Simplify routed experts test and move base64 encoding to tokenizer manager

python/sglang/srt/managers/detokenizer_manager.py

@@ -21,7 +21,6 @@
 from typing import Dict, List, Optional, Tuple, Union
 
 import psutil
-import pybase64
 import setproctitle
 import zmq
 
@@ -319,30 +318,13 @@ def _decode_batch_token_id_output(self, recv_obj: BatchTokenIDOutput):
 
         return output_strs
 
-    def _extract_routed_experts(
-        self, recv_obj: BatchTokenIDOutput
-    ) -> list[str | None] | None:
-        routed_experts = None
-        if recv_obj.routed_experts is not None:
-            routed_experts = [
-                (
-                    pybase64.b64encode(routed_experts.numpy().tobytes()).decode("utf-8")
-                    if routed_experts is not None
-                    else None
-                )
-                for routed_experts in recv_obj.routed_experts
-            ]
-        return routed_experts
-
     def handle_batch_token_id_out(self, recv_obj: BatchTokenIDOutput):
         # If handling idle batch, set output_strs to [].
         output_strs = (
             self._decode_batch_token_id_output(recv_obj)
             if len(recv_obj.rids) > 0
             else []
         )
-        routed_experts = self._extract_routed_experts(recv_obj)
-
         return BatchStrOutput(
             rids=recv_obj.rids,
             http_worker_ipcs=recv_obj.http_worker_ipcs,
@@ -370,7 +352,7 @@ def handle_batch_token_id_out(self, recv_obj: BatchTokenIDOutput):
             output_token_ids_logprobs_idx=recv_obj.output_token_ids_logprobs_idx,
             output_token_entropy_val=recv_obj.output_token_entropy_val,
             output_hidden_states=recv_obj.output_hidden_states,
-            routed_experts=routed_experts,
+            routed_experts=recv_obj.routed_experts,
             customized_info=recv_obj.customized_info,
             placeholder_tokens_idx=None,
             placeholder_tokens_val=None,

python/sglang/srt/managers/tokenizer_manager.py

@@ -32,6 +32,7 @@
 from typing import Any, Awaitable, Dict, List, Optional, Tuple, Union
 
 import fastapi
+import pybase64
 import uvloop
 import zmq
 import zmq.asyncio
@@ -1597,7 +1598,11 @@ def _handle_batch_output(
             if getattr(recv_obj, "output_hidden_states", None):
                 meta_info["hidden_states"] = recv_obj.output_hidden_states[i]
             if getattr(recv_obj, "routed_experts", None):
-                meta_info["routed_experts"] = recv_obj.routed_experts[i]
+                routed_experts_tensor = recv_obj.routed_experts[i]
+                if routed_experts_tensor is not None:
+                    meta_info["routed_experts"] = pybase64.b64encode(
+                        routed_experts_tensor.numpy().tobytes()
+                    ).decode("utf-8")
             if getattr(recv_obj, "customized_info", None):
                 for k, v in recv_obj.customized_info.items():
                     meta_info[k] = v[i]

python/sglang/srt/utils/numa_utils.py

@@ -155,7 +155,7 @@ def _is_numa_available() -> bool:
         return False
 
     if not shutil.which("numactl") and envs.SGLANG_NUMA_BIND_V2.get():
-        logger.warning(
+        logger.debug(
             "numactl command not found, skipping NUMA node configuration for GPU. Install numactl (e.g., apt-get install numactl) to enable automatic NUMA binding."
         )
         return False

python/sglang/utils.py

@@ -204,7 +204,16 @@ def encode_image_base64(image_path: Union[str, bytes]):
     elif isinstance(image_path, bytes):
         return pybase64.b64encode(image_path).decode("utf-8")
     else:
-        # image_path is PIL.WebPImagePlugin.WebPImageFile
+        import torch
+
+        if isinstance(image_path, torch.Tensor):
+            # Convert GPU-decoded image tensor (C, H, W) uint8 to PIL Image
+            from PIL import Image
+
+            tensor = image_path.cpu() if image_path.device.type != "cpu" else image_path
+            image_path = Image.fromarray(tensor.permute(1, 2, 0).numpy())
+
+        # image_path is a PIL Image
         image = image_path
         buffered = BytesIO()
         image.save(buffered, format="PNG")

test/registered/language/test_srt_backend.py

@@ -32,6 +32,7 @@ def setUpClass(cls):
             model_path=DEFAULT_MODEL_NAME_FOR_TEST,
             cuda_graph_max_bs=4,
             mem_fraction_static=0.7,
+            log_level="info",
         )
         sgl.set_default_backend(cls.backend)
 

test/registered/rl/test_return_routed_experts.py

@@ -1,13 +1,14 @@
 import asyncio
+import json
 import logging
 import unittest
 from typing import List
 
 import aiohttp
-import requests
 import torch
 from torch.nn.utils.rnn import pad_sequence
 
+from sglang.benchmark.utils import download_and_cache_hf_file
 from sglang.srt.layers.moe.routed_experts_capturer import (
     extract_routed_experts_from_meta_info,
 )
@@ -21,23 +22,18 @@
     popen_launch_server,
 )
 
-register_cuda_ci(est_time=360, suite="stage-c-test-4-gpu-h100")
+register_cuda_ci(est_time=200, suite="stage-b-test-2-gpu-large")
 register_amd_ci(
-    est_time=360,
-    suite="stage-c-test-4-gpu-amd",
-    disabled="TP=4 DP=4 routed expert mismatch >15% on AMD; needs TP/DP tuning + concurrency reduction",
+    est_time=200,
+    suite="stage-b-test-2-gpu-large-amd",
+    disabled="TP=2 DP=2 routed expert mismatch >15% on AMD; needs TP/DP tuning + concurrency reduction",
 )
 
-SHAREGPT_URL = (
-    "https://huggingface.co/datasets/anon8231489123/"
-    "ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json"
-)
+SHAREGPT_REPO_ID = "anon8231489123/ShareGPT_Vicuna_unfiltered"
+SHAREGPT_FILENAME = "ShareGPT_V3_unfiltered_cleaned_split.json"
 logger = logging.getLogger(__name__)
 
 
-@unittest.skip(
-    "Flaky in CI, need to be fixed and re-enabled. See https://github.com/sgl-project/sglang/issues/21266"
-)
 class TestReturnRoutedExperts(CustomTestCase):
     # modified from test_hicache.py
     @classmethod
@@ -50,31 +46,28 @@ def setUpClass(cls):
             "--disable-cuda-graph",
             "--disable-radix-cache",
             "--tp",
-            4,
+            2,
             "--dp",
-            4,
+            2,
             "--enable-dp-attention",
         ]
         cls.reference_args = [
             "--enable-return-routed-experts",
             "--enable-deterministic-inference",
             "--tp",
-            4,
+            2,
             "--dp",
-            4,
+            2,
             "--enable-dp-attention",
         ]
         cls.sampling_args = {
             "temperature": 0,
         }
         # prepare ShareGPT dataset
-        try:
-            response = requests.get(SHAREGPT_URL, timeout=60)
-            response.raise_for_status()
-            data = response.json()
-            print(f"Dataset size: {len(data)}")
-        except requests.exceptions.RequestException as e:
-            raise Exception(f"Failed to download ShareGPT dataset: {e}") from e
+        dataset_path = download_and_cache_hf_file(SHAREGPT_REPO_ID, SHAREGPT_FILENAME)
+        with open(dataset_path) as f:
+            data = json.load(f)
+        print(f"Dataset size: {len(data)}")
         cls.texts = []
         for s in data:
             if "conversations" in s and len(s["conversations"]) > 0: