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6edf340338
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38157 This removes the error prone process of assembling `torch/__init__.pyi` (and frequently forgetting to expose things), since now we can simply rely on the true source file to get things done. Most of the old codegen in gen_pyi.py is now rerouted to various files: - `torch/_C/__init__.pyi` (the dumping pile of all misc bindings) - `torch/_C/_nn.pyi` (NN function bindings) - `torch/_C/_VariableFunctions.pyi` (torch function bindings) `torch.types` grew a bunch more definitions that previously where defined in `torch/__init__.pyi` Some miscellaneous changes - Fixed a bug where we treat single TensorList argument as implying varargs are accepted. This is actually only supported on IntList. This means we can correctly generate a stub for dequantize. - Add missing manual stub for nonzero - Switched torch/onnx/operators.py to directly refer to _C module, since apparently mypy doesn't think that methods prefixed with underscores get reexported. This may be a recurring theme; maybe we need to find a better way to solve it. Because I was really lazy, I dumped namedtuple definitions in both `torch._C` and `torch._C._VariableFunctions`. This is definitely wrong. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Differential Revision: D21497400 Pulled By: ezyang fbshipit-source-id: 07b126141c82efaca37be27c07255cb2b9b3f064
331 lines
14 KiB
Python
331 lines
14 KiB
Python
from torch import Tensor
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from torch.types import _size
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from typing import Any, Optional, Tuple, Dict, List