[Speculators] Move tests + fix integration (#27308)

Signed-off-by: Dipika Sikka <dipikasikka1@gmail.com>
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
Signed-off-by: rahul-tuli <rtuli@redhat.com>
Co-authored-by: Rahul Tuli <rtuli@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>

tests/v1/e2e/test_spec_decode.py

@@ -121,6 +121,86 @@ def test_ngram_correctness(
     cleanup_dist_env_and_memory()
 
 
+@pytest.mark.parametrize(
+    "model_path",
+    [
+        "RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3",
+        "RedHatAI/Qwen3-8B-speculator.eagle3",
+    ],
+    ids=["llama3_eagle3_speculator", "qwen3_eagle3_speculator"],
+)
+def test_speculators_model_integration(
+    monkeypatch: pytest.MonkeyPatch,
+    sampling_config: SamplingParams,
+    model_path: str,
+):
+    """
+    Test that speculators models work with the simplified integration.
+
+    This verifies the `vllm serve <speculator-model>` use case where
+    speculative config is automatically detected from the model config
+    without requiring explicit --speculative-config argument.
+
+    Tests:
+    1. Speculator model is correctly detected
+    2. Verifier model is extracted from speculator config
+    3. Speculative decoding is automatically enabled
+    4. Text generation works correctly
+    5. Output matches reference (non-speculative) generation
+    """
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    # Generate test prompts
+    test_prompts = get_test_prompts(mm_enabled=False)
+
+    # First run: Direct speculator model (simplified integration)
+    spec_llm = LLM(model=model_path, max_model_len=1024)
+    spec_outputs = spec_llm.chat(test_prompts, sampling_config)
+
+    # Verify speculative config was auto-detected
+    assert spec_llm.llm_engine.vllm_config.speculative_config is not None, (
+        f"Speculative config should be auto-detected for {model_path}"
+    )
+
+    spec_config = spec_llm.llm_engine.vllm_config.speculative_config
+    assert spec_config.num_speculative_tokens > 0, (
+        f"Expected positive speculative tokens, "
+        f"got {spec_config.num_speculative_tokens}"
+    )
+
+    # Verify draft model is set to the speculator model
+    assert spec_config.model == model_path, (
+        f"Draft model should be {model_path}, got {spec_config.model}"
+    )
+
+    # Extract verifier model for reference run
+    verifier_model = spec_llm.llm_engine.vllm_config.model_config.model
+
+    del spec_llm
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
+
+    # Second run: Reference without speculative decoding
+    ref_llm = LLM(model=verifier_model, max_model_len=1024)
+    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
+    del ref_llm
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
+
+    # Compare outputs
+    matches = sum(
+        1
+        for ref, spec in zip(ref_outputs, spec_outputs)
+        if ref.outputs[0].text == spec.outputs[0].text
+    )
+
+    # Heuristic: expect at least 66% of prompts to match exactly
+    assert matches >= int(0.66 * len(ref_outputs)), (
+        f"Only {matches}/{len(ref_outputs)} outputs matched. "
+        f"Expected at least {int(0.66 * len(ref_outputs))} matches."
+    )
+
+
 @pytest.mark.parametrize(
     ["model_setup", "mm_enabled"],
     [

tests/v1/spec_decode/test_speculators_eagle3.py

@@ -22,10 +22,6 @@ from vllm.model_executor.models.interfaces import supports_eagle3
             "nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized-w4a16",
             id="qwen3-eagle3-speculator-w4a16-verifier",
         ),
-        pytest.param(
-            "nm-testing/random-weights-llama3.1.8b-2layer-eagle3",
-            id="llama3-eagl3-multiple-layers",
-        ),
     ],
 )
 def test_eagle3_speculators_model(