[fsdp, trainer] fix: save config parameters to wandb in SFT

[EasonZhong668]

What does this PR do?

This PR adds support for logging Config parameters to Weights & Biases (wandb) when running SFT training via fsdp_sft_trainer.py.

Previously, wandb overview panel did not include configuration details, making it harder to trace experiment setups, as shown below:

This PR ensures that all relevant config parameters are recorded, improving reproducibility and experiment tracking:

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Test

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torchrun --nnodes=1 --nproc_per_node=8
-m verl.trainer.fsdp_sft_trainer
data.train_files=/root/data/gsm8k/train.parquet
data.val_files=/root/data/gsm8k/test.parquet
data.train_batch_size=1024
data.prompt_key=extra_info
data.response_key=extra_info
data.prompt_dict_keys=['question']
data.response_dict_keys=['answer']
data.micro_batch_size_per_gpu=4
model.partial_pretrain=Qwen/Qwen2.5-7B-Instruct
optim.lr=1e-5
optim.betas='[0.9,0.95]'
optim.weight_decay=0.01
optim.warmup_steps_ratio=0.05
optim.clip_grad=1.0
optim.lr_scheduler=cosine
trainer.default_local_dir=/workspace/verl/checkpoints/sft/gsm8k/qwen-7b
trainer.project_name=gsm8k-sft
trainer.experiment_name=gsm8k-sft-qwen-7b
trainer.total_epochs=4
trainer.logger='["wandb"]'

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[gemini-code-assist]

Code Review

This pull request adds support for logging configuration parameters to Weights & Biases, improving experiment tracking and reproducibility. The implementation is straightforward. The review includes one high-severity comment regarding the placement of an import statement to ensure consistency and adherence to Python's standard style guide (PEP 8).

[eric-haibin-lin]

Merged. Thx!