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https://github.com/zebrajr/pytorch.git
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fd4beb7a05
Fixes #102768 - Provides proper function declarations in generated `torch/nn/functional.pyi`. - Moves some functions from manually defined in `functional.pyi.in` to generated code, in order to single-source the signature. - Includes some of the functions in `torch._C._nn` into its `.pyi.in`, but not exhaustive (only what's already there). Pull Request resolved: https://github.com/pytorch/pytorch/pull/102918 Approved by: https://github.com/drisspg, https://github.com/malfet
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
from typing import List, Optional, overload, Sequence, Tuple, Union
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from torch import memory_format, Tensor
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from torch.types import _bool, _device, _dtype, _int, _size
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# Defined in tools/autograd/templates/python_nn_functions.cpp
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${c_nn_function_hints}
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# Defined in aten/src/ATen/native/mkldnn/Linear.cpp
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def mkldnn_linear(input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ...
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# Defined at aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp
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def mkldnn_reorder_conv2d_weight(
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self: Tensor,
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padding: List,
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stride: List,
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dilatation: List,
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groups: int,
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) -> Tensor: ...
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def mkldnn_reorder_conv3d_weight(
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self: Tensor,
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padding: List,
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stride: List,
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dilatation: List,
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groups: int,
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) -> Tensor: ...
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# Defined in aten/src/ATen/native/mkldnn/Prelu.cpp
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def mkldnn_prelu(input: Tensor, weight: Tensor) -> Tensor: ...
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# Defined at tools/autograd/templates/python_nn_functions.cpp
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@overload
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def _parse_to(
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device: _device,
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dtype: _dtype,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> Tuple[_device, _dtype, _bool, memory_format]: ...
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@overload
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def _parse_to(
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dtype: _dtype,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> Tuple[_device, _dtype, _bool, memory_format]: ...
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@overload
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def _parse_to(
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tensor: Tensor,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> Tuple[_device, _dtype, _bool, memory_format]: ...
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# Defined in aten/src/ATen/native/PadSequence.cpp
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def pad_sequence(
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sequences: List[Tensor],
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batch_first: bool = False,
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padding_value: float = ...,
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) -> Tensor: ...
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def flatten_dense_tensors(tensors: List[Tensor]) -> Tensor: ...
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def unflatten_dense_tensors(flat: Tensor, tensors: List[Tensor]) -> List[Tensor]: ...
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