[Minor] Improve the code style in TokenizerManager (#767)

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

@@ -376,7 +376,7 @@ class Batch:
                     logit_bias = torch.zeros(
                         (bs, vocab_size), dtype=torch.float32, device=device
                     )
-                logit_bias[i] = int_token_logit_bias
+                logit_bias[i][: len(int_token_logit_bias)] = int_token_logit_bias
 
         # Set fields
         self.input_ids = torch.tensor(

python/sglang/srt/managers/tokenizer_manager.py

@@ -133,24 +133,10 @@ class TokenizerManager:
             async for response in self._handle_batch_request(obj, request):
                 yield response
 
-    async def _handle_single_request(self, obj, request, index=None, is_prefill=False):
+    async def _handle_single_request(
-        if is_prefill:
+        self, obj, request, index=None, is_cache_for_prefill=False
-            if isinstance(obj.text, list):
+    ):
-                input_text = obj.text[index]
+        if not is_cache_for_prefill:
-                rid = obj.rid[index]
-            else:
-                input_text = obj.text
-                rid = obj.rid[0]
-            input_ids = self.tokenizer.encode(input_text)
-            sampling_params = SamplingParams(**obj.sampling_params[0])
-            sampling_params.max_new_tokens = 0
-            pixel_values, image_hash, image_size = await self._get_pixel_values(
-                obj.image_data[0]
-            )
-            return_logprob = obj.return_logprob[0]
-            logprob_start_len = obj.logprob_start_len[0]
-            top_logprobs_num = obj.top_logprobs_num[0]
-        else:
             rid = obj.rid if index is None else obj.rid[index]
             input_text = obj.text if index is None else obj.text[index]
             input_ids = (
@@ -177,6 +163,22 @@ class TokenizerManager:
             top_logprobs_num = (
                 obj.top_logprobs_num if index is None else obj.top_logprobs_num[index]
             )
+        else:
+            if isinstance(obj.text, list):
+                input_text = obj.text[index]
+                rid = obj.rid[index]
+            else:
+                input_text = obj.text
+                rid = obj.rid[0]
+            input_ids = self.tokenizer.encode(input_text)
+            sampling_params = SamplingParams(**obj.sampling_params[0])
+            sampling_params.max_new_tokens = 0
+            pixel_values, image_hash, image_size = await self._get_pixel_values(
+                obj.image_data[0]
+            )
+            return_logprob = obj.return_logprob[0]
+            logprob_start_len = obj.logprob_start_len[0]
+            top_logprobs_num = obj.top_logprobs_num[0]
 
         tokenized_obj = TokenizedGenerateReqInput(
             rid,
@@ -196,26 +198,26 @@ class TokenizerManager:
         event = asyncio.Event()
         state = ReqState([], False, event)
         self.rid_to_state[rid] = state
-        if is_prefill:
+        if not is_cache_for_prefill:
-            await self._wait_for_prefill_response(event, state, obj, request, rid)
-            yield input_ids
-        else:
             async for response in self._wait_for_response(
                 event, state, obj, rid, request
             ):
                 yield response
+        else:
+            await self._wait_for_cache_prefill_response(event, state, obj, rid, request)
+            yield input_ids
 
-    async def _handle_batch_request(self, obj, request):
+    async def _handle_batch_request(self, obj: GenerateReqInput, request):
         batch_size = obj.batch_size
         parallel_sample_num = obj.sampling_params[0].get("n", 1)
 
         if parallel_sample_num != 1:
-            ## send prefill requests
+            # Send prefill requests to cache the common input
             parallel_sample_num += 1
             input_id_result = [] if obj.input_ids is None else None
             for i in range(batch_size):
                 async for input_id in self._handle_single_request(
-                    obj, request, index=i, is_prefill=True
+                    obj, request, index=i, is_cache_for_prefill=True
                 ):
                     if input_id_result is not None:
                         input_id_result.append(input_id)
@@ -224,6 +226,7 @@ class TokenizerManager:
                 obj.input_ids = input_id_result
             elif input_id_result is not None:
                 obj.input_ids = input_id_result[0]
+
         # First send out all requests
         for i in range(batch_size):
             for j in range(parallel_sample_num):
@@ -308,17 +311,15 @@ class TokenizerManager:
 
         yield output_list
 
-    def _validate_input_length(self, input_ids):
+    def _validate_input_length(self, input_ids: List[int]):
         if len(input_ids) >= self.context_len:
             raise ValueError(
                 f"The input ({len(input_ids)} tokens) is longer than the "
                 f"model's context length ({self.context_len} tokens)."
             )
 
