triton KV append kernel hard-asserts CUDA, blocking non-CUDA Triton backends

### Bug description

`triton_append_key_value_cache` in `megatron/core/inference/contexts/fused_kv_append_kernel.py` hard-asserts that all tensors are on CUDA:

```python
assert (
key.device.type == 'cuda'
and value.device.type == 'cuda'
and memory_buffer.device.type == 'cuda'
), "All tensors must be on CUDA devices."
```

The kernel itself is pure Triton and device-agnostic; the assert is the only CUDA-ism in the function. On any accelerator that exposes a Triton backend but is not CUDA (e.g. Ascend NPU, where `torch_npu` provides the Triton backend and the device type is `npu`), the dynamic-batching inference path hits this assert on the first KV append even though the kernel would run fine.

### Environment

- Megatron-LM main @ 1d82259e1
- Non-CUDA accelerator with a Triton backend (e.g. Ascend 910B, device type `npu`)

### Steps to reproduce

Run the dynamic-batching inference path with `triton_append_key_value_cache` (the fused KV-append kernel) on a non-CUDA Triton backend. The CUDA-only device assert fires in the input-validation preamble.

### Expected behavior

The triton kernel is a device-agnostic launchable kernel and should run on any device backed by Triton. The assert's real intent is to reject tensors that cannot be launched on a Triton backend at all (CPU, meta); it should be expressed that way instead of as a CUDA allowlist, so it holds for every current and future Triton-backed accelerator.

### Proposed fix

```python
assert (
key.device.type not in ('cpu', 'meta')
and value.device.type not in ('cpu', 'meta')
and memory_buffer.device.type not in ('cpu', 'meta')
), "All tensors must be on a device with a Triton backend (CUDA, NPU, ...)."
```

Open megatron/core/inference/contexts/fused_kv_append_kernel.py and inspect triton_append_key_value_cache, especially its input-validation preamble. Reproduce the dynamic-batching inference path on a non-CUDA Triton backend, then verify that valid accelerator tensors pass while CPU and meta tensors remain rejected.

Written by the indexing model from the issue text.