A5 support reshape and cache in CP situation

[lenghuixing0330]

What this PR does / why we need it?

The reshape and cache operators of A5 require that the inputs be contiguous.
In some scenarios, such as sequence parallelism, there are some non-contiguous operations, such as slicing with intervals.
slot_mapping = attn_metadata.slot_mapping[: num_decode_tokens * self.pcp_size : self.pcp_size], where slot_mapping is non-contiguous and needs to be contiguous.

Does this PR introduce any user-facing change?

How was this patch tested?

• vLLM version: v0.18.0
• vLLM main: vllm-project/vllm@ed359c4

[gemini-code-assist]

Summary of Changes

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This pull request enhances A5 device support for KV cache reshape and cache operations within the context parallel attention module. It streamlines the interaction with device-specific operations by routing them through a dedicated DeviceOperator, standardizes parameter naming for clarity, and enforces tensor contiguity for key and slot mapping data, which is vital for efficient processing on NPU hardware.

Highlights

• Centralized KV Cache Operations: The reshape_and_cache calls within the context parallel attention mechanism have been refactored to utilize the DeviceOperator class, centralizing device-specific operations.
• Parameter Name Standardization: The parameter name slot_indices has been updated to slot_mapping in the reshape_and_cache function calls for consistency.
• Tensor Contiguity Enforcement: The reshape_and_cache method in DeviceOperator now explicitly ensures that the key and slot_mapping tensors are contiguous before being passed to the underlying NPU scatter KV cache function, which can be crucial for performance and correctness on Ascend devices.

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[gemini-code-assist]

Code Review

This pull request refactors the reshape_and_cache operation by introducing a DeviceOperator abstraction. It moves the direct calls to torch_npu._npu_reshape_and_cache into this new operator, improving modularity and maintainability. Additionally, the reshape_and_cache method within the A5DeviceAdaptor is updated to ensure that the key, value, and slot_mapping tensors are contiguous before being passed to torch_npu.npu_scatter_pa_kv_cache, which can enhance performance and prevent potential issues with non-contiguous data. The argument name slot_indices is also updated to slot_mapping for consistency. There are no review comments to address.