-    def _get_sampling_params(self, sampling_params_data, max_new_tokens=None):
+    def _get_sampling_params(self, sampling_params_data: dict):
         sampling_params = SamplingParams(**sampling_params_data)
-        if max_new_tokens is not None:
-            sampling_params.max_new_tokens = max_new_tokens
         if sampling_params.max_new_tokens != 0:
             sampling_params.normalize(self.tokenizer)
             sampling_params.verify()
@@ -332,7 +333,14 @@ class TokenizerManager:
         else:
             return None, None, None
 
-    async def _wait_for_response(self, event, state, obj, rid, request):
+    async def _wait_for_response(
+        self,
+        event: asyncio.Event,
+        state: ReqState,
+        obj: GenerateReqInput,
+        rid: str,
+        request,
+    ):
         while True:
             try:
                 await asyncio.wait_for(event.wait(), timeout=4)
@@ -361,7 +369,14 @@ class TokenizerManager:
             event.clear()
             yield out
 
-    async def _wait_for_prefill_response(self, event, state, obj, request, rid):
+    async def _wait_for_cache_prefill_response(
+        self,
+        event: asyncio.Event,
+        state: ReqState,
+        obj: GenerateReqInput,
+        rid: str,
+        request,
+    ):
         while True:
             try:
                 await asyncio.wait_for(state.event.wait(), timeout=4)
@@ -380,7 +395,7 @@ class TokenizerManager:
         req = FlushCacheReq()
         self.send_to_router.send_pyobj(req)
 
-    def abort_request(self, rid):
+    def abort_request(self, rid: str):
         if rid not in self.rid_to_state:
             return
         del self.rid_to_state[rid]
@@ -426,7 +441,11 @@ class TokenizerManager:
                 state.event.set()
 
     def convert_logprob_style(
-        self, ret, return_logprob, top_logprobs_num, return_text_in_logprobs
+        self,
+        ret: dict,
+        return_logprob: bool,
+        top_logprobs_num: int,
+        return_text_in_logprobs: bool,
     ):
         if return_logprob:
             ret["meta_info"]["prefill_token_logprobs"] = self.detokenize_logprob_tokens(
@@ -450,7 +469,7 @@ class TokenizerManager:
                 )
         return ret
 
-    def detokenize_logprob_tokens(self, token_logprobs, decode_to_text):
+    def detokenize_logprob_tokens(self, token_logprobs, decode_to_text: bool):
         if not decode_to_text:
             return [(logprob, token_id, None) for logprob, token_id in token_logprobs]
 
@@ -461,7 +480,7 @@ class TokenizerManager:
             for (logprob, token_id), token_text, in zip(token_logprobs, token_texts)
         ]
 
-    def detokenize_top_logprobs_tokens(self, top_logprobs, decode_to_text):
+    def detokenize_top_logprobs_tokens(self, top_logprobs, decode_to_text: bool):
         for i, t in enumerate(top_logprobs):
             if t:
                 top_logprobs[i] = self.detokenize_logprob_tokens(t, decode_to_text)

python/sglang/test/test_programs.py

@@ -118,7 +118,11 @@ def test_decode_json_regex():
             s += "}"
 
     ret = decode_json.run()
-    js_obj = json.loads(ret["json_output"])
+    try:
+        js_obj = json.loads(ret["json_output"])
+    except json.decoder.JSONDecodeError:
+        print(ret["json_output"])
+        raise
     assert isinstance(js_obj["name"], str)
     assert isinstance(js_obj["population"], int)