init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/_310p/ops/fla/l2norm.py

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+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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.
+# mypy: ignore-errors
+
+from __future__ import annotations
+
+import math
+
+import torch
+import torch_npu
+from vllm.model_executor.layers.fla.ops.utils import tensor_cache
+
+
+@tensor_cache
+def _l2norm_unit_weight(dim: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
+    # RMSNorm with weight 1/sqrt(dim) matches L2 norm: x / sqrt(sum(x^2)).
+    return torch.full((dim,), 1.0 / math.sqrt(dim), dtype=dtype, device=device)
+
+
+def l2norm_310p(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
+    """L2-normalize the last dimension using the 310P NPU RMSNorm kernel."""
+    orig_shape = x.shape
+    dim = x.shape[-1]
+    x_2d = x.reshape(-1, dim).contiguous()
+    weight = _l2norm_unit_weight(dim, x.dtype, x.device)
+    # RMSNorm: y = x / sqrt(mean(x^2) + eps_rms) * weight
+    # With weight=1/sqrt(dim), this equals x / sqrt(sum(x^2) + dim * eps_rms).
+    # L2 norm needs y = x / sqrt(sum(x^2) + eps), so eps_rms = eps / dim.
+    y, _ = torch_npu.npu_rms_norm(x_2d, weight, eps / dim)
+    return y.reshape(orig_shape)