Improve perf for mem efficient grad mgmt #20480
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orttraining/orttraining/python/training/ortmodule/_mem_efficient_grad_mgmt.py
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### Improve perf for mem efficient grad mgmt When memory efficient gradient mangement feature is enabled, the weight retrieval PythonOp for every layers will be launched at the beginning of the forward, which would make GPU stream idle for few milliseconds. The reason is the ReversedDFS ordering cannot ALWAYS handle such input branching well, so we introduce a distantance-to-input_leaf concepts when doing the reversedDFS, which not only move the problematical PythonOp to the place where it is needed, but also those Cast ops following the weight retrieval to the place where it is needed. Main branch: 102.19 - 26.35s = 75.84s for 260 steps(4627samples), 61.04sample/second This PR: 100.28s - 25.10s = 75.18s for 260 steps. 61.54samples/second (+0.8% gains) Main branch:  This PR:  ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
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Improve perf for mem efficient grad mgmt
When memory efficient gradient mangement feature is enabled, the weight retrieval PythonOp for every layers will be launched at the beginning of the forward, which would make GPU stream idle for few milliseconds. The reason is the ReversedDFS ordering cannot ALWAYS handle such input branching well, so we introduce a distantance-to-input_leaf concepts when doing the reversedDFS, which not only move the problematical PythonOp to the place where it is needed, but also those Cast ops following the weight retrieval to the place where it is needed.
Main branch: 102.19 - 26.35s = 75.84s for 260 steps(4627samples), 61.04sample/second
This PR: 100.28s - 25.10s = 75.18s for 260 steps. 61.54samples/second (+0.8% gains)
Main branch:
This PR:
Motivation and Context