pytorch
diff --git a/‎docs/source/export.ir_spec.rst
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diff --git a/‎test/distributed/_tensor/experimental/test_tp_transform.py
Lines changed: 3 additions & 3 deletions b/‎test/distributed/_tensor/experimental/test_tp_transform.py
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diff --git a/‎test/dynamo/test_export.py
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diff --git a/‎test/export/test_pass_infra.py
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diff --git a/‎test/quantization/pt2e/test_duplicate_dq.py
Lines changed: 2 additions & 2 deletions b/‎test/quantization/pt2e/test_duplicate_dq.py
Lines changed: 2 additions & 2 deletions
diff --git a/‎test/quantization/pt2e/test_metadata_porting.py
Lines changed: 3 additions & 3 deletions b/‎test/quantization/pt2e/test_metadata_porting.py
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diff --git a/‎test/test_out_dtype_op.py
Lines changed: 8 additions & 8 deletions b/‎test/test_out_dtype_op.py
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diff --git a/‎torch/_export/__init__.py
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diff --git a/‎torch/_export/serde/upgrade.py
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diff --git a/‎torch/_export/utils.py
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@@ -110,7 +110,7 @@ Example::
        def forward(self, x, y):
          return x + y
 
-   mod = torch._export.export(MyModule())
+   mod = torch.export.export(MyModule())
    print(mod.graph)
 
 The above is the textual representation of a Graph, with each line being a node.
 
@@ -72,7 +72,7 @@ def test_tp_transform_with_uncovered_op(self):
         inputs = (torch.randn(7, 3, requires_grad=False).to(device=self.device_type),)
         with torch.no_grad():
             res = model(*inputs)
-        exported_program = torch._export.export(
+        exported_program = torch.export.export(
             model,
             inputs,
             constraints=None,
@@ -111,7 +111,7 @@ def test_tp_transform_e2e(self):
 
         with torch.inference_mode():
             res = model(*inputs)
-        exported_program = torch._export.export(
+        exported_program = torch.export.export(
             model,
             inputs,
             constraints=None,
@@ -148,7 +148,7 @@ def test_tp_transform_no_bias(self):
 
         with torch.inference_mode():
             res = model(*inputs)
-        exported_program = torch._export.export(
+        exported_program = torch.export.export(
             model,
             inputs,
             constraints=None,
 
@@ -21,9 +21,9 @@
 from torch._dynamo import config
 from torch._dynamo.exc import UserError
 from torch._dynamo.testing import normalize_gm
-from torch._export import dynamic_dim
 from torch._higher_order_ops.out_dtype import out_dtype
 from torch._subclasses import fake_tensor
+from torch.export import dynamic_dim
 from torch.fx.experimental.proxy_tensor import make_fx
 from torch.fx.experimental.symbolic_shapes import (
     ConstraintViolationError,
 
@@ -124,7 +124,7 @@ def forward(self, x1, x2):
         input_tensor1 = torch.tensor(5.0)
         input_tensor2 = torch.tensor(6.0)
 
-        ep_before = torch._export.export(my_module, (input_tensor1, input_tensor2))
+        ep_before = torch.export.export(my_module, (input_tensor1, input_tensor2))
         from torch.fx.passes.infra.pass_base import PassResult
 
         def modify_input_output_pass(gm):
 
@@ -4,7 +4,7 @@
 from typing import Any, Dict
 
 import torch
-import torch._export as export
+from torch._export import capture_pre_autograd_graph
 
 from torch.ao.quantization.observer import (
     HistogramObserver,
@@ -102,7 +102,7 @@ def _test_duplicate_dq(
 
         # program capture
         m = copy.deepcopy(m_eager)
-        m = export.capture_pre_autograd_graph(
+        m = capture_pre_autograd_graph(
             m,
             example_inputs,
         )
 
@@ -5,7 +5,7 @@
 from typing import List
 
 import torch
-import torch._export as export
+import torch._export
 from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
 from torch.ao.quantization.quantizer import Quantizer
 from torch.ao.quantization.quantizer.xnnpack_quantizer import (
@@ -64,7 +64,7 @@ class TestMetaDataPorting(QuantizationTestCase):
     def _test_quant_tag_preservation_through_decomp(
         self, model, example_inputs, from_node_to_tags
     ):
-        ep = export.export(model, example_inputs)
+        ep = torch.export.export(model, example_inputs)
         found_tags = True
         not_found_nodes = ""
         for from_node, tag in from_node_to_tags.items():
@@ -102,7 +102,7 @@ def _test_metadata_porting(
 
         # program capture
         m = copy.deepcopy(m_eager)
-        m = export.capture_pre_autograd_graph(
+        m = torch._export.capture_pre_autograd_graph(
             m,
             example_inputs,
         )
 
@@ -5,7 +5,6 @@
 import torch._dynamo
 import torch._inductor
 import torch._inductor.decomposition
-import torch._export
 from torch._higher_order_ops.out_dtype import out_dtype
 from torch.fx.experimental.proxy_tensor import make_fx
 from torch.testing._internal.common_utils import (
@@ -62,7 +61,7 @@ def forward(self, x):
         weight = torch.randint(-128, 127, (5, 5), dtype=torch.int8)
         m = M(weight)
         x = torch.randint(-128, 127, (5, 5), dtype=torch.int8)
-        ep = torch._export.export(
+        ep = torch.export.export(
             m,
             (x,),
         )
@@ -121,14 +120,15 @@ def f(x, y):
         self.assertTrue(torch.allclose(numerical_res, gm(*inp)))
 
     def test_out_dtype_non_functional(self):
-        def f(x, y):
-            return out_dtype(
-                torch.ops.aten.add_.Tensor, torch.int32, x, y
-            )
+        class M(torch.nn.Module):
+            def forward(self, x, y):
+                return out_dtype(
+                    torch.ops.aten.add_.Tensor, torch.int32, x, y
+                )
 
         with self.assertRaisesRegex(ValueError, "out_dtype's first argument needs to be a functional operator"):
-            _ = torch._export.export(
-                f, (torch.randint(-128, 127, (5, 5), dtype=torch.int8), torch.randint(-128, 127, (5, 5), dtype=torch.int8)),
+            _ = torch.export.export(
+                M(), (torch.randint(-128, 127, (5, 5), dtype=torch.int8), torch.randint(-128, 127, (5, 5), dtype=torch.int8)),
             )
 
     def test_out_dtype_non_op_overload(self):
 
@@ -223,36 +223,6 @@ def _eval(self, mode: bool = True):
     return module
 
 
-def export(
-    f: Callable,
-    args: Tuple[Any, ...],
-    kwargs: Optional[Dict[str, Any]] = None,
-    constraints: Optional[List[Constraint]] = None,
-    *,
-    strict: bool = True,
-    preserve_module_call_signature: Tuple[str, ...] = (),
-) -> ExportedProgram:
-    from torch.export._trace import _export
-    warnings.warn("This function is deprecated. Please use torch.export.export instead.")
-
-    if constraints is not None:
-        warnings.warn(
-            "Using `constraints` to specify dynamic shapes for export is DEPRECATED "
-            "and will not be supported in the future. "
-            "Please use `dynamic_shapes` instead (see docs on `torch.export.export`).",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-    return _export(
-        f,
-        args,
-        kwargs,
-        constraints,
-        strict=strict,
-        preserve_module_call_signature=preserve_module_call_signature,
-    )
-
-
 def save(
     ep: ExportedProgram,
     f: Union[str, os.PathLike, io.BytesIO],
 
@@ -3,7 +3,7 @@
 from typing import Tuple, Dict, Optional, List
 
 import torch
-from torch._export import export
+from torch.export import export
 from torch._export.pass_base import _ExportPassBaseDeprecatedDoNotUse
 from torch._export.pass_infra.node_metadata import NodeMetadata
 from torch._export.pass_infra.proxy_value import ProxyValue
 
@@ -3,9 +3,9 @@
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 import torch
-
-from torch._export import ExportedProgram
 from torch._subclasses.fake_tensor import FakeTensor
+
+from torch.export import ExportedProgram
 from torch.utils._pytree import (
     _register_pytree_node,
     Context,