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[Typing][C-73] Add type annotations for python/paddle/incubate/nn/functional/fused_layer_norm.py #67159

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merged 8 commits into from
Aug 18, 2024
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[Typing][C-73] Add type annotations for python/paddle/incubate/nn/functional/fused_layer_norm.py #67159

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5c2aa86
Update fused_layer_norm.py
haoyu2022 Aug 7, 2024
48e7400
Update python/paddle/incubate/nn/functional/fused_layer_norm.py
haoyu2022 Aug 7, 2024
127a017
fix bugs
haoyu2022 Aug 15, 2024
b6c29ff
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
haoyu2022 Aug 15, 2024
3738e71
update
haoyu2022 Aug 15, 2024
203dd40
update and fix
haoyu2022 Aug 17, 2024
7c58812
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
haoyu2022 Aug 17, 2024
41c37dc
fix
haoyu2022 Aug 18, 2024
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40 changes: 40 additions & 0 deletions python/paddle/incubate/nn/functional/fused_layer_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,11 +12,51 @@
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from typing import TYPE_CHECKING, overload

import paddle
from paddle import _C_ops
from paddle.framework import LayerHelper, in_dynamic_mode, in_pir_mode

if TYPE_CHECKING:
from paddle import Tensor


@overload
def fused_layer_norm(
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根据 residual 使用 overload

from __future__ import annotations

from typing import TYPE_CHECKING, overload

import paddle
from paddle import _C_ops
from paddle.framework import LayerHelper, in_dynamic_mode, in_pir_mode

if TYPE_CHECKING:
    from paddle import Tensor

@overload
def fused_layer_norm(
    x: Tensor,
    norm_weight: Tensor,
    norm_bias: Tensor,
    epsilon: float,
    residual_alpha: float = ...,
    begin_norm_axis: int = ...,
    bias: Tensor | None = ...,
    residual: None = ...,
    quant_scale: float = ...,
    quant_round_type: float = ...,
    quant_max_bound: float = ...,
    quant_min_bound: float = ...,
) -> Tensor: ...

@overload
def fused_layer_norm(
    x: Tensor,
    norm_weight: Tensor,
    norm_bias: Tensor,
    epsilon: float,
    residual_alpha: float = ...,
    begin_norm_axis: int = ...,
    bias: Tensor | None = ...,
    residual: Tensor = ...,
    quant_scale: float = ...,
    quant_round_type: float = ...,
    quant_max_bound: float = ...,
    quant_min_bound: float = ...,
) -> tuple[Tensor, Tensor]: ...

def fused_layer_norm(
    x,
    norm_weight,
    norm_bias,
    epsilon,
    residual_alpha=1.0,
    begin_norm_axis=1,
    bias=None,
    residual=None,
    quant_scale=-1,
    quant_round_type=0,
    quant_max_bound=0,
    quant_min_bound=0,
):
    r"""
    Apply Fused LayerNorm kernel. Also support LayerNorm(bias + residual_alpha * residual + x) fused pattern.

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好的，感谢

x: Tensor,
norm_weight: Tensor,
norm_bias: Tensor,
epsilon: float,
residual_alpha: float = ...,
begin_norm_axis: int = ...,
bias: Tensor | None = ...,
residual: None = ...,
quant_scale: float = ...,
quant_round_type: float = ...,
quant_max_bound: float = ...,
quant_min_bound: float = ...,
) -> Tensor: ...


@overload
def fused_layer_norm(
x: Tensor,
norm_weight: Tensor,
norm_bias: Tensor,
epsilon: float,
residual_alpha: float = ...,
begin_norm_axis: int = ...,
bias: Tensor | None = ...,
residual: Tensor = ...,
quant_scale: float = ...,
quant_round_type: float = ...,
quant_max_bound: float = ...,
quant_min_bound: float = ...,
) -> tuple[Tensor, Tensor]: ...


def fused_layer_norm(
x,
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