Fix `input_ids` && rename to `fill_ids` (#1021)

python/sglang/bench_latency.py

@@ -152,7 +152,7 @@ def prepare_inputs_for_correctness_test(bench_args, tokenizer):
         req = Req(rid=i, origin_input_text=prompts[i], origin_input_ids=tmp_input_ids)
         req.prefix_indices = []
         req.sampling_params = sampling_params
-        req.input_ids = req.origin_input_ids
+        req.fill_ids = req.origin_input_ids
         reqs.append(req)
 
     return input_ids, reqs
@@ -163,7 +163,7 @@ def prepare_extend_inputs_for_correctness_test(
 ):
     for i in range(len(reqs)):
         req = reqs[i]
-        req.input_ids += input_ids[i][bench_args.cut_len :]
+        req.fill_ids += input_ids[i][bench_args.cut_len :]
         req.prefix_indices = model_runner.req_to_token_pool.req_to_token[
             i, : bench_args.cut_len
         ]
@@ -182,7 +182,7 @@ def prepare_synthetic_inputs_for_latency_test(batch_size, input_len):
         req = Req(rid=i, origin_input_text="", origin_input_ids=list(input_ids[i]))
         req.prefix_indices = []
         req.sampling_params = sampling_params
-        req.input_ids = req.origin_input_ids
+        req.fill_ids = req.origin_input_ids
         reqs.append(req)
 
     return reqs

python/sglang/srt/managers/policy_scheduler.py

@@ -138,7 +138,7 @@ class PrefillAdder:
     def add_inflight_req(self, req: Req):
         truncated = req.extend_input_len > self.rem_chunk_tokens
         req.extend_input_len = min(req.extend_input_len, self.rem_chunk_tokens)
-        req.input_ids = req.input_ids[: len(req.prefix_indices) + req.extend_input_len]
+        req.fill_ids = req.fill_ids[: len(req.prefix_indices) + req.extend_input_len]
         self.can_run_list.append(req)
 
         self._prefill_one_req(
@@ -193,7 +193,7 @@ class PrefillAdder:
                     return False
 
                 req.extend_input_len = trunc_len
-                req.input_ids = req.input_ids[: len(req.prefix_indices) + trunc_len]
+                req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]
                 self.can_run_list.append(req)
                 self.new_inflight_req = req
                 self.tree_cache.inc_lock_ref(req.last_node)

python/sglang/srt/managers/schedule_batch.py

@@ -99,7 +99,7 @@ class Req:
         self.origin_input_ids_unpadded = origin_input_ids  # Before image padding
         self.origin_input_ids = origin_input_ids
         self.output_ids = []  # Each decode stage's output ids
-        self.input_ids = None  # input_ids = origin_input_ids + output_ids
+        self.fill_ids = None  # fill_ids = origin_input_ids + output_ids
 
         # Memory info
         self.req_pool_idx = None
@@ -165,12 +165,12 @@ class Req:
         return self.finished_reason is not None
 
     def init_next_round_input(self):
-        self.input_ids = self.origin_input_ids + self.output_ids
-        self.extend_input_len = len(self.input_ids) - len(self.prefix_indices)
+        self.fill_ids = self.origin_input_ids + self.output_ids
+        self.extend_input_len = len(self.fill_ids) - len(self.prefix_indices)
 
     def adjust_max_prefix_ids(self):
-        self.input_ids = self.origin_input_ids + self.output_ids
-        input_len = len(self.input_ids)
+        self.fill_ids = self.origin_input_ids + self.output_ids
+        input_len = len(self.fill_ids)
         max_prefix_len = input_len
 
         if self.sampling_params.max_new_tokens > 0:
@@ -184,7 +184,7 @@ class Req:
                 # Need at least two tokens to compute normalized logprob
                 max_prefix_len = min(max_prefix_len, input_len - 2)
 
-        return self.input_ids[:max_prefix_len]
+        return self.fill_ids[:max_prefix_len]
 
     # Based on https://github.com/vllm-project/vllm/blob/7a64d24aad69e4d2548aa0bf528d9fe63428ab01/vllm/transformers_utils/detokenizer.py#L194-L313
     def init_incremental_detokenize(self):
@@ -427,7 +427,7 @@ class ScheduleBatch:
     def prepare_for_extend(self, vocab_size: int, int_token_logit_bias: torch.Tensor):
         bs = self.batch_size()
         reqs = self.reqs
-        input_ids = [r.input_ids[len(r.prefix_indices) :] for r in reqs]
+        input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
         extend_num_tokens = sum(len(ids) for ids in input_ids)
         seq_lens = []
 
@@ -438,7 +438,7 @@ class ScheduleBatch:
         pt = 0
         for i, req in enumerate(reqs):
             req.req_pool_idx = req_pool_indices_cpu[i]
-            pre_len, seq_len = len(req.prefix_indices), len(req.input_ids)
+            pre_len, seq_len = len(req.prefix_indices), len(req.fill_ids)
             ext_len = seq_len - pre_len
             seq_lens.append(seq_len)
 
@@ -632,7 +632,8 @@ class ScheduleBatch:
     def prepare_for_decode(self, input_ids=None):
         if input_ids is None:
             input_ids = [
-                r.output_ids[-1] if r.output_ids else r.input_ids[-1] for r in self.reqs
+                r.output_ids[-1] if r.output_ids else r.origin_input_ids[-1]
+                for r in self.reqs
             ]
         else:
             self.penalizer_orchestrator.cumulate_input_tokens(input_ids)

python/sglang/srt/managers/tp_worker.py

@@ -515,7 +515,7 @@ class ModelTpServer:
 
         self.handle_finished_requests(batch)
 
-    def add_logprob_return_values(self, i, req, pt, next_token_ids, output):
+    def add_logprob_return_values(self, i, req: Req, pt, next_token_ids, output):
         if req.normalized_prompt_logprob is None:
             req.normalized_prompt_logprob = output.normalized_prompt_logprobs[i]
 
@@ -524,12 +524,12 @@ class ModelTpServer:
             req.input_token_logprobs = list(
                 zip(
                     output.input_token_logprobs[pt : pt + req.extend_input_len - 1],
-                    req.input_ids[-req.extend_input_len + 1 :],
+                    req.fill_ids[-req.extend_input_len + 1 :],
                 )
             )
             if req.logprob_start_len == 0:
                 req.input_token_logprobs = [
-                    (None, req.input_ids[0])
+                    (None, req.fill_ids[0])
                 ] + req.input_token_logprobs
 
         if req.last_update_decode_tokens != 0:
@@ -543,7 +543,7 @@ class ModelTpServer:
                             + req.extend_input_len
                             - 1
                         ],
-                        req.input_ids[-req.last_update_decode_tokens + 1 :],
+                        req.fill_ids[-req.last_update_decode_tokens + 1 :],
                     )
                 )
             )

python/sglang/srt/mem_cache/chunk_cache.py

@@ -34,7 +34,7 @@ class ChunkCache(BasePrefixCache):
 
     def cache_finished_req(self, req: "Req", token_ids=None):
         if token_ids is None:
-            token_ids = (req.input_ids + req.output_ids)[:-1]
+            token_ids = (req.origin_input_ids + req.output_ids)[:-1]
 
         kv_indices = self.req_to_token_pool.req_to_token[
             req.req_pool_idx, : len(token_ids)
@@ -45,7 +45,7 @@ class ChunkCache(BasePrefixCache):
 
     def cache_unfinished_req(self, req: "Req", token_ids=None):
         if token_ids is None:
-            token_ids = req.input_ids
+            token_ids = req.fill_ids
 
         kv_indices = self.req_to_token_pool.req_to_token[
             req.req_pool_idx, : len(token_ids)

python/sglang/srt/mem_cache/radix_cache.py

@@ -92,7 +92,7 @@ class RadixCache(BasePrefixCache):
     def cache_finished_req(self, req: "Req", token_ids=None):
         """Cache request when it finishes."""
         if token_ids is None:
-            token_ids = (req.input_ids + req.output_ids)[:-1]
+            token_ids = (req.origin_input_ids + req.output_ids)[:-1]
         kv_indices = self.req_to_token_pool.req_to_token[
             req.req_pool_idx, : len(token_ids)
         ]
@@ -116,7 +116,7 @@ class RadixCache(BasePrefixCache):
             return
 
         if token_ids is None:
-            token_ids = req.input_ids
+            token_ids = req.fill_ids
 
         kv_indices = self.req_to_token_pool.req_to_token[
             req.req_pool_idx, : len(token_ids)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -109,7 +109,7 @@ class InputMetadata:
                 self.positions = torch.tensor(
                     np.concatenate(
                         [
-                            np.arange(len(req.prefix_indices), len(req.input_ids))
+                            np.arange(len(req.prefix_indices), len(req.fill_ids))
                             for req in batch.reqs
                         ],
                         axis=0,
@@ -124,7 +124,7 @@ class InputMetadata:
                         [
                             np.arange(
                                 len(req.prefix_indices) + position_ids_offsets_cpu[i],
-                                len(req.input_ids) + position_ids_offsets_cpu[i],
+                                len(req.fill_ids) + position_ids_offsets_cpu[i],
                             )
                             for i, req in enumerate(batch.reqs)
                         ],
@@ -141,7 +141,7 @@ class InputMetadata:
             self.extend_seq_lens = self.extend_start_loc = self.extend_no_prefix = None
         else:
             prefix_lens_cpu = [
-                len(r.input_ids) - len(r.prefix_indices) for r in batch.reqs
+                len(r.fill_ids) - len(r.prefix_indices) for r in batch.reqs
             ]
             self.extend_seq_lens = torch.tensor(prefix_lens_cpu, device="cuda")
             self.extend_start_loc = torch.zeros_like(self.seq_lens)
@@ -149,7 +149,7 @@ class InputMetadata:
             self.extend_no_prefix = all(x == 0 for x in prefix_lens_cpu)
 
     def init_total_num_tokens(self, batch: ScheduleBatch):
-        self.total_num_tokens = sum(len(req.input_ids) for req in batch.reqs)
+        self.total_num_tokens = sum(len(req.fill_ids) for req in batch.reqs)
 
     @classmethod
     def from_schedule_batch(
@@ -203,7 +203,7 @@ class InputMetadata:
 
     def init_triton_args(self, batch: ScheduleBatch, prefix_lens):
         """Init auxiliary variables for triton attention backend."""
-        self.triton_max_seq_len = max(len(r.input_ids) for r in batch.reqs)
+        self.triton_max_seq_len = max(len(r.fill_ids) for r in batch.reqs)
         self.triton_prefix_lens = prefix_lens
         self.triton_start_loc = torch.zeros_like(self.seq_lens, dtype=torch.int32)
         self.triton_start_loc[1:] = torch.cumsum(self.seq_lens[:-1], dim=0)