[small][batch invariance] Rename the env and internal flags to simplify usage (#26855)

Signed-off-by: Bram Wasti <bwasti@meta.com>

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -16,7 +16,7 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.batch_invariant import (
-    vllm_kernel_override_batch_invariant,
+    vllm_is_batch_invariant,
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
@@ -841,7 +841,7 @@ def get_moe_configs(
     """
 
     # Avoid optimizing for the batch invariant case. Use default config
-    if vllm_kernel_override_batch_invariant():
+    if vllm_is_batch_invariant():
         return None
 
     # First look up if an optimized configuration is available in the configs
@@ -976,7 +976,7 @@ def get_default_config(
     dtype: str | None,
     block_shape: list[int] | None = None,
 ) -> dict[str, int]:
-    if vllm_kernel_override_batch_invariant():
+    if vllm_is_batch_invariant():
         config = {
             "BLOCK_SIZE_M": 64,
             "BLOCK_SIZE_N": 64,
@@ -1136,7 +1136,7 @@ def fused_topk_bias(
     ) + e_score_correction_bias.unsqueeze(0)
 
     # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = vllm_kernel_override_batch_invariant()
+    use_sorted = vllm_is_batch_invariant()
     topk_indices = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=use_sorted)[1]
     topk_weights = scores.gather(1, topk_indices)
     if renormalize:
@@ -1200,7 +1200,7 @@ def grouped_topk(
         )  # [n, n_group]
 
     # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = vllm_kernel_override_batch_invariant()
+    use_sorted = vllm_is_batch_invariant()
     group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=use_sorted)[
         1
     ]  # [n, top_k_group]