[CI] Fix ngram & suffix test oom (#4755)

### What this PR does / why we need it?
Avoid oom during CI by using `with VllmRunner` instead of `LLM()`, and
enable `test_ngram_correctness`

### How was this patch tested?
CI passed.

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

---------

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

Author: fluctlux

.github/workflows/_e2e_test.yaml

@@ -108,8 +108,7 @@ jobs:
           # ------------------------------------ v1 spec decode test ------------------------------------ #
           pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py
           pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_torchair_correctness.py
-          # Fix me: test_eagle_correctness OOM error
-          #pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py
+          pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py
 
   e2e-2-cards:
     name: multicard-2

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from __future__ import annotations
 
+import os
 import random
 from typing import Any
 
@@ -9,6 +10,8 @@
 
 from tests.e2e.conftest import VllmRunner
 
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
 
 @pytest.fixture
 def test_prompts():
@@ -61,7 +64,6 @@ def eagle3_model_name():
     return "vllm-ascend/EAGLE3-LLaMA3.1-Instruct-8B"
 
 
-@pytest.mark.skip("TODO: Revert me after ngram oom issue on ci is fixed")
 def test_ngram_correctness(
     test_prompts: list[list[dict[str, Any]]],
     sampling_config: SamplingParams,
@@ -71,9 +73,11 @@ def test_ngram_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using ngram speculative decoding.
     '''
-    ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=False)
-    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-    del ref_llm
+
+    with VllmRunner(model_name, max_model_len=1024,
+                    enforce_eager=False) as ref_llm:
+        ref_outputs = ref_llm.model.chat(test_prompts, sampling_config)
+
     with VllmRunner(model_name,
                     speculative_config={
                         "method": "ngram",
@@ -156,9 +160,10 @@ def test_suffix_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using ngram speculative decoding.
     '''
-    ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=False)
-    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-    del ref_llm
+    with VllmRunner(model_name, max_model_len=1024,
+                    enforce_eager=False) as ref_llm:
+        ref_outputs = ref_llm.model.chat(test_prompts, sampling_config)
+
     with VllmRunner(model_name,
                     speculative_config={
                         "method": "suffix",