87 lines
4.0 KiB
Python
87 lines
4.0 KiB
Python
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# mypy: disable-error-code="type-arg"
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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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def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ...
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def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ...
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def avg_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ...
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def avg_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ...
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def elu_(input: Tensor, alpha: float = ...) -> Tensor: ...
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def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> Tuple[Tensor, Tensor]: ...
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def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> Tuple[Tensor, Tensor]: ...
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def gelu(input: Tensor, approximate: str = ...) -> Tensor: ...
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def hardsigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
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def hardtanh(input: Tensor, min_val: float = ..., max_val: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ...
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def hardtanh_(input: Tensor, min_val: float = ..., max_val: float = ...) -> Tensor: ...
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def leaky_relu(input: Tensor, negative_slope: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ...
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def leaky_relu_(input: Tensor, negative_slope: float = ...) -> Tensor: ...
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def linear(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: ...
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def log_sigmoid(input: Tensor) -> Tensor: ...
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def one_hot(tensor: Tensor, num_classes: int = ...) -> Tensor: ...
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def pad(input: Tensor, pad: Sequence[int], mode: str = ..., value: Optional[float] = None) -> Tensor: ...
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def scaled_dot_product_attention(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: float = 0.0, is_causal: bool = False, scale: Optional[float] = None) -> Tensor: ...
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def softplus(input: Tensor, beta: float = ..., threshold: float = ...) -> Tensor: ...
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def softshrink(input: Tensor, lambd: float = ...) -> Tensor: ...
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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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