[Bugfix] Fix the Eagle3 inference failure issue

[sunchendd]

What this PR does / why we need it?

Fix the Eagle3 inference failure issue.
error message: "EngineCore encountered an issue. See stack trace (above) for the root cause."

Fixes #4323

How was this patch tested?

vllm serve /nfs/1_AscendPackage/05_weights_public/Qwen3-32B \ --served-model-name Qwen3-32B \ -tp 4 \ --host "0.0.0.0" \ --port "8000" \ --trust-remote-code \ --speculative-config '{"method":"eagle3","model":"/home/scd/qwen3_32b_eagle3/","num_speculative_tokens":4,"draft_tensor_parallel_size":1}' \ --max-num-batched-tokens 4096 \ --max-model-len 4096

curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen3-32B",
        "prompt": "hi, where is the capital of France?",
        "max_tokens": 10,
        "temperature": 0
    }' | python3 -m json.tool

vLLM version: v0.11.0
vLLM-ascend version: v0.11.0rc2

• vLLM version: v0.11.2
• vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

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[gemini-code-assist]

Code Review

This pull request fixes an inference failure with Eagle3 speculative decoding. The changes primarily involve updating the attention mask generation logic in get_splitfuse_attn_mask to handle different scenarios correctly, adjusting the attention state for Eagle3 in the model runner, and modifying how the attention mask is obtained in the Eagle proposer. The core logic of the fix seems sound. I've identified a critical issue in vllm_ascend/worker/model_runner_v1.py where the logic for determining the attention state for Eagle3 speculative decoding could be incorrect, potentially leading to the use of a wrong attention mechanism. I've provided a detailed comment and a suggested fix for this.

[gemini-code-assist]

The logic to determine the attention state for Eagle3 speculative decoding appears to be incorrect. Currently, it sets attn_state to AscendAttentionState.ChunkedPrefill for Eagle and Eagle3, and AscendAttentionState.SpecDecoding for other drafters. However, ChunkedPrefill is typically used for prefill stages, not for speculative decoding which happens after prefill. For speculative decoding, AscendAttentionState.SpecDecoding should be used to ensure the correct attention mechanism is applied. Using ChunkedPrefill here could lead to incorrect attention masks and potential failures during the decoding phase.

Suggested change

	if self.drafter and self.drafter.name in (SpecDcodeType.EAGLE,
	SpecDcodeType.EAGLE3):
	attn_state = AscendAttentionState.ChunkedPrefill
	else:
	attn_state = AscendAttentionState.SpecDecoding
	if self.drafter and self.drafter.name in (SpecDcodeType.EAGLE,
	SpecDcodeType.EAGLE3):
	attn_state = AscendAttentionState.SpecDecoding
	else:
	attn_state = AscendAttentionState.SpecDecoding