[SOT][Dist] Add guard for dist tensor

paddle/fluid/pybind/eager_method.cc

@@ -398,6 +398,31 @@ static PyObject* tensor_method_numpy(TensorObject* self,
                            dense_tensor->place(),
                            dense_tensor->Holder()->ptr(),
                            dense_tensor->Holder()->size());
+    } else if (self->tensor.is_dist_tensor()) {
+#ifdef PADDLE_WITH_DISTRIBUTE
+      VLOG(6) << "Getting DistTensor's numpy value";
+      auto* dist_tensor =
+          static_cast<phi::distributed::DistTensor*>(self->tensor.impl().get());
+      auto dense_tensor = ReshardXToReplicated(dist_tensor);
+
+      cpu_tensor.set_meta(dense_tensor.meta());
+      auto tmp_allocation_ptr =
+          memory::Alloc(cpu_place, dense_tensor.Holder()->size());
+      cpu_tensor.ResetHolder(std::shared_ptr<phi::Allocation>(
+          tmp_allocation_ptr.release(), tmp_allocation_ptr.get_deleter()));
+      paddle::memory::Copy(place,
+                           cpu_tensor.Holder()->ptr(),
+                           dense_tensor.place(),
+                           dense_tensor.Holder()->ptr(),
+                           dense_tensor.Holder()->size());
+#else
+      PADDLE_THROW(
+          common::errors::Unavailable("The `numpy()` method of (Dist)Tensor "
+                                      "is not supported in the current "
+                                      "PaddlePaddle, please recompile and "
+                                      "installPaddlePaddle with the option "
+                                      "of `WITH_DISTRIBUTE=ON`."));
+#endif
     } else {
       VLOG(6) << "Getting DenseTensor's numpy value";
       auto dense_tensor =

python/paddle/jit/sot/opcode_translator/executor/variables/basic.py

@@ -26,6 +26,7 @@
 from paddle.framework import core
 
 from ....infer_meta import (
+    DistInfo,
     MetaInfo,
     SymbolicBool,
     SymbolicFloat,
@@ -482,7 +483,7 @@ def make_stringified_guard(self) -> list[StringifiedExpression]:
         # A quick check path for PIR, we don't need dtype conversion for AMP in PIR
         meta = self.origin_meta
         dtype_str, dtype_free_vars = stringify_pyobject(meta.dtype)
-        return [
+        guards = [
             # Check rank
             StringifiedExpression(
                 f"len({{}}.shape) == {len(meta.shape)}",
@@ -511,7 +512,68 @@ def make_stringified_guard(self) -> list[StringifiedExpression]:
                 [frame_value_tracer],
                 union_free_vars(frame_value_tracer.free_vars),
             ),
+            # Check whether this tensor is distributed
+            StringifiedExpression(
+                f"{{}}.is_dist() is {(self.meta.dist_info is not None)!r}",
+                [frame_value_tracer],
+                union_free_vars(frame_value_tracer.free_vars),
+            ),
         ]
+        if self.meta.dist_info is not None:
+            tensor_dist_info = self.meta.dist_info
+            guards.extend(
+                [
+                    # check mesh shape
+                    StringifiedExpression(
+                        f"DistInfo.from_tensor({{}}).mesh.shape == {tensor_dist_info.mesh.shape}",
+                        [frame_value_tracer],
+                        union_free_vars(
+                            frame_value_tracer.free_vars,
+                            {
+                                "paddle": paddle,
+                                "DistInfo": DistInfo,
+                            },
+                        ),
+                    ),
+                    # check mesh process ids
+                    StringifiedExpression(
+                        f"DistInfo.from_tensor({{}}).mesh.process_ids == {tensor_dist_info.mesh.process_ids}",
+                        [frame_value_tracer],
+                        union_free_vars(
+                            frame_value_tracer.free_vars,
+                            {
+                                "paddle": paddle,
+                                "DistInfo": DistInfo,
+                            },
+                        ),
+                    ),
+                    # check dims mapping
+                    StringifiedExpression(
+                        f"DistInfo.from_tensor({{}}).dims_mapping == {tensor_dist_info.dims_mapping}",
+                        [frame_value_tracer],
+                        union_free_vars(
+                            frame_value_tracer.free_vars,
+                            {
+                                "paddle": paddle,
+                                "DistInfo": DistInfo,
+                            },
+                        ),
+                    ),
+                    # check local shape
+                    StringifiedExpression(
+                        f"DistInfo.from_tensor({{}}).local_shape == {tensor_dist_info.local_shape}",
+                        [frame_value_tracer],
+                        union_free_vars(
+                            frame_value_tracer.free_vars,
+                            {
+                                "paddle": paddle,
+                                "DistInfo": DistInfo,
+                            },
+                        ),
+                    ),
+                ]
+            )
+        return guards
 
     def get_iter(self):
         from .iter import SequenceIterVariable

test/sot/test_sot_distribution.py

@@ -0,0 +1,66 @@
+# Copyright (c) 2025 PaddlePaddle Authors. 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License 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.
+
+import unittest
+
+from test_case_base import (
+    TestCaseBase,
+    test_instruction_translator_cache_context,
+)
+
+import paddle
+import paddle.distributed as dist
+from paddle.jit.sot.psdb import check_no_breakgraph
+
+
+@check_no_breakgraph
+def fn(x, y):
+    return x + y
+
+
+@unittest.skipIf(
+    not paddle.is_compiled_with_distribute(),
+    reason='Not compiled with distribute.',
+)
+class TestGuardForDistInfo(TestCaseBase):
+    def test_fn(self):
+        x = paddle.ones([2, 2])
+        x.stop_gradient = False
+        y = paddle.zeros([2, 2])
+        y.stop_gradient = False
+        mesh1 = dist.ProcessMesh([0, 1], dim_names=['x'])
+        mesh2 = dist.ProcessMesh([0, 1], dim_names=['y'])
+        mesh3 = dist.ProcessMesh([0, 2], dim_names=['x'])
+        dist_x1 = dist.shard_tensor(
+            x, mesh1, [dist.Replicate()], stop_gradient=False
+        )
+        dist_y1 = dist.shard_tensor(
+            y, mesh1, [dist.Replicate()], stop_gradient=False
+        )
+        dist_x2 = dist.shard_tensor(x, mesh2, [dist.Replicate()])
+        dist_y2 = dist.shard_tensor(y, mesh2, [dist.Replicate()])
+        dist_x3 = dist.shard_tensor(x, mesh3, [dist.Replicate()])
+        dist_y3 = dist.shard_tensor(y, mesh3, [dist.Replicate()])
+        with test_instruction_translator_cache_context() as ctx:
+            self.assertEqual(ctx.translate_count, 0)
+            self.assert_results(fn, dist_x1, dist_y1)
+            self.assertEqual(ctx.translate_count, 1)
+            self.assert_results(fn, dist_x2, dist_y2)
+            self.assertEqual(ctx.translate_count, 1)
+            self.assert_results(fn, dist_x3, dist_y3)
+            self.assertEqual(ctx.translate_count, 2)
+
+
+if __name__ == "__main__":
+    unittest.main()