[Refactor] Remove Unused Func in Batch Invariant (#28881)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/batch_invariant.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import contextlib
 import os
-from collections import namedtuple
 from collections.abc import Callable
 from functools import cache
 from typing import Any
@@ -725,10 +723,6 @@ _original_cublas_workspace_cfg = None
 _original_cublaslt_workspace_size = None
 
 
-def is_batch_invariant_mode_enabled():
-    return _batch_invariant_MODE
-
-
 def enable_batch_invariant_mode():
     global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
     global _original_fp16_reduction_precision, _original_bf16_reduction_precision
@@ -791,73 +785,6 @@ def enable_batch_invariant_mode():
     torch.backends.cuda.preferred_blas_library(backend="cublaslt")
 
 
-def disable_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
-    if not _batch_invariant_MODE:
-        return
-
-    if _batch_invariant_LIB is not None:
-        _batch_invariant_LIB._destroy()
-    if _original_torch_bmm is not None:
-        torch.bmm = _original_torch_bmm
-        _original_torch_bmm = None
-
-    if _original_bf16_reduction_precision is not None:
-        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = (
-            _original_bf16_reduction_precision
-        )
-        _original_bf16_reduction_precision = None
-    if _original_fp16_reduction_precision is not None:
-        torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = (
-            _original_fp16_reduction_precision
-        )
-        _original_fp16_reduction_precision = None
-
-    torch.backends.cuda.preferred_blas_library(backend="default")
-
-    if not is_torch_equal_or_newer("2.10.0.dev"):
-        # Set cublas env vars to previous results. If previous results are None,
-        # that means the env vars were not set, so we should remove them.
-        if _original_cublas_workspace_cfg:
-            os.environ["CUBLAS_WORKSPACE_CONFIG"] = _original_cublas_workspace_cfg
-        elif "CUBLAS_WORKSPACE_CONFIG" in os.environ:
-            del os.environ["CUBLAS_WORKSPACE_CONFIG"]
-
-        if _original_cublaslt_workspace_size:
-            os.environ["CUBLASLT_WORKSPACE_SIZE"] = _original_cublaslt_workspace_size
-        elif "CUBLASLT_WORKSPACE_SIZE" in os.environ:
-            del os.environ["CUBLASLT_WORKSPACE_SIZE"]
-
-    _original_cublas_workspace_cfg = None
-    _original_cublaslt_workspace_size = None
-
-    _batch_invariant_MODE = False
-    _batch_invariant_LIB = None
-
-
-@contextlib.contextmanager
-def set_batch_invariant_mode(enabled: bool = True):
-    global _batch_invariant_MODE, _batch_invariant_LIB
-    old_data = (_batch_invariant_MODE, _batch_invariant_LIB)
-    if enabled:
-        enable_batch_invariant_mode()
-    else:
-        disable_batch_invariant_mode()
-    yield
-    if _batch_invariant_LIB is not None:
-        _batch_invariant_LIB._destroy()
-    _batch_invariant_MODE, _batch_invariant_LIB = old_data
-
-
-AttentionBlockSize = namedtuple("AttentionBlockSize", ["block_m", "block_n"])
-
-
-def get_batch_invariant_attention_block_size() -> AttentionBlockSize:
-    return AttentionBlockSize(block_m=16, block_n=16)
-
-
 @cache
 def vllm_is_batch_invariant():
     env_key = "VLLM_BATCH_INVARIANT"