[CP V1 Deprecation 4/5] Canonicalize prefill CP API names (#36229)

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

@@ -28,7 +28,7 @@ from sglang.srt.layers.cp.utils import (
     cp_gather_after_forward,
     cp_shard_model_inputs,
     get_cp_strategy,
-    is_cp_v2_active,
+    is_cp_active,
     prepare_cp_forward,
 )
 from sglang.srt.layers.pooler import EmbeddingPoolerOutput
@@ -281,8 +281,8 @@ class EagerRunner(BaseRunner):
         if not self.enable_pdmux:
             forward_batch = self.load_batch(forward_batch, pp_proxy_tensors)
 
-        cp_v2_active = is_cp_v2_active(forward_batch)
-        if cp_v2_active:
+        cp_active = is_cp_active(forward_batch)
+        if cp_active:
             prepare_cp_forward(forward_batch)
 
         # Target verify can arrive with ``forward_metadata_ready`` set by an
@@ -293,7 +293,7 @@ class EagerRunner(BaseRunner):
         # directly from the live ``spec_info`` tensors.
         if (
             forward_batch.needs_forward_metadata_init()
-            or cp_v2_active
+            or cp_active
             or forward_batch.forward_mode.is_target_verify()
         ):
             if model_runner.ps.attn_dcp_size > 1 and hasattr(
@@ -330,7 +330,7 @@ class EagerRunner(BaseRunner):
                 torch.get_device_module(model_runner.device),
             )
 
-        if not cp_v2_active:
+        if not cp_active:
             forward_batch.attn_cp_metadata = None
 
         category = (
@@ -344,7 +344,7 @@ class EagerRunner(BaseRunner):
                 _is_hip
                 and pcg_runner is not None
                 and not isinstance(pcg_runner, EagerRunner)
-                and not cp_v2_active
+                and not cp_active
             ):
                 # HIP PCG eager fallback: enter the PCG context so Dynamo guards
                 # and PCG-specific MoE/attention paths stay consistent.
@@ -366,8 +366,8 @@ class EagerRunner(BaseRunner):
                         forward_batch,
                         **kwargs,
                     )
-            elif cp_v2_active:
-                ret = self._execute_extend_cp_v2(forward_batch, kwargs)
+            elif cp_active:
+                ret = self._execute_extend_cp(forward_batch, kwargs)
             else:
                 ret = model_runner.model.forward(
                     forward_batch.input_ids,
@@ -377,7 +377,7 @@ class EagerRunner(BaseRunner):
                 )
         return ret
 
-    def _execute_extend_cp_v2(
+    def _execute_extend_cp(
         self, forward_batch: ForwardBatch, kwargs: dict
     ) -> Union[LogitsProcessorOutput, PPProxyTensors]:
         """CP extend: shard inputs at the model boundary, run the body on the