Summary:
Reopen the PR: https://github.com/pytorch/pytorch/pull/45837
This PR add a new feature for Partitioner() class called size_based_partition. Given a list of devices with the same memory size, this function could distribute graph nodes into different devices. To implement this feature, several help functions are created in Partitioner.py and GraphManipulation.py.
An unit test is also added in test/test_fx_experimental.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46282
Reviewed By: gcatron
Differential Revision: D24288470
Pulled By: scottxu0730
fbshipit-source-id: e81b1e0c56e34f61e497d868882126216eba7538
Summary:
WIP: This PR is working in progress for the partition of fx graph module. _class partitioner_ generates partitions for the graph module. _class partition_ is a partition node in the partitions.
_Partitioner()_ : create a partitioner
_partition_graph(self, fx_module: GraphModule, devices: List[str]) -> None_:
use fx graph module and devices as the input and create partition_ids for each node inside the graph module
_dump_partition_DAG(self) -> None_:
print out the information about each partition, including its id, its backend type (what type of device this partition uses), all the nodes included in this partition, its parent partitions, children partitions, input nodes, and output nodes.
So far, only a single partition is considered, which means there is only one device with unlimited memory.
A test unit call _test_find_single_partition()_ is added to test if all nodes in the graph are marked for the only partition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45429
Reviewed By: izdeby
Differential Revision: D24026268
Pulled By: scottxu0730
fbshipit-source-id: 119d506f33049a59b54ad993670f4ba5d8e15b0b
