53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import annotations
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from typing import TYPE_CHECKING
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from paddle import _C_ops
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from paddle.framework import in_dynamic_or_pir_mode
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if TYPE_CHECKING:
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from paddle import Tensor
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def fast_ln(
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x: Tensor,
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scale: Tensor,
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bias: Tensor,
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epsilon: float = 1e-5,
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) -> tuple[Tensor, Tensor, Tensor]:
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r"""
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Apply Fast LayerNorm kernel.
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Args:
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x (Tensor): the input Tensor..
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scale (Tensor): the weight Tensor to affine output.
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bias (Tensor): the bias Tensor to affine output.
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epsilon (float): a small float number to avoid divide 0.
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Returns:
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y: the Tensor after performing layernorm.
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mean: the mean of input tensor
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invvar: the invert variance(scaling factor) of y
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.fast_ln(
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x,
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scale,
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bias,
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epsilon,
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)
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