._check_backend_works("cudagraphs", device)
 
-    @unittest.skipIf(not has_onnxruntime(), "requires onnxruntime")
-    def test_onnxrt(self, device):
-        self._check_backend_works("onnxrt", device)
-
     @unittest.skipIf(not has_tvm(), "requires tvm")
     def test_tvm(self, device):
         self._check_backend_works("tvm", device)

torch/_dynamo/backends/onnxrt.py

@@ -4,35 +4,38 @@
 # to the right people, please tag related GitHub issues with `module: onnx`.
 #
 # Maintainers' Github IDs: wschin, xadupre
-from torch.onnx._internal.onnxruntime import (
-    is_onnxrt_backend_supported,
-    torch_compile_backend,
-)
+# from torch.onnx._internal.onnxruntime import (
+#     is_onnxrt_backend_supported,
+#     torch_compile_backend,
+# )
 
-from .registry import register_backend
+# from .registry import register_backend
+
+"""
+Placeholder for onnxruntime backend for dynamo
+"""
+
+# def has_onnxruntime():
+#     # FIXME: update test/dynamo/test_backends.py to call is_onnxrt_backend_supported()
+#     return is_onnxrt_backend_supported()
 
 
-def has_onnxruntime():
-    # FIXME: update test/dynamo/test_backends.py to call is_onnxrt_backend_supported()
-    return is_onnxrt_backend_supported()
+# if is_onnxrt_backend_supported():
+#     register_backend(name="onnxrt", compiler_fn=torch_compile_backend)
+# else:
 
+#     def information_displaying_backend(*args, **kwargs):
+#         raise ImportError(
+#             "onnxrt is not registered as a backend. "
+#             "Please make sure all dependencies such as "
+#             "numpy, onnx, onnxscript, and onnxruntime-training are installed. "
+#             "Suggested procedure to fix dependency problem:\n"
+#             "  (1) pip or conda install numpy onnx onnxscript onnxruntime-training.\n"
+#             "  (2) Open a new python terminal.\n"
+#             "  (3) Call the API `torch.onnx.is_onnxrt_backend_supported()`:\n"
+#             "  (4)   If it returns `True`, then you can use `onnxrt` backend.\n"
+#             "  (5)   If it returns `False`, please execute the package importing section in "
+#             "torch/onnx/_internal/onnxruntime.py under pdb line-by-line to see which import fails."
+#         )
 
-if is_onnxrt_backend_supported():
-    register_backend(name="onnxrt", compiler_fn=torch_compile_backend)
-else:
-
-    def information_displaying_backend(*args, **kwargs):
-        raise ImportError(
-            "onnxrt is not registered as a backend. "
-            "Please make sure all dependencies such as "
-            "numpy, onnx, onnxscript, and onnxruntime-training are installed. "
-            "Suggested procedure to fix dependency problem:\n"
-            "  (1) pip or conda install numpy onnx onnxscript onnxruntime-training.\n"
-            "  (2) Open a new python terminal.\n"
-            "  (3) Call the API `torch.onnx.is_onnxrt_backend_supported()`:\n"
-            "  (4)   If it returns `True`, then you can use `onnxrt` backend.\n"
-            "  (5)   If it returns `False`, please execute the package importing section in "
-            "torch/onnx/_internal/onnxruntime.py under pdb line-by-line to see which import fails."
-        )
-
-    register_backend(name="onnxrt", compiler_fn=information_displaying_backend)
+#     register_backend(name="onnxrt", compiler_fn=information_displaying_backend)

torch/onnx/__init__.py

@@ -38,8 +38,6 @@ __all__ = [
     "OnnxExporterError",
     "ONNXProgram",
     "enable_fake_mode",
-    # DORT / torch.compile
-    "is_onnxrt_backend_supported",
 ]
 
 from typing import Any, Callable, TYPE_CHECKING
@@ -51,12 +49,6 @@ from torch._C._onnx import OperatorExportTypes, TensorProtoDataType, TrainingMod
 
 from ._internal._exporter_legacy import enable_fake_mode
 from ._internal.exporter._onnx_program import ONNXProgram
-from ._internal.onnxruntime import (
-    is_onnxrt_backend_supported,
-    OrtBackend as _OrtBackend,
-    OrtBackendOptions as _OrtBackendOptions,
-    OrtExecutionProvider as _OrtExecutionProvider,
-)
 from ._type_utils import JitScalarType
 from .errors import OnnxExporterError
 from .utils import (
@@ -98,10 +90,7 @@ if TYPE_CHECKING:
 JitScalarType.__module__ = "torch.onnx"
 ONNXProgram.__module__ = "torch.onnx"
 OnnxExporterError.__module__ = "torch.onnx"
-_OrtBackend.__module__ = "torch.onnx"
-_OrtBackendOptions.__module__ = "torch.onnx"
 enable_fake_mode.__module__ = "torch.onnx"
-is_onnxrt_backend_supported.__module__ = "torch.onnx"
 
 producer_name = "pytorch"
 producer_version = _C_onnx.PRODUCER_VERSION