[Bugfix] Fix embedding model hangs with --enable-metrics (#2822)

python/sglang/srt/configs/model_config.py

@@ -128,7 +128,7 @@ class ModelConfig:
         self.num_hidden_layers = self.hf_text_config.num_hidden_layers
         self.vocab_size = self.hf_text_config.vocab_size
 
-        # Veirfy quantization
+        # Verify quantization
         self._verify_quantization()
 
         # Cache attributes

python/sglang/srt/managers/tokenizer_manager.py

@@ -688,7 +688,7 @@ class TokenizerManager:
                     if self.enable_metrics:
                         completion_tokens = (
                             recv_obj.completion_tokens[i]
-                            if recv_obj.completion_tokens
+                            if getattr(recv_obj, "completion_tokens", None)
                             else 0
                         )
 
@@ -716,7 +716,11 @@ class TokenizerManager:
                                 time.time() - state.created_time
                             )
                             # Compute time_per_output_token for the non-streaming case
-                            if not state.obj.stream and completion_tokens >= 1:
+                            if (
+                                hasattr(state.obj, "stream")
+                                and not state.obj.stream
+                                and completion_tokens >= 1
+                            ):
                                 self.metrics_collector.observe_time_per_output_token(
                                     (time.time() - state.created_time)
                                     / completion_tokens

python/sglang/srt/model_executor/model_runner.py

@@ -724,7 +724,7 @@ class ModelRunner:
         elif forward_batch.forward_mode.is_idle():
             return self.forward_idle(forward_batch)
         else:
-            raise ValueError(f"Invaid forward mode: {forward_batch.forward_mode}")
+            raise ValueError(f"Invalid forward mode: {forward_batch.forward_mode}")
 
     def sample(
         self, logits_output: LogitsProcessorOutput, forward_batch: ForwardBatch

test/srt/test_openai_server.py

@@ -14,6 +14,7 @@ import openai
 from sglang.srt.hf_transformers_utils import get_tokenizer
 from sglang.srt.utils import kill_process_tree
 from sglang.test.test_utils import (
+    DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST,
     DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
     DEFAULT_URL_FOR_TEST,
@@ -675,5 +676,45 @@ class TestOpenAIServerEBNF(unittest.TestCase):
         ), "Function name should be add for the above response"
 
 
+class TestOpenAIEmbedding(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+
+        # Configure embedding-specific args
+        other_args = ["--is-embedding", "--enable-metrics"]
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            api_key=cls.api_key,
+            other_args=other_args,
+        )
+        cls.base_url += "/v1"
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_embedding_single(self):
+        """Test single embedding request"""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+        response = client.embeddings.create(model=self.model, input="Hello world")
+        self.assertEqual(len(response.data), 1)
+        self.assertTrue(len(response.data[0].embedding) > 0)
+
+    def test_embedding_batch(self):
+        """Test batch embedding request"""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+        response = client.embeddings.create(
+            model=self.model, input=["Hello world", "Test text"]
+        )
+        self.assertEqual(len(response.data), 2)
+        self.assertTrue(len(response.data[0].embedding) > 0)
+        self.assertTrue(len(response.data[1].embedding) > 0)
+
+
 if __name__ == "__main__":
     unittest.main()