44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# mypy: ignore-errors
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from __future__ import annotations
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import math
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import torch
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import torch_npu
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from vllm.model_executor.layers.fla.ops.utils import tensor_cache
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@tensor_cache
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def _l2norm_unit_weight(dim: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
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# RMSNorm with weight 1/sqrt(dim) matches L2 norm: x / sqrt(sum(x^2)).
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return torch.full((dim,), 1.0 / math.sqrt(dim), dtype=dtype, device=device)
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def l2norm_310p(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
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"""L2-normalize the last dimension using the 310P NPU RMSNorm kernel."""
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orig_shape = x.shape
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dim = x.shape[-1]
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x_2d = x.reshape(-1, dim).contiguous()
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weight = _l2norm_unit_weight(dim, x.dtype, x.device)
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# RMSNorm: y = x / sqrt(mean(x^2) + eps_rms) * weight
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# With weight=1/sqrt(dim), this equals x / sqrt(sum(x^2) + dim * eps_rms).
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# L2 norm needs y = x / sqrt(sum(x^2) + eps), so eps_rms = eps / dim.
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y, _ = torch_npu.npu_rms_norm(x_2d, weight, eps / dim)
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return y.reshape(orig_shape)
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