diff --git a/csrc/core/batch_invariant.hpp b/csrc/core/batch_invariant.hpp
index fffe96b86857..8273bc74b1ef 100644
--- a/csrc/core/batch_invariant.hpp
+++ b/csrc/core/batch_invariant.hpp
@@ -1,7 +1,6 @@
 #pragma once
 #include <cstdlib>
 #include <string>
-#include <cctype>
 
 namespace vllm {
 
diff --git a/vllm/model_executor/layers/batch_invariant.py b/vllm/model_executor/layers/batch_invariant.py
index bcdd30500329..2e1beeec1b71 100644
--- a/vllm/model_executor/layers/batch_invariant.py
+++ b/vllm/model_executor/layers/batch_invariant.py
@@ -8,15 +8,12 @@
 import torch
 
 import vllm.envs as envs
-from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.mem_utils import get_max_shared_memory_bytes
 from vllm.utils.platform_utils import num_compute_units
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
-logger = init_logger(__name__)
-
 
 def _matmul_launch_metadata(
     grid: Callable[..., Any], kernel: Any, args: dict[str, Any]
@@ -24,15 +21,8 @@ def _matmul_launch_metadata(
     ret = {}
     m, n, k = args["M"], args["N"], args["K"]
     ret["name"] = f"{kernel.name} [M={m}, N={n}, K={k}]"
-    if "tiles_per_update" in args:
-        ret["name"] = (
-            f"{kernel.name} [M={m}, N={n}, K={k}, "
-            f"tiles_per_update={args['tiles_per_update']:02}]"
-        )
-    if "c_ptr" in args:
-        bytes_per_elem = args["c_ptr"].element_size()
-    else:
-        bytes_per_elem = 1 if args["FP8_OUTPUT"] else 2
+
+    bytes_per_elem = args["c_ptr"].element_size()
     ret[f"flops{bytes_per_elem * 8}"] = 2.0 * m * n * k
     ret["bytes"] = bytes_per_elem * (m * k + n * k + m * n)
     return ret
@@ -191,7 +181,6 @@ def grid(META):
             "num_warps": 8,
         },
     }
-    # print(a.device, b.device, c.device)
     matmul_kernel_persistent[grid](
         a,
         b,
@@ -420,7 +409,7 @@ def log_softmax(input: torch.Tensor, dim: int = -1) -> torch.Tensor:
         input: Input tensor
         dim: Dimension along which to compute log_softmax
              (only -1 or last dim supported)
-    >> Stashed changes
+
     Returns:
         Tensor with log_softmax applied along the specified dimension
     """
@@ -910,18 +899,11 @@ def linear_batch_invariant(input, weight, bias=None):
 
 _batch_invariant_MODE = False
 _batch_invariant_LIB = None
-_original_torch_bmm = None
-_original_fp16_reduction_precision = None
-_original_bf16_reduction_precision = None
-_original_cublas_workspace_cfg = None
-_original_cublaslt_workspace_size = None
 _fp16_block_size_n = 256
 
 
 def enable_batch_invariant_mode():
-    global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
-    global _original_fp16_reduction_precision, _original_bf16_reduction_precision
-    global _original_cublas_workspace_cfg, _original_cublaslt_workspace_size
+    global _batch_invariant_MODE, _batch_invariant_LIB
     global _fp16_block_size_n
 
     if _batch_invariant_MODE:
@@ -941,10 +923,6 @@ def enable_batch_invariant_mode():
         # Hopper (SM90) and Blackwell (SM100): the only source of batch
         # variance is split-k, which we disable via the cuBLAS workspace
         # config.
-        _original_cublas_workspace_cfg = os.environ.get("CUBLAS_WORKSPACE_CONFIG", None)
-        _original_cublaslt_workspace_size = os.environ.get(
-            "CUBLASLT_WORKSPACE_SIZE", None
-        )
         os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
         os.environ["CUBLASLT_WORKSPACE_SIZE"] = "1"
 
@@ -966,16 +944,8 @@ def enable_batch_invariant_mode():
     _batch_invariant_LIB.impl(
         "aten::bmm", bmm_batch_invariant, "CUDA", allow_override=True
     )
-    _original_torch_bmm = torch.bmm
     torch.bmm = bmm_batch_invariant
 
-    _original_bf16_reduction_precision = (
-        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction
-    )
-    _original_fp16_reduction_precision = (
-        torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction
-    )
-
     reduced_precision_val = (
         (False, False) if is_torch_equal_or_newer("2.10.0") else False
     )