Fix memory pool index error (#616)

python/sglang/srt/layers/radix_attention.py

@@ -137,9 +137,6 @@ class RadixAttention(nn.Module):
 
     def store_kv_cache(self, cache_k, cache_v, input_metadata: InputMetadata):
         key_buffer = input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id)
-        value_buffer = input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id)
-        if input_metadata.out_cache_loc is not None:
         key_buffer[input_metadata.out_cache_loc] = cache_k
+        value_buffer = input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id)
         value_buffer[input_metadata.out_cache_loc] = cache_v
-        else:
-            raise RuntimeError()

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

@@ -132,7 +132,8 @@ class CudaGraphRunner:
         index = bisect.bisect_left(self.batch_size_list, raw_bs)
         bs = self.batch_size_list[index]
         if bs != raw_bs:
-            self.seq_lens.fill_(1)
+            self.seq_lens.zero_()
+            self.position_ids_offsets.fill_(1)
             self.out_cache_loc.zero_()
 
         # Common inputs

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

@@ -315,7 +315,7 @@ class ModelTpServer:
 
     def get_new_fill_batch(self) -> Optional[Batch]:
         running_bs = len(self.running_batch.reqs) if self.running_batch is not None else 0
-        if running_bs > self.max_running_requests:
+        if running_bs >= self.max_running_requests:
             return
 
         # Compute matched prefix length
@@ -393,7 +393,7 @@ class ModelTpServer:
             else:
                 break
 
-            if running_bs + len(can_run_list) > self.max_running_requests:
+            if running_bs + len(can_run_list) >= self.max_running_requests:
                 break
 
         if len(can_run_list) == 0:

python/sglang/srt/memory_pool.py

@@ -46,7 +46,7 @@ class TokenToKVPool:
 
         # [size, key/value, head_num, head_dim] for each layer
         self.kv_data = [
-            torch.empty((size, 2, head_num, head_dim), dtype=dtype, device="cuda")
+            torch.empty((size + 1, 2, head_num, head_dim), dtype=dtype, device="cuda")
             for _ in range(layer_num)
         ]