[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/mean.py

@@ -0,0 +1,177 @@
+# 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
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def mean_kernel(
+    input_ptr,
+    output_ptr,
+    input_stride0,
+    input_stride1,
+    input_stride2,
+    output_stride0,
+    output_stride1,
+    M,  # size before reduction dim
+    N,  # size of reduction dim
+    K,  # size after reduction dim
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Kernel for computing mean along a single dimension.
+    Input is viewed as (M, N, K) where N is the dimension being reduced.
+    """
+    # Program ID gives us which output element we're computing
+    pid = tl.program_id(0)
+
+    # Compute output indices
+    m_idx = pid // K
+    k_idx = pid % K
+
+    # Bounds check
+    if m_idx >= M or k_idx >= K:
+        return
+    # Accumulate sum across reduction dimension
+    acc = 0.0
+    for n_start in range(0, N, BLOCK_SIZE):
+        n_offsets = n_start + tl.arange(0, BLOCK_SIZE)
+        mask = n_offsets < N
+
+        # Calculate input indices
+        input_idx = m_idx * input_stride0 + n_offsets * input_stride1 + k_idx * input_stride2
+        # Load and accumulate
+        vals = tl.load(input_ptr + input_idx, mask=mask, other=0.0)
+        acc += tl.sum(vals)
+
+    # Compute mean and store
+    mean_val = acc / N
+    output_idx = m_idx * output_stride0 + k_idx * output_stride1
+    tl.store(output_ptr + output_idx, mean_val)
+
+
+def mean_dim(
+    input_: torch.Tensor,
+    dim: int,
+    keepdim: bool = False,
+    dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    """
+    Triton implementation of torch.mean with single dimension reduction.
+
+    Args:
+        input: Input tensor
+        dim: Single dimension along which to compute mean
+        keepdim: Whether to keep the reduced dimension
+        dtype: Output dtype. If None, uses input dtype (or float32 for integer inputs)
+
+    Returns:
+        Tensor with mean values along specified dimension
+    """
+    # Validate inputs
+    assert -input_.ndim <= dim < input_.ndim, (
+        f"Invalid dimension {dim} for tensor with {input_.ndim} dimensions")
+
+    # Handle negative dim
+    if dim < 0:
+        dim = dim + input_.ndim
+    # Handle dtype
+    if dtype is None:
+        if input_.dtype in [torch.int8, torch.int16, torch.int32, torch.int64]:
+            dtype = torch.float32
+        else:
+            dtype = input_.dtype
+    # Convert input to appropriate dtype if needed
+    if input_.dtype != dtype:
+        input_ = input_.to(dtype)
+
+    # Get input shape and strides
+    shape = list(input_.shape)
+    # Calculate dimensions for kernel
+    M = 1
+    for i in range(dim):
+        M *= shape[i]
+
+    N = shape[dim]
+
+    K = 1
+    for i in range(dim + 1, len(shape)):
+        K *= shape[i]
+
+    # Reshape input to 3D view (M, N, K)
+    input_3d = input_.reshape(M, N, K)
+
+    # Create output shape
+    if keepdim:
+        output_shape = shape.copy()
+        output_shape[dim] = 1
+    else:
+        output_shape = shape[:dim] + shape[dim + 1:]
+
+    # Create output tensor
+    output = torch.empty(output_shape, dtype=dtype, device=input_.device)
+
+    # Reshape output for kernel
+    if keepdim:
+        output_2d = output.reshape(M, 1, K).squeeze(1)
+    else:
+        output_2d = output.reshape(M, K)
+
+    # Launch kernel
+    grid = (M * K, )
+    BLOCK_SIZE = 1024
+
+    mean_kernel[grid](
+        input_3d,
+        output_2d,
+        input_3d.stride(0),
+        input_3d.stride(1),
+        input_3d.stride(2),
+        output_2d.stride(0),
+        output_2d.stride(1) if output_2d.ndim > 1 else 0,
+        M,
+        N,
+        K,
+        BLOCK_SIZE,
+    )
+
+    return output
+
+
+def mean_batch_invariant(
+    input_: torch.Tensor,
+    dim: list[int],
+    keepdim: bool = False,
+    dtype: torch.dtype = torch.float16,
+):
+    assert dtype is None or dtype == torch.float32, f"unsupported dtype: {dtype}"
+    if len(dim) == 1:
+        return mean_dim(input_, dim[0], keepdim=keepdim)
+    else:
+        assert input_.dtype in {torch.float16, torch.bfloat16, torch.float32
+                                }, ("only float types supported for now")
+        if len(dim) == 0:
+            dim = list(range(input_.ndim))
+        n_elems = 1
+        for d in dim:
+            n_elems *= input_.shape[d]
+        return torch.sum(input_, dim=dim, keepdim=keepdim,
+                         dtype=torch.float32).to(dtype
+                                                 or input_.dtype) / n_elems