[Feature] implement basic framework for batch invariant (#5517)

### What this PR does / why we need it?
This PR implement the basic framework for batch invariant, please see
https://github.com/vllm-project/vllm-ascend/issues/5487.
### Does this PR introduce _any_ user-facing change?
we reuse the function `vllm_is_batch_invariant` in vllm to judge if
batch invariant is enabled.

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Signed-off-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Signed-off-by: zjchenn <zjchenn@gmail.com>
Signed-off-by: wangx700 <wangxin700@huawei.com>
Co-authored-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Co-authored-by: zjchenn <zjchenn@gmail.com>
Co-authored-by: wangx700 <wangxin700@huawei.com>

vllm_ascend/ops/triton/batch_invariant/softmax.py

@@ -0,0 +1,29 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/batch_invariant.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+import torch
+
+
+def softmax_batch_invariant(input_, dim, dtype=None):
+    # Compute softmax in a deterministic way
+    # First subtract max for numerical stability (standard practice)
+    input_max = torch.amax(input_, dim=dim, keepdim=True)
+    input_ = input_ - input_max
+    exp_x = torch.exp(input_)
+    sum_exp_x = torch.sum(exp_x, dim=dim, keepdim=True)
+    return exp_x / sum_exp_x