Fix a bug in BatchTokenIDOut & Misc style and dependency updates (#7457)

python/pyproject.toml

@@ -29,6 +29,7 @@ runtime_common = [
     "msgspec",
     "ninja",
     "orjson",
+    "outlines==0.1.11",
     "packaging",
     "partial_json_parser",
     "pillow",
@@ -50,13 +51,12 @@ runtime_common = [
 srt = [
     "sglang[runtime_common]",
     "sgl-kernel==0.1.9",
-    "flashinfer_python==0.2.6.post1",
     "torch==2.7.1",
     "torchaudio==2.7.1",
     "torchvision==0.22.1",
     "cuda-python",
-    "outlines>=0.0.44,<=0.1.11",
     "einops",
+    "flashinfer_python==0.2.6.post1",
 ]
 
 blackwell = [
@@ -66,7 +66,6 @@ blackwell = [
     "torchaudio==2.7.1",
     "torchvision==0.22.1",
     "cuda-python",
-    "outlines>=0.0.44,<=0.1.11",
     "einops",
     "flashinfer_python==0.2.6.post1",
 ]
@@ -77,23 +76,22 @@ srt_hip = [
     "sglang[runtime_common]",
     "torch",
     "vllm==0.6.7.dev2",
-    "outlines==0.1.11"
 ]
 
 # xpu is not enabled in public vllm and torch whl,
 # need to follow https://docs.vllm.ai/en/latest/getting_started/xpu-installation.htmlinstall vllm
-srt_xpu = ["sglang[runtime_common]", "outlines>=0.0.44,<=0.1.11"]
+srt_xpu = ["sglang[runtime_common]"]
 
 # For Intel Gaudi(device : hpu) follow the installation guide
 # https://docs.vllm.ai/en/latest/getting_started/gaudi-installation.html
-srt_hpu = ["sglang[runtime_common]", "outlines>=0.0.44,<=0.1.11"]
+srt_hpu = ["sglang[runtime_common]"]
 
 # CPU: currently, there are no pre-built vllm wheels for CPU.
 # To install vllm for CPU, please follow the instruction here:
 # https://docs.vllm.ai/en/latest/getting_started/installation/cpu/index.html
-srt_cpu = ["sglang[runtime_common]", "outlines>=0.0.44,<=0.1.11", "einops"]
+srt_cpu = ["sglang[runtime_common]", "einops"]
 # https://vllm-ascend.readthedocs.io/en/latest/installation.html
-srt_npu = ["sglang[runtime_common]", "outlines>=0.0.44,<=0.1.11"]
+srt_npu = ["sglang[runtime_common]"]
 
 openai = ["openai>=1.0", "tiktoken"]
 anthropic = ["anthropic>=0.20.0"]

python/sglang/srt/managers/schedule_batch.py

@@ -788,6 +788,7 @@ class Req:
         self.multimodal_inputs = None
         self.grammar = None
         self.origin_input_ids = [0]  # set it to one token to skip the long prefill
+        self.return_logprob = False
         self.finished_reason = FINISH_ABORT(
             error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
         )

python/sglang/srt/managers/scheduler.py

@@ -1374,7 +1374,14 @@ class Scheduler(
             )
             raise ValueError(msg)
 
-        if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
+        if self.disaggregation_mode == DisaggregationMode.DECODE:
+            req_total_size = (
+                self.req_to_token_pool.size + self.req_to_token_pool.pre_alloc_size
+            )
+        else:
+            req_total_size = self.req_to_token_pool.size
+
+        if len(self.req_to_token_pool.free_slots) != req_total_size:
             msg = (
                 "req_to_token_pool memory leak detected!"
                 f"available_size={len(self.req_to_token_pool.free_slots)}, "

python/sglang/srt/managers/tokenizer_manager.py

@@ -1226,7 +1226,7 @@ class TokenizerManager:
                     state.last_output_offset = len(state.output_ids)
                 else:
                     state.output_ids.extend(recv_obj.output_ids[i])
-                    output_token_ids = state.output_ids
+                    output_token_ids = state.output_ids.copy()
 
                 out_dict = {
                     "output_ids": output_token_ids,

python/sglang/srt/server_args.py

@@ -1723,9 +1723,8 @@ class PortArgs:
             dist_init_host, dist_init_port = dist_init_addr
             port_base = int(dist_init_port) + 1
             if dp_rank is None:
-                scheduler_input_port = (
-                    port_base + 3
-                )  # TokenizerManager to DataParallelController
+                # TokenizerManager to DataParallelController
+                scheduler_input_port = port_base + 3
             else:
                 scheduler_input_port = port_base + 3 + 1 + dp_rank
 

python/sglang/srt/utils.py

@@ -1917,13 +1917,6 @@ def configure_ipv6(dist_init_addr):
     return port, host
 
 
-def rank0_log(msg: str):
-    from sglang.srt.distributed import get_tensor_model_parallel_rank
-
-    if get_tensor_model_parallel_rank() == 0:
-        logger.info(msg)
-
-
 def rank0_print(msg: str):
     from sglang.srt.distributed import get_tensor_model_parallel_rank
 
@@ -1931,6 +1924,9 @@ def rank0_print(msg: str):
         print(msg, flush=True)
 
 
+rank0_log = rank0_print
+
+
 def get_cuda_version():
     if torch.version.cuda:
         return tuple(map(int, torch.version.cuda.split(".")))