Adjust default mem fraction to avoid OOM (#823)

python/sglang/srt/layers/radix_attention.py

@@ -103,7 +103,7 @@ class RadixAttention(nn.Module):
         return o
 
     def extend_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
-        if not input_metadata.use_ragged:
+        if not input_metadata.flashinfer_use_ragged:
             self.store_kv_cache(k, v, input_metadata)
 
             o = input_metadata.flashinfer_prefill_wrapper_paged.forward(

python/sglang/srt/managers/schedule_batch.py

@@ -781,7 +781,7 @@ class InputMetadata:
     flashinfer_prefill_wrapper_ragged: "BatchPrefillWithRaggedKVCacheWrapper" = None
     flashinfer_prefill_wrapper_paged: "BatchPrefillWithPagedKVCacheWrapper" = None
     flashinfer_decode_wrapper: "BatchDecodeWithPagedKVCacheWrapper" = None
-    use_ragged: bool = False
+    flashinfer_use_ragged: bool = False
 
     @classmethod
     def create(
@@ -797,10 +797,10 @@ class InputMetadata:
         return_logprob=False,
         skip_flashinfer_init=False,
     ):
-        use_ragged = False
+        flashinfer_use_ragged = False
         if not skip_flashinfer_init and not model_runner.server_args.disable_flashinfer:
             if forward_mode != ForwardMode.DECODE and int(torch.sum(seq_lens)) > 4096:
-                use_ragged = True
+                flashinfer_use_ragged = True
             init_flashinfer_args(
                 forward_mode,
                 model_runner,
@@ -808,7 +808,7 @@ class InputMetadata:
                 seq_lens,
                 prefix_lens,
                 model_runner.flashinfer_decode_wrapper,
-                use_ragged,
+                flashinfer_use_ragged,
             )
 
         batch_size = len(req_pool_indices)
@@ -863,7 +863,7 @@ class InputMetadata:
             flashinfer_prefill_wrapper_ragged=model_runner.flashinfer_prefill_wrapper_ragged,
             flashinfer_prefill_wrapper_paged=model_runner.flashinfer_prefill_wrapper_paged,
             flashinfer_decode_wrapper=model_runner.flashinfer_decode_wrapper,
-            use_ragged=use_ragged,
+            flashinfer_use_ragged=flashinfer_use_ragged,
         )
 
         if model_runner.server_args.disable_flashinfer:
@@ -884,7 +884,7 @@ def init_flashinfer_args(
     seq_lens,
     prefix_lens,
     flashinfer_decode_wrapper,
-    use_ragged=False,
+    flashinfer_use_ragged=False,
 ):
     """Init auxiliary variables for FlashInfer attention backend."""
     num_qo_heads = model_runner.model_config.num_attention_heads // model_runner.tp_size
@@ -893,7 +893,7 @@ def init_flashinfer_args(
     batch_size = len(req_pool_indices)
     total_num_tokens = int(torch.sum(seq_lens))
 
-    if use_ragged:
+    if flashinfer_use_ragged:
         paged_kernel_lens = prefix_lens
     else:
         paged_kernel_lens = seq_lens
@@ -929,7 +929,7 @@ def init_flashinfer_args(
         qo_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
         qo_indptr[1:] = torch.cumsum(seq_lens - prefix_lens, dim=0)
 
-        if use_ragged:
+        if flashinfer_use_ragged:
             model_runner.flashinfer_prefill_wrapper_ragged.end_forward()
             model_runner.flashinfer_prefill_wrapper_ragged.begin_forward(
                 qo_indptr,

python/sglang/srt/model_executor/model_runner.py

@@ -212,9 +212,14 @@ class ModelRunner:
             )
 
         if max_num_reqs is None:
-            max_num_reqs = max(
-                int(self.max_total_num_tokens / self.model_config.context_len * 512),
-                2048,
+            max_num_reqs = min(
+                max(
+                    int(
+                        self.max_total_num_tokens / self.model_config.context_len * 512
+                    ),
+                    2048,
+                ),
+                5120,
             )
 
         self.req_to_token_pool = ReqToTokenPool(

python/sglang/srt/server_args.py

@@ -91,15 +91,15 @@ class ServerArgs:
             self.tokenizer_path = self.model_path
         if self.mem_fraction_static is None:
             if self.tp_size >= 16:
-                self.mem_fraction_static = 0.80
+                self.mem_fraction_static = 0.79
             elif self.tp_size >= 8:
-                self.mem_fraction_static = 0.84
+                self.mem_fraction_static = 0.83
             elif self.tp_size >= 4:
-                self.mem_fraction_static = 0.86
+                self.mem_fraction_static = 0.85
             elif self.tp_size >= 2:
-                self.mem_fraction_static = 0.88
+                self.mem_fraction_static = 0.87
             else:
-                self.mem_fraction_static = 0.89
+                self.mem_fraction_static = 0.88
         if isinstance(self.additional_ports, int):
             self.additional_ports = [self.additional_ports]
         elif self.additional_ports is None: