[style] Extract init-static values in forward path (#30708)

python/sglang/srt/layers/attention/hybrid_attn_backend.py

@@ -24,6 +24,12 @@ class HybridAttnBackend(AttentionBackend):
         self.data_type = model_runner.kv_cache_dtype
         self.token_to_kv_pool = model_runner.token_to_kv_pool
         self.req_to_token_pool = model_runner.req_to_token_pool
+        self.spec_attn_is_decode = (
+            model_runner.server_args.speculative_attention_mode == "decode"
+        )
+        self.spec_attn_is_prefill = (
+            model_runner.server_args.speculative_attention_mode == "prefill"
+        )
 
     def _select_backend(self, forward_mode: ForwardMode) -> AttentionBackend:
         """
@@ -45,7 +51,7 @@ class HybridAttnBackend(AttentionBackend):
         elif forward_mode.is_target_verify():
             return (
                 self.decode_backend
-                if self.model_runner.server_args.speculative_attention_mode == "decode"
+                if self.spec_attn_is_decode
                 else self.prefill_backend
             )
         else:
@@ -71,7 +77,7 @@ class HybridAttnBackend(AttentionBackend):
         self.decode_backend.init_cuda_graph_state(max_bs, max_num_tokens)
         if (
             self.model_runner.server_args.speculative_algorithm is not None
-            and self.model_runner.server_args.speculative_attention_mode == "prefill"
+            and self.spec_attn_is_prefill
         ):
             # When speculative decoding is enabled, we need to initialize the backend
             # that will be used for target_verify.
@@ -144,7 +150,7 @@ class HybridAttnBackend(AttentionBackend):
         return backend.get_indexer_metadata(layer_id, forward_batch)
 
     def update_mamba_state_after_mtp_verify(self, *args, **kwargs):
-        if self.model_runner.server_args.speculative_attention_mode == "decode":
+        if self.spec_attn_is_decode:
             backend = self.decode_backend
         else:
             backend = self.prefill_backend

python/sglang/srt/layers/logits_processor.py

@@ -360,6 +360,7 @@ class LogitsProcessor(nn.Module):
 
         self.return_full_logits = return_full_logits
         self.enable_mis = get_server_args().enable_mis
+        self.rl_on_policy_target = get_server_args().rl_on_policy_target
 
         self._logits_gatherer = triton_symm_mem_ag.MultimemAllGatherer(
             max_tokens=triton_symm_mem_ag.recommended_max_tokens(
@@ -969,7 +970,7 @@ class LogitsProcessor(nn.Module):
                     None,  # bias
                     True,  # is_vnni
                 )
-            elif get_server_args().rl_on_policy_target is not None:
+            elif self.rl_on_policy_target is not None:
                 # Due to tie-weight, we may not be able to change lm_head's weight dtype
                 logits = torch.matmul(
                     hidden_states.bfloat16(), lm_head.weight.T.bfloat16()

python/sglang/srt/model_executor/cpu_graph_runner.py

@@ -553,6 +553,9 @@ class CPUGraphRunner:
         # Parse args
         self.model_runner = model_runner
         self.device = model_runner.device
+        self.enable_return_hidden_states = (
+            model_runner.server_args.enable_return_hidden_states
+        )
         # bs -> compiled fn (text-only / skip_cross_attention=True)
         self.graphs = {}
         # bs -> compiled fn (cross-attention / skip_cross_attention=False, enc-dec only)
@@ -581,7 +584,7 @@ class CPUGraphRunner:
         self.num_tokens_per_bs = 1
 
         # If returning hidden states is enabled, set initial capture hidden mode to full to avoid double-capture on startup
-        if model_runner.server_args.enable_return_hidden_states:
+        if self.enable_return_hidden_states:
             self.capture_hidden_mode = CaptureHiddenMode.FULL
 
         assert (
@@ -870,7 +873,7 @@ class CPUGraphRunner:
         )
         capture_hidden_mode_required_for_returning_hidden_states = (
             CaptureHiddenMode.FULL
-            if self.model_runner.server_args.enable_return_hidden_states
+            if self.enable_return_hidden_states
             else CaptureHiddenMode.NULL
         )
 

python/sglang/srt/model_executor/forward_batch_info.py

@@ -1078,14 +1078,12 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
         # batch_size * [3 * seq_len]
         batch_size = self.seq_lens_cpu.shape[0]
         mrope_positions_list = [[]] * batch_size
+        rl_on_policy_target = get_server_args().rl_on_policy_target
         for batch_idx in range(batch_size):
             mm_input = batch.multimodal_inputs[batch_idx]
             if self.forward_mode.is_decode():
                 # 3 * N
-                if (
-                    mm_input is None
-                    or get_server_args().rl_on_policy_target is not None
-                ):
+                if mm_input is None or rl_on_policy_target is not None:
                     mrope_positions_list[batch_idx] = torch.full(
                         (3, 1),
                         self.seq_lens_cpu[batch_idx] - 1,
@@ -1101,10 +1099,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
                     batch.extend_lens[batch_idx],
                     batch.prefix_lens[batch_idx],
                 )
-                if (
-                    mm_input is None
-                    or get_server_args().rl_on_policy_target is not None
-                ):
+                if mm_input is None or rl_on_policy_target is not None:
                     # text only
                     mrope_positions = torch.tensor(
                         [

python/sglang/srt/model_executor/model_runner.py

@@ -3050,7 +3050,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             self.msprobe_debugger.stop()
             self.msprobe_debugger.step()
 
-        if self.server_args.elastic_ep_backend is not None:
+        if self.enable_elastic_ep:
             self.maybe_recover_ep_ranks()
 
         return output

python/sglang/srt/model_executor/runner/base_runner.py

@@ -199,6 +199,10 @@ class BaseRunner(ABC):
         self.tp_size = model_runner.server_args.tp_size
         self.dp_size = model_runner.server_args.dp_size
         self.pp_size = model_runner.server_args.pp_size
+        self.enable_pdmux = model_runner.server_args.enable_pdmux
+        self.enable_return_hidden_states = (
+            model_runner.server_args.enable_return_hidden_states
+        )
         self.attn_tp_size = get_parallel().attn_tp_size
         self.attn_tp_rank = get_parallel().attn_tp_rank
         self.tbo_plugin = TboCudaGraphRunnerPlugin()

python/sglang/srt/model_executor/runner/decode_cuda_graph_runner.py

@@ -204,7 +204,6 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
         self.enable_profile_cuda_graph = (
             model_runner.server_args.enable_profile_cuda_graph
         )
-        self.enable_pdmux = model_runner.server_args.enable_pdmux
 
         self.attn_tp_size = get_parallel().attn_tp_size
         self.attn_tp_rank = get_parallel().attn_tp_rank
@@ -259,7 +258,7 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
             KTMoEWrapper.set_capture_batch_sizes(self.capture_bs)
 
         # If returning hidden states is enabled, set initial capture hidden mode to full to avoid double-capture on startup
-        if model_runner.server_args.enable_return_hidden_states:
+        if self.enable_return_hidden_states:
             self.capture_hidden_mode = CaptureHiddenMode.FULL
 
         # Attention backend
@@ -882,7 +881,7 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
         )
         capture_hidden_mode_required_for_returning_hidden_states = (
             CaptureHiddenMode.FULL
-            if self.model_runner.server_args.enable_return_hidden_states
+            if self.enable_return_hidden_states
             else CaptureHiddenMode.NULL
         )
 

python/sglang/srt/model_executor/runner/eager_runner.py

@@ -216,7 +216,7 @@ class EagerRunner(BaseRunner):
         runs under. PDmux selects a per-stream backend and publishes it via an
         active ForwardContext; non-pdmux uses attn_backend + the ambient ctx."""
         model_runner = self.model_runner
-        if model_runner.server_args.enable_pdmux:
+        if self.enable_pdmux:
             return model_runner.decode_attn_backend, forward_context(
                 ForwardContext(attn_backend=model_runner.decode_attn_backend)
             )
@@ -228,7 +228,7 @@ class EagerRunner(BaseRunner):
         pp_proxy_tensors=None,
     ) -> Union[LogitsProcessorOutput, PPProxyTensors]:
         model_runner = self.model_runner
-        enable_pdmux = model_runner.server_args.enable_pdmux
+        enable_pdmux = self.enable_pdmux
         attn_backend, pdmux_ctx = self._resolve_decode_pdmux()
         if not enable_pdmux:
             forward_batch = self.load_batch(forward_batch, pp_proxy_tensors)
@@ -263,7 +263,7 @@ class EagerRunner(BaseRunner):
         model_runner = self.model_runner
         kwargs = model_runner._extend_forward_kwargs(forward_batch, pp_proxy_tensors)
 
-        if not model_runner.server_args.enable_pdmux:
+        if not self.enable_pdmux:
             forward_batch = self.load_batch(forward_batch, pp_proxy_tensors)
 
         if forward_batch.needs_forward_metadata_init():
@@ -393,7 +393,7 @@ class EagerRunner(BaseRunner):
         # Padded idle (DP-attn MLP sync) needs metadata reinit; unpadded must
         # drop stale forward_metadata to avoid an SWA use-after-free on req_pool.
         if forward_batch.batch_size > 0:
-            if not model_runner.server_args.enable_pdmux:
+            if not self.enable_pdmux:
                 forward_batch = self.load_batch(forward_batch, pp_proxy_tensors)
             model_runner.attn_backend.init_forward_metadata(forward_batch)
         else: