[NPU] fix some npu error with OffloaderV2

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Motivation

when set --offload-mode=meta or sharded_gpu in feature offloaderV2, it doesn't work with npu.Then I fix some error to support those features in npu.

Modifications

1.fix attr weight_loader of params missing in func _move_param_to_meta
2.Skip format cast for meta tensors
3.process tensor to be contiguous

For this configuration:
python -m sglang.launch_server
--model-path /home/weights/deepseekv3-lite-base-latest
--host 127.0.0.1
--port 8080
--attention-backend ascend
--mem-fraction-static 0.9
--base-gpu-id 14
--tp 1
--dp 2
--offload-num-in-group 1
--offload-prefetch-step 1
--offload-mode sharded_gpu
--offload-group-size 4
--disable-cuda-graph \

curl --location 'http://127.0.0.1:8080/generate' --header 'Content-Type:application/json' --data '{"text": "The captial of France is", "sampling_params": {"temperature": 0, "max_new_tokens": 20}}

Before:

After the first modification:

After the second modification:

After the third modification:
the output is nomal

the third modification sovled another OffloaderV1 Accuracy error
When forwarding, torch.Tensor.to(device) changes the layout of non-contiguous tensors, causing an accuracy error in the MoE model in offloaderV1 mode on TP2.
For this configuration

curl --location 'http://127.0.0.1:8080/generate' --header 'Content-Type:application/json' --data '{"text": "The captial of France is", "sampling_params": {"temperature": 0, "max_new_tokens": 20}}'
before

after

Accuracy Tests

Benchmarking and Profiling

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Update documentation according to Write documentations.
• Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.
• Follow the SGLang code style guidance.

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