[Fix] the issue of random order when input is a list (#1199)

python/sglang/srt/managers/tokenizer_manager.py

@@ -437,13 +437,13 @@ class TokenizerManager:
         is_stream = hasattr(obj, "stream") and obj.stream
 
         tasks = [asyncio.create_task(gen.__anext__()) for gen in generators]
-        output_list = []
+        output_list = [None] * len(tasks)
 
         while tasks:
             done, _ = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
 
             for task in done:
-                gen_index = tasks.index(task)
+                cur_index = tasks.index(task)
 
                 try:
                     result = task.result()
@@ -451,14 +451,14 @@ class TokenizerManager:
                     if is_stream:
                         yield result
                     else:
-                        output_list.append(result)
+                        output_list[result["index"]] = result
 
-                    tasks[gen_index] = asyncio.create_task(
-                        generators[gen_index].__anext__()
+                    tasks[cur_index] = asyncio.create_task(
+                        generators[cur_index].__anext__()
                     )
                 except StopAsyncIteration:
-                    del generators[gen_index]
-                    del tasks[gen_index]
+                    del generators[cur_index]
+                    del tasks[cur_index]
 
         if not is_stream:
             yield output_list

python/sglang/srt/server.py

@@ -591,7 +591,7 @@ class Runtime:
 
     def generate(
         self,
-        prompt: str,
+        prompt: Union[str, List[str]],
         sampling_params: Optional[Dict] = None,
         return_logprob: Optional[Union[List[bool], bool]] = False,
         logprob_start_len: Optional[Union[List[int], int]] = None,
@@ -612,7 +612,7 @@ class Runtime:
 
     def encode(
         self,
-        prompt: str,
+        prompt: Union[str, List[str]],
     ):
         json_data = {
             "text": prompt,

python/sglang/test/runners.py

@@ -28,10 +28,10 @@ from sglang.srt.server import Runtime
 DEFAULT_PROMPTS = [
     # the output of gemma-2-2b from SRT is unstable on the commented prompt
     # "The capital of France is",
+    "Apple is red. Banana is Yellow. " * 800 + "Apple is",
     "The capital of the United Kindom is",
     "Today is a sunny day and I like",
     "AI is a field of computer science focused on",
-    "Apple is red. Banana is Yellow. " * 800 + "Apple is",
 ]
 
 dirpath = os.path.dirname(__file__)

test/srt/models/test_embedding_models.py

@@ -20,7 +20,7 @@ import torch
 from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
 from sglang.test.test_utils import get_similarities
 
-MODELS = [("intfloat/e5-mistral-7b-instruct", 1)]
+MODELS = [("intfloat/e5-mistral-7b-instruct", 1, 0.2)]
 TORCH_DTYPES = [torch.float16]
 
 
@@ -32,6 +32,7 @@ class TestEmbeddingModels(unittest.TestCase):
         model_path,
         tp_size,
         torch_dtype,
+        long_context_tolerance,
     ) -> None:
         with HFRunner(
             model_path, torch_dtype=torch_dtype, is_generation_model=False
@@ -52,20 +53,22 @@ class TestEmbeddingModels(unittest.TestCase):
             hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
             srt_logits = torch.Tensor(srt_outputs.embed_logits[i])
 
-            similarities = torch.tensor(get_similarities(hf_logits, srt_logits))
-            print("max similarity diff", torch.max(abs(similarities - 1)))
+            similarity = torch.tensor(get_similarities(hf_logits, srt_logits))
+            print("similarity diff", abs(similarity - 1))
 
-            if hf_logits.shape[0] <= 100:
-                tolerance = 1e-2
-                assert torch.all(
-                    abs(similarities - 1) < tolerance
-                ), "embeddings are not all close"
+            if len(prompts[i]) <= 1000:
+                tolerance = 1e-5
+            else:
+                tolerance = long_context_tolerance
+            assert torch.all(
+                abs(similarity - 1) < tolerance
+            ), "embeddings are not all close"
 
     def test_prefill_logits(self):
-        for model, tp_size in MODELS:
+        for model, tp_size, long_context_tolerance in MODELS:
             for torch_dtype in TORCH_DTYPES:
                 self.assert_close_prefill_logits(
-                    DEFAULT_PROMPTS, model, tp_size, torch_dtype
+                    DEFAULT_PROMPTS, model, tp_size, torch_dtype, long_context_tolerance
                 )