Introduce NgramEmbeddingManager component (#31154)

test/registered/unit/layers/attention/test_gdn_prefill_backend_policy.py

@@ -37,6 +37,7 @@ def make_runner(
 
     return SimpleNamespace(
         server_args=args,
+        model_config=SimpleNamespace(),
         hybrid_gdn_config=SimpleNamespace(
             linear_key_head_dim=key_dim,
             linear_value_head_dim=value_dim,
@@ -60,6 +61,11 @@ class TestFlashInferGDNPrefillBackendPolicy(unittest.TestCase):
         flashinfer_available=True,
     ):
         with (
+            patch.object(
+                gdn_backend,
+                "hybrid_gdn_config",
+                return_value=runner.hybrid_gdn_config,
+            ),
             patch.object(gdn_backend, "is_cuda", return_value=cuda),
             patch.object(torch.cuda, "get_device_capability", return_value=capability),
             patch.object(torch.version, "cuda", cuda_version),

test/registered/unit/managers/test_scheduler_chunked_req_gate.py

@@ -97,7 +97,10 @@ def _scheduler_for_get_next_batch(*, tree_cache, chunked_req) -> Scheduler:
     s.dp_attn_adapter.maybe_prepare_mlp_sync_batch = MagicMock(
         side_effect=lambda batch, **_: batch
     )
-    s._maybe_prepare_ngram_embedding = MagicMock(side_effect=lambda batch: batch)
+    s.ngram_embedding_manager = MagicMock()
+    s.ngram_embedding_manager.prepare_for_forward = MagicMock(
+        side_effect=lambda batch, **_: batch
+    )
     s.update_running_batch = MagicMock(side_effect=lambda batch: batch)
     s.tree_cache = tree_cache
     s.chunked_req = chunked_req

test/registered/unit/model_executor/model_runner_components/test_ngram_embedding_manager.py

@@ -11,7 +11,7 @@ from sglang.test.test_utils import CustomTestCase, maybe_stub_sgl_kernel
 
 maybe_stub_sgl_kernel()
 
-from sglang.srt.model_executor.ngram_token_table import (  # noqa: E402
+from sglang.srt.model_executor.model_runner_components.ngram_embedding_manager import (  # noqa: E402
     update_ngram_token_table_after_sampling,
 )
 
@@ -37,7 +37,7 @@ class TestNgramTokenTableUpdate(CustomTestCase):
         seq_lens = torch.tensor([11, 22, 33, 44], dtype=torch.int64)
 
         with patch(
-            "sglang.srt.model_executor.ngram_token_table.update_token_table"
+            "sglang.srt.model_executor.model_runner_components.ngram_embedding_manager.update_token_table"
         ) as update_mock:
             updated = update_ngram_token_table_after_sampling(
                 ngram_embedding_info=info,
@@ -71,7 +71,7 @@ class TestNgramTokenTableUpdate(CustomTestCase):
         info = _make_ngram_info(2, skip_token_table_update=torch.tensor([True, True]))
 
         with patch(
-            "sglang.srt.model_executor.ngram_token_table.update_token_table"
+            "sglang.srt.model_executor.model_runner_components.ngram_embedding_manager.update_token_table"
         ) as update_mock:
             updated = update_ngram_token_table_after_sampling(
                 ngram_embedding_info=info,
@@ -91,7 +91,7 @@ class TestNgramTokenTableUpdate(CustomTestCase):
         seq_lens = torch.tensor([11, 22, 33], dtype=torch.int64)
 
         with patch(
-            "sglang.srt.model_executor.ngram_token_table.update_token_table"
+            "sglang.srt.model_executor.model_runner_components.ngram_embedding_manager.update_token_table"
         ) as update_mock:
             updated = update_ngram_token_table_after_sampling(
                 ngram_embedding_info=info,