[Core] NGram GPU Implementation compatible with Async Scheduler

tests/v1/e2e/test_async_scheduling.py

@@ -73,7 +73,7 @@ def test_without_spec_decoding(
     run_tests(monkeypatch, MODEL, test_configs, test_sampling_params)
 
 
-def test_with_spec_decoding(monkeypatch: pytest.MonkeyPatch):
+def test_with_eagle3_spec_decoding(monkeypatch: pytest.MonkeyPatch):
     """Test consistency and acceptance rates with some different combos of
     preemption, executor, async scheduling, prefill chunking,
     spec decoding model length.
@@ -106,6 +106,42 @@ def test_with_spec_decoding(monkeypatch: pytest.MonkeyPatch):
     run_tests(monkeypatch, MTP_MODEL, test_configs, [{}])
 
 
+def test_with_ngram_gpu_spec_decoding(monkeypatch: pytest.MonkeyPatch):
+    """Test ngram_gpu speculative decoding with different configurations.
+
+    This test specifically validates ngram_gpu behavior with various:
+    - Number of speculative tokens (2-6)
+    - Prompt lookup window sizes (min/max)
+    - Async scheduling enabled (as in production)
+    - Different executors and chunking settings
+    """
+
+    # Variant with larger speculation window
+    ngram_gpu_config = {
+        "method": "ngram_gpu",
+        "num_speculative_tokens": 3,
+        "prompt_lookup_max": 3,
+        "prompt_lookup_min": 2,
+    }
+
+    # Test configurations covering various scenarios
+    # test_preemption, executor, async_scheduling,
+    # spec_config, test_prefill_chunking
+    test_configs = [
+        (False, "mp", False, None, False),
+        (False, "mp", False, ngram_gpu_config, False),
+        (True, "mp", False, ngram_gpu_config, True),
+        (False, "mp", True, ngram_gpu_config, False),
+        (True, "mp", True, ngram_gpu_config, False),
+        (True, "uni", True, ngram_gpu_config, False),
+        (True, "mp", True, ngram_gpu_config, True),
+    ]
+
+    # Use MODEL (Qwen) for ngram_gpu tests as it's lighter weight
+    # and ngram_gpu doesn't require a specific draft model
+    run_tests(monkeypatch, MODEL, test_configs, [{}])
+
+
 @dynamo_config.patch(cache_size_limit=16)
 def run_tests(
     monkeypatch: pytest.MonkeyPatch,
@@ -217,18 +253,19 @@ def run_test(
         else dict(gpu_memory_utilization=0.9)
     )
     spec_mml = (spec_config or {}).get("max_model_len")
+    spec_method = (spec_config or {}).get("method", "none")
     test_config = (
         f"executor={executor}, preemption={test_preemption}, "
         f"async_sched={async_scheduling}, "
         f"chunk_prefill={test_prefill_chunking}, "
-        f"spec_decoding={spec_decoding}, spec_mml={spec_mml}"
+        f"spec_decoding={spec_decoding}, spec_method={spec_method}, spec_mml={spec_mml}"
     )
     print("-" * 80)
     print(f"---- TESTING {test_str}: {test_config}")
     print("-" * 80)
     with VllmRunner(
         model,
-        max_model_len=512,
+        max_model_len=4096,
         enable_chunked_prefill=test_prefill_chunking,
         # Force prefill chunking
         max_num_batched_tokens=48 if test_prefill_chunking else None,

vllm/config/speculative.py

@@ -40,13 +40,15 @@
     "pangu_ultra_moe_mtp",
 ]
 EagleModelTypes = Literal["eagle", "eagle3", MTPModelTypes]
+NgramGPUTypes = Literal["ngram_gpu"]
 SpeculativeMethod = Literal[
     "ngram",
     "medusa",
     "mlp_speculator",
     "draft_model",
     "suffix",
     EagleModelTypes,
+    NgramGPUTypes,
 ]
 
 
@@ -260,6 +262,8 @@ def __post_init__(self):
                     self.quantization = self.target_model_config.quantization
             elif self.method in ("ngram", "[ngram]"):
                 self.model = "ngram"
+            elif self.method == "ngram_gpu":
+                self.model = "ngram_gpu"
             elif self.method == "suffix":
                 self.model = "suffix"
             else:
@@ -274,9 +278,10 @@ def __post_init__(self):
         ):
             self.method = "ngram"
 
-        if self.method in ("ngram", "[ngram]"):
+        if self.method in ("ngram", "[ngram]", "ngram_gpu"):
             # Unified to "ngram" internally
-            self.method = "ngram"
+            if self.method in ("ngram", "[ngram]"):
+                self.method = "ngram"
             # Set default values if not provided
             if self.prompt_lookup_min is None and self.prompt_lookup_max is None:
                 # TODO(woosuk): Tune these values. They are arbitrarily chosen.

vllm/config/vllm.py

@@ -21,7 +21,7 @@
 from pydantic.dataclasses import dataclass
 
 import vllm.envs as envs
-from vllm.config.speculative import EagleModelTypes
+from vllm.config.speculative import EagleModelTypes, NgramGPUTypes
 from vllm.logger import enable_trace_function_call, init_logger
 from vllm.transformers_utils.runai_utils import is_runai_obj_uri
 from vllm.utils import random_uuid
@@ -378,10 +378,12 @@ def __post_init__(self):
             # Currently, async scheduling only support eagle speculative
             # decoding.
             if self.speculative_config is not None:
-                if self.speculative_config.method not in get_args(EagleModelTypes):
+                if self.speculative_config.method not in get_args(
+                    EagleModelTypes
+                ) and self.speculative_config.method not in get_args(NgramGPUTypes):
                     raise ValueError(
                         "Currently, async scheduling is only supported "
-                        "with EAGLE/MTP kind of speculative decoding"
+                        "with EAGLE/MTP/NGram GPU kind of speculative decoding"
                     )
                 if self.speculative_config.disable_padded_drafter_batch:
                     raise ValueError(