Improve docs and warnings (#1164)

python/sglang/srt/hf_transformers_utils.py

@@ -147,13 +147,12 @@ def get_tokenizer(
         and kwargs.get("use_fast", True)
         and tokenizer_name != _FAST_LLAMA_TOKENIZER
     ):
-        pass
-        # warnings.warn(
-        #    "For some LLaMA V1 models, initializing the fast tokenizer may "
-        #    "take a long time. To reduce the initialization time, consider "
-        #    f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
-        #    "tokenizer."
-        # )
+        warnings.warn(
+            "For some LLaMA V1 models, initializing the fast tokenizer may "
+            "take a long time. To reduce the initialization time, consider "
+            f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
+            "tokenizer."
+        )
     try:
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name,

python/sglang/srt/managers/schedule_batch.py

@@ -270,7 +270,7 @@ class Req:
 
         if all_ids[prompt_tokens - 1] != self.origin_input_ids_unpadded[-1]:
             # TODO(lsyin): fix token fusion
-            warnings.warn(
+            logging.warning(
                 "Token fusion between input and output, try to avoid this by removing the space at the end of the input."
             )
             return False
@@ -791,7 +791,7 @@ class ScheduleBatch:
             )
 
         if not torch.all(success):
-            warnings.warn("Sampling failed, fallback to top_k=1 strategy")
+            logging.warning("Sampling failed, fallback to top_k=1 strategy")
             probs = probs.masked_fill(torch.isnan(probs), 0.0)
             argmax_ids = torch.argmax(probs, dim=-1)
             batch_next_token_ids = torch.where(

python/sglang/srt/managers/tp_worker.py

@@ -774,7 +774,7 @@ class ModelTpServer:
             torch.cuda.empty_cache()
             logger.info("Cache flushed successfully!")
         else:
-            warnings.warn(
+            logging.warning(
                 f"Cache not flushed because there are pending requests. "
                 f"#queue-req: {len(self.waiting_queue)}, "
                 f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"

python/sglang/srt/model_executor/model_runner.py

@@ -237,7 +237,7 @@ class ModelRunner:
         self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
         if max_total_tokens is not None:
             if max_total_tokens > self.max_total_num_tokens:
-                warnings.warn(
+                logging.warning(
                     f"max_total_tokens={max_total_tokens} is larger than the profiled value "
                     f"{self.max_total_num_tokens}. "
                     f"Use the profiled value instead."

python/sglang/srt/openai_api/adapter.py

@@ -17,10 +17,10 @@ limitations under the License.
 
 import asyncio
 import json
+import logging
 import os
 import time
 import uuid
-import warnings
 from http import HTTPStatus
 from typing import Dict, List, Optional
 
@@ -65,6 +65,8 @@ from sglang.srt.openai_api.protocol import (
     UsageInfo,
 )
 
+logger = logging.getLogger(__name__)
+
 chat_template_name = None
 
 
@@ -408,7 +410,7 @@ def v1_generate_request(all_requests: List[CompletionRequest]):
                 "Parallel sampling is not supported for completions from files"
             )
         if request.echo and request.logprobs:
-            warnings.warn(
+            logger.warning(
                 "Echo is not compatible with logprobs. "
                 "To compute logprobs of input prompt, please use SGLang /request API."
             )