[V1][Spec Decode] Handle draft tokens beyond max_model_len (#16087)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/v1/spec_decode/test_max_len.py

@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Test whether spec decoding handles the max model length properly."""
+
+import pytest
+
+from vllm import LLM, SamplingParams
+
+_PROMPTS = [
+    "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
+    "Repeat the following sentence 10 times: Consistency is key to mastering any skill.",  # noqa: E501
+    "Who won the Turing Award in 2018, and for what contribution? Describe in detail.",  # noqa: E501
+]
+
+
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
+def test_ngram_max_len(
+    monkeypatch: pytest.MonkeyPatch,
+    num_speculative_tokens: int,
+):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+
+        llm = LLM(
+            model="facebook/opt-125m",
+            max_model_len=100,
+            enforce_eager=True,  # For faster initialization.
+            speculative_config={
+                "method": "ngram",
+                "prompt_lookup_max": 5,
+                "prompt_lookup_min": 3,
+                "num_speculative_tokens": num_speculative_tokens,
+            },
+        )
+        sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
+        llm.generate(_PROMPTS, sampling_params)
+
+
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
+def test_eagle_max_len(
+    monkeypatch: pytest.MonkeyPatch,
+    num_speculative_tokens: int,
+):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+
+        llm = LLM(
+            model="meta-llama/Meta-Llama-3-8B-Instruct",
+            enforce_eager=True,  # For faster initialization.
+            speculative_config={
+                "method": "eagle",
+                "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
+                "num_speculative_tokens": num_speculative_tokens,
+            },
+            max_model_len=100,
+        )
+        sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
+        llm.generate(_PROMPTS, sampling_params)