Fix max_tokens for OpenAI chat completion API (#766)

python/sglang/srt/managers/controller/tp_worker.py

@@ -98,17 +98,21 @@ class ModelTpServer:
             if server_args.max_prefill_tokens is None
             else server_args.max_prefill_tokens
         )
-        self.max_running_requests = (
-            self.max_total_num_tokens // 2
-            if server_args.max_running_requests is None
-            else server_args.max_running_requests
-        )
         self.max_running_requests = min(
-            self.max_running_requests, self.model_runner.req_to_token_pool.size - 1
+            (
+                self.max_total_num_tokens // 2
+                if server_args.max_running_requests is None
+                else server_args.max_running_requests
+            ),
+            self.model_runner.req_to_token_pool.size - 1,
         )
         self.int_token_logit_bias = torch.tensor(
             get_int_token_logit_bias(self.tokenizer, self.model_config.vocab_size)
         )
+        self.max_req_input_len = min(
+            self.model_config.context_len - 1,
+            self.max_total_num_tokens - 1,
+        )
         set_random_seed(server_args.random_seed)
 
         # Print info
@@ -295,18 +299,16 @@ class ModelTpServer:
                 )
 
         # Truncate prompts that are too long
-        req.origin_input_ids = req.origin_input_ids[: self.model_config.context_len - 1]
+        if len(req.origin_input_ids) >= self.max_req_input_len:
+            logger.warn(
+                "Request length is longer than the KV cache pool size or "
+                "the max context length. Truncated!!!"
+            )
+            req.origin_input_ids = req.origin_input_ids[: self.max_req_input_len]
         req.sampling_params.max_new_tokens = min(
-            req.sampling_params.max_new_tokens,
-            self.model_config.context_len - 1 - len(req.origin_input_ids),
-            self.max_total_num_tokens - 128 - len(req.origin_input_ids),
+            req.sampling_params.max_new_tokens or 1 << 30,
+            self.max_req_input_len - 1 - len(req.origin_input_ids),
         )
-        if req.sampling_params.max_new_tokens < 0:
-            req.origin_input_ids = req.origin_input_ids[
-                : self.max_total_num_tokens - 128
-            ]
-            logger.error("Request longer than memory pool size, truncated!!!")
-
         self.forward_queue.append(req)
 
     def get_new_prefill_batch(self) -> Optional[Batch]:

python/sglang/srt/openai_api/protocol.py

@@ -152,7 +152,7 @@ class ChatCompletionRequest(BaseModel):
     logit_bias: Optional[Dict[str, float]] = None
     logprobs: Optional[bool] = False
     top_logprobs: Optional[int] = None
-    max_tokens: Optional[int] = 16
+    max_tokens: Optional[int] = None
     n: Optional[int] = 1
     presence_penalty: Optional[float] = 0.0
     response_format: Optional[ResponseFormat] = None

python/sglang/srt/sampling_params.py

@@ -65,10 +65,11 @@ class SamplingParams:
             raise ValueError(
                 "presence_penalty must be in [-2, 2], got " f"{self.presence_penalty}."
             )
-        if self.max_new_tokens < 0:
-            raise ValueError(
-                f"max_new_tokens must be at least 0, got {self.max_new_tokens}."
-            )
+        if self.max_new_tokens is not None:
+            if self.max_new_tokens < 0:
+                raise ValueError(
+                    f"max_new_tokens must be at least 0, got {self.max_new_tokens}."
+                )
 
     def normalize(self, tokenizer):
         # Process stop strings