Enhance get_balanced_memory to ensure adequate GPU allocation for small models with large layers

[xin3he]

What does this PR do?

Fixes intel/auto-round#1889

For W2A16 facebook/opt-125m, we have below param size:

'model': 185272320, 
'model.decoder.embed_tokens': 154435584,

When there are two GPUs, the current logic will split the param size by //2 and the embedding is dispatched on CUDA:1
max_memory = {0: 97288832, 1: 84647280640}

lm_eval requires embedding on cuda:0 to match the input device.

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[github-actions]

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CI recap

Dashboard: View test results in Grafana
Latest run: 30235719757:2
Result: failure | Jobs: 2 | Tests: 6 | Failures: 1 | Duration: 33s

Code quality check failed: test jobs were skipped. Fix the code quality issues and push again to run tests.

[Rocketknight1]

cc @SunMarc for accelerate

[SunMarc]

Thanks for tacking this ! Left a comment.

[SunMarc]

We should get the max_layer_size only from the leaves as the leaves should include the embeddings. I feel that we are taking into account modules that shouldn't be counted. For the no_split_module, it will still be accounted in the buffer above so that's fine to not deal with this case. Also we should only modify the GPU0 memory for this case if possible. In the last GPU, we leave enough space for the rest.

[xin3he]

Thanks, updated