Tuple as device_type input to support Heterogenous Sharding of tables across different device_typestable #2600
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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… across different device_typestable (pytorch#2600) Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard Differential Revision: D65933148
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Summary: As we plan to support heterogenous sharding across different device types (cuda / cpu etc), we will pass device type per shard in the format of tuple for device_type_from_sharding_info where each index will represent the device_type for that particular shard
Differential Revision: D65933148