Split learning LLM example.

[dixiyao]

Description

• Added three more API functions in the plato/trainer/split_learning: forward_to_intermediate_feature, update_weights_before_cut, test_model_split_leaffect. Revised the API for better design such as avoiding possible memory leakage.
• The revision will not affect previous split learning examples.
• Provide an example of fine-tuning Huggingface (GPT-2, OPT) with the split learning. First, the split_learning_llm_model.py includes the client model and the server model the contain layers on the client or on the server. The split_learning_trainer.py shows how we can use the current split learning API to train LLM with split learning. We directly use the huggingface trainer during the testing phase.
• The LoRA fine-tuning is supported by using LoRA model in split_learning_llm_model.py and the LoRA algorithm for split learning split_learning_lora_algorithm.py.

How has this been tested?

To test the GPT2

python ./examples/split_learning/llm_split_learning/split_learning_main.py -c ./examples/split_learning/llm_split_learning/split_learning_wikitext2_gpt2.yml 

To test the OPT

python ./examples/split_learning/llm_split_learning/split_learning_main.py -c ./examples/split_learning/llm_split_learning/split_learning_wikitext2_opt350m.yml

To test the LoRA GPT2

python ./examples/split_learning/llm_split_learning/split_learning_main.py -c ./examples/split_learning/llm_split_learning/split_learning_wikitext2_gpt2_lora.yml 

Types of changes

• Bug fix (non-breaking change which fixes an issue) Fixes #
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)

Checklist:

• My code has been formatted using Black and checked using PyLint.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.

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