Main2main upgrade to vllm 0317 afternoon (#7409)

### What this PR does / why we need it?

1.fix "TypeError: get_attn_backend() remove variable": [Refactor
`check_and_update_config`](https://github.com/vllm-project/vllm/pull/35122)

2.fix [Rename `compile_ranges_split_points` to
`compile_ranges_endpoints`](https://github.com/vllm-project/vllm/pull/36027)

3.fix "RuntimeError: device_allocator not a DeviceAllocator":[Replace
memory related torch.cuda
APIs"](https://github.com/vllm-project/vllm/pull/37031)

4.fix [Support multiple KV groups in OffloadingSpec
](https://github.com/vllm-project/vllm/pull/36610) removed
self.offloaded_block_size and changed self.gpu_block_size from a scalar
to a tuple of per-group block sizes, adding block_size_factor.

5.fix [Consolidate
SupportsEagle](https://github.com/vllm-project/vllm/pull/36063) renamed
get_eagle3_aux_hidden_state_layers() to
get_eagle3_default_aux_hidden_state_layers() and added a
supports_eagle3() guard before calling it.

### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
E2E


- vLLM version: v0.17.0
- vLLM main:
8a680463fa

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: Claude Code <noreply@anthropic.com>

vllm_ascend/patch/platform/patch_torch_accelerator.py

@@ -6,3 +6,11 @@ def patch_empty_cache() -> None:
 
 
 torch.accelerator.empty_cache = patch_empty_cache
+
+# Monkey-patch torch.accelerator memory APIs for NPU compatibility.
+# Upstream vLLM (commit 747b068) replaced current_platform.memory_stats()
+# with torch.accelerator.memory_stats(), but torch.accelerator does not
+# properly delegate to NPU. We redirect to torch.npu.* equivalents.
+torch.accelerator.memory_stats = torch.npu.memory_stats  # type: ignore[attr-defined]
+torch.accelerator.memory_reserved = torch.npu.memory_reserved  # type: ignore[attr-defined]
+torch.accelerator.reset_peak_memory_stats = torch.npu.reset_peak_memory_stats  # type: ignore[attr-defined]