[Feat] Support async_scheduler and disable_padded_drafter_batch in eagle (#4893)

### What this PR does / why we need it?
We refactored the eagle_proposer.py to adapt the framework of eagle.py
in vllm-v0.12.0, to support the logit of padded drafter batch and
async-scheduler.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: drslark <slarksblood@qq.com>

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -7,9 +7,10 @@ import random
 from typing import Any
 
 import pytest
+from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
 
-from tests.e2e.conftest import VllmRunner
+from tests.e2e.conftest import VllmRunner, cleanup_dist_env_and_memory
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
@@ -115,41 +116,67 @@ 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("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
-
+    # NOTE: e2e of eagle has many problems before.
+    # We first check whether it is functioning properly.
+    # Should fix the e2e with VllmRunner in future.
     spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
-    with VllmRunner(
-            model_name,
-            max_num_seqs=1,
-            max_num_batched_tokens=2048,
-            gpu_memory_utilization=0.6,
-            speculative_config={
-                "method": "eagle3" if use_eagle3 else "eagle",
-                "model": spec_model_name,
-                "num_speculative_tokens": 2,
-                "max_model_len": 128,
-            },
-            max_model_len=128,
-            enforce_eager=False,
-    ) as runner:
-        spec_outputs = runner.model.chat(test_prompts, sampling_config)
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    prompts = [{
+        "role": "user",
+        "content": "Hello, my name is"
+    }, {
+        "role": "user",
+        "content": "The president of the United States is"
+    }, {
+        "role": "user",
+        "content": "The capital of France is"
+    }, {
+        "role": "user",
+        "content": "The future of AI is"
+    }]
+    prompts = [
+        tokenizer.apply_chat_template(
+            [prompt],
+            tokenize=False,
+            add_generation_prompt=True,
+        ) for prompt in prompts
+    ]
 
-    matches = 0
-    misses = 0
-    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-        if ref_output.outputs[0].text == spec_output.outputs[0].text:
-            matches += 1
-        else:
-            misses += 1
-            print(f"ref_output: {ref_output.outputs[0].text}")
-            print(f"spec_output: {spec_output.outputs[0].text}")
+    sampling_params = SamplingParams(
+        max_tokens=300,
+        temperature=0.0,
+        ignore_eos=False,
+    )
 
-    # Heuristic: expect at least 66% of the prompts to match exactly
-    # Upon failure, inspect the outputs to check for inaccuracy.
-    assert matches > int(0.66 * len(ref_outputs))
+    # Create an LLM.
+    llm = LLM(
+        model=model_name,
+        tensor_parallel_size=1,
+        pipeline_parallel_size=1,
+        data_parallel_size=1,
+        disable_log_stats=False,
+        max_model_len=4096,
+        seed=1024,
+        async_scheduling=True,
+        compilation_config={
+            "level": 3,
+            "cudagraph_mode": "FULL_DECODE_ONLY",
+            "cudagraph_num_of_warmups": 1,
+            "cudagraph_capture_sizes": [12],
+        },
+        speculative_config={
+            "disable_padded_drafter_batch": False,
+            "method": "eagle3" if use_eagle3 else "eagle",
+            "model": spec_model_name,
+            "num_speculative_tokens": 2,
+            "max_model_len": 128,
+            "draft_vocab_size": 128256,
+        },
+    )
+    llm.generate(prompts, sampling_params)
+    cleanup_dist_env_and_memory()
+    del llm
 
 
 @pytest.mark.skip(