chore: import upstream snapshot with attribution

python/paddle/distributed/auto_parallel/static/dist_input_spec.py

@@ -0,0 +1,62 @@
+#   Copyright (c) 2023 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 copy
+
+from paddle.static import InputSpec
+
+from ..placement_type import get_shard_spec
+from .utils import convert_to_dims_mapping
+
+
+class DistributedInputSpec(InputSpec):
+    def __init__(
+        self,
+        shape,
+        dtype='float32',
+        name=None,
+        stop_gradient=False,
+        mesh=None,
+        placements=None,
+        local_shape=None,
+    ):
+        super().__init__(shape, dtype, name, stop_gradient)
+        self.mesh = copy.deepcopy(mesh)
+        sharding_specs = get_shard_spec(mesh, placements, len(self.shape))
+        self.dims_mapping = convert_to_dims_mapping(sharding_specs, mesh)
+        self.local_shape = local_shape
+
+    @classmethod
+    def from_dtensor(cls, dtensor, name=None, shape=None):
+        """
+        Generates a DistributedInputSpec based on dist tensor.
+
+        Args:
+            dtensor: the dist tensor.
+
+        Returns:
+            A DistributedInputSpec instance generated from dtensor.
+        """
+        return cls(
+            shape=dtensor.shape if shape is None else shape,
+            dtype=dtensor.dtype,
+            name=name,
+            stop_gradient=dtensor.stop_gradient,
+            mesh=dtensor.process_mesh,
+            placements=dtensor.placements,
+            local_shape=dtensor._local_value().shape,
+        )
+
+    def __repr__(self):
+        return f"{super().__repr__()}, mesh:{self.mesh}, placements:{self.dims_mapping}"