wehub-resource-sync
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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from paddle.static import InputSpec
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from ..placement_type import get_shard_spec
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from .utils import convert_to_dims_mapping
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class DistributedInputSpec(InputSpec):
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def __init__(
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self,
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shape,
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dtype='float32',
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name=None,
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stop_gradient=False,
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mesh=None,
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placements=None,
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local_shape=None,
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):
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super().__init__(shape, dtype, name, stop_gradient)
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self.mesh = copy.deepcopy(mesh)
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sharding_specs = get_shard_spec(mesh, placements, len(self.shape))
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self.dims_mapping = convert_to_dims_mapping(sharding_specs, mesh)
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self.local_shape = local_shape
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@classmethod
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def from_dtensor(cls, dtensor, name=None, shape=None):
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"""
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Generates a DistributedInputSpec based on dist tensor.
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Args:
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dtensor: the dist tensor.
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Returns:
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A DistributedInputSpec instance generated from dtensor.
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"""
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return cls(
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shape=dtensor.shape if shape is None else shape,
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dtype=dtensor.dtype,
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name=name,
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stop_gradient=dtensor.stop_gradient,
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mesh=dtensor.process_mesh,
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placements=dtensor.placements,
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local_shape=dtensor._local_value().shape,
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)
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def __repr__(self):
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return f"{super().__repr__()}, mesh:{self.mesh}, placements:{self.dims_mapping}"
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