mindspore.merge_sliced_parameter

mindspore.merge_sliced_parameter(sliced_parameters, strategy=None)[source]

Merge parameter slices into one parameter. Used in the case of distributed inference.

Parameters

sliced_parameters (list[Parameter]) – Parameter slices in order of rank id.
strategy (Optional[dict]) – Parameter slice strategy, whose key is parameter name and value is slice strategy of this parameter. If strategy is None, just merge parameter slices in 0 axis order. Default: None.

Returns

Parameter, the merged parameter which has the whole data.

Raises

ValueError – Failed to merge.
TypeError – The sliced_parameters is incorrect or strategy is not dict.
KeyError – The parameter name is not in keys of strategy.

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Tensor, Parameter
>>>
>>> sliced_parameters = [
...                      Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])),
...                                "network.embedding_table"),
...                      Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])),
...                                "network.embedding_table"),
...                      Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])),
...                                "network.embedding_table"),
...                      Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])),
...                                "network.embedding_table")]
>>> merged_parameter = ms.merge_sliced_parameter(sliced_parameters)
>>> print(merged_parameter)
Parameter (name=network.embedding_table, shape=(12,), dtype=Float64, requires_grad=True)