LayerNorm 算子 KernelMod 创建失败

#### 1.Describe the current behavior / 问题描述 (Mandatory / 必填) > `mindspore.mint.nn.functional.layer_norm` 相关接口执行时报错 `Create kernelmod for op LayerNormExt failed`，导致 LayerNorm 计算失败。 #### 2.Environment / 环境信息 (Mandatory / 必填) - **Hardware Environment / 硬件环境(Mandatory / 必填)**: | 后端类型 | 硬件具体类别 | | --- | --- | | CPU | Mac CPU/Win CPU | - **Software Environment / 软件环境 (Mandatory / 必填)**: | Software | Version | | --- | --- | | MindSpore | MindSpore 2.4.0 | | Python | Python 3.10 | | OS platform | Mac Win | #### 3.Steps to reproduce the issue / 重现步骤 (Mandatory / 必填) > 1. 运行 `test_layer_norm.py` 部分相关代码（已提交 PR ） ```python def test_layernorm_fixed_dtype_output_equality(): """ (1b) 固定dtype=float32, 随机输入, 对比输出 """ print("===== LayerNorm fixed dtype output equality test =====") x_np = np.random.randn(2,3,4).astype(np.float32) w_np = np.random.randn(4).astype(np.float32) b_np = np.random.randn(4).astype(np.float32) ms_in = Tensor(x_np, mstype.float32) ms_w = Tensor(w_np, mstype.float32) ms_b = Tensor(b_np, mstype.float32) out_ms = F_ms.layer_norm(ms_in, normalized_shape=(4,), weight=ms_w, bias=ms_b, eps=1e-5).asnumpy() x_torch = torch.tensor(x_np, dtype=torch.float32) w_torch = torch.tensor(w_np, dtype=torch.float32) b_torch = torch.tensor(b_np, dtype=torch.float32) out_pt = F_torch.layer_norm(x_torch, normalized_shape=(4,), weight=w_torch, bias=b_torch, eps=1e-5).numpy() diff = np.abs(out_ms - out_pt).max() print("Max diff:", diff) assert diff < 1e-3, f"LayerNorm diff too large: {diff}" def test_layernorm_fixed_shape_diff_params(): """ (1c) 测试 normalized_shape 是 int or tuple, weight/bias 可省略 """ print("===== LayerNorm fixed shape diff params test =====") x = Tensor(np.random.randn(2,4).astype(np.float32)) # normalized_shape int vs tuple out1 = F_ms.layer_norm(x, 4) # int out2 = F_ms.layer_norm(x, (4,)) # tuple diff = np.abs(out1.asnumpy() - out2.asnumpy()).max() print(…