[BugFix] Fix eagle3 accuracy problem when enforce_eager=True (#4521)

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

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
def main():
    prompts = [
        "The future of AI is",
    ]

    # Create a sampling params object.
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
    # Create an LLM.
    llm = LLM(
            model="meta-llama/Llama-3.1-8B-Instruct",
            tensor_parallel_size=1,
            speculative_config={
                "method": "eagle3",
                "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
                "num_speculative_tokens": 3
            },
            enforce_eager=True,
        )

    # Generate texts from the prompts.
    outputs = llm.generate(prompts, sampling_params)
    print(f"Outputs: {outputs}")
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

- vLLM version: v0.12.0
- vLLM main:
vllm-project/vllm@ad32e3e

---------

Signed-off-by: zhaomingyu <[email protected]>
Co-authored-by: wangxiyuan <[email protected]>

Author: zhaomingyu13

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -110,7 +110,7 @@ def test_eagle_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using eagle speculative decoding.
     '''
-    pytest.skip("exist OOM error")
+    pytest.skip("To be aligned with GPU")
     ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=False)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
     del ref_llm

vllm_ascend/spec_decode/eagle_proposer.py

@@ -79,7 +79,7 @@ def __init__(self,
                                    dtype=torch.int32)
         attn_mask_len = self.vllm_config.model_config.max_model_len
         self.attn_mask_builder = AttentionMaskBuilder(
-            attn_mask_len, self.vllm_config.model_config.dtype)
+            attn_mask_len, self.vllm_config.model_config.dtype, device=device)
 
     def load_model(self, model: nn.Module) -> None:
         target_attn_layer_names = set(
@@ -430,9 +430,7 @@ def _propose(
 
         query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
         max_query_len = query_lens.max().item()
-        attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
-            seq_lens, target_positions, self.vllm_config.model_config.dtype,
-            self.device)
+        attn_mask = self.runner.attn_mask
 
         common_attn_metadata = AscendCommonAttentionMetadata(
             query_start_loc=cu_num_tokens.to(device),
@@ -507,9 +505,15 @@ def _propose(
         attn_metadata.num_actual_tokens = batch_size
         attn_metadata.max_query_len = 1
         attn_metadata.query_start_loc = self.arange[:batch_size + 1]
+        attn_metadata.query_start_loc_list = attn_metadata.query_start_loc[
+            1:].tolist()
+        attn_metadata.num_decodes, attn_metadata.num_prefills, attn_metadata.num_decode_tokens, attn_metadata.num_prefill_tokens = 0, batch_size, 0, batch_size
+        attn_metadata.num_actual_tokens_pcp_padded = attn_metadata.num_decode_tokens + attn_metadata.num_prefill_tokens
         query_lens.fill_(1)
         attn_metadata.query_lens = query_lens
 
+        attn_metadata.actual_seq_lengths_q = [1 + i for i in range(batch_size)]
+        attn_metadata.seq_lens_list = seq_lens.tolist()
         attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
         for now_speculative in range(
                 self.vllm_config.speculative_config.num_speculative_tokens -
@@ -536,6 +540,9 @@ def _propose(
             # TODO: Increment the sequence lengths.
 
             attn_metadata.seq_lens += 1
+            attn_metadata.seq_lens_list = [
+                _ + 1 for _ in attn_metadata.seq_lens_list
+            ]
             # TODO: Consider max model length.
             # attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
             #                                 self.max_model_len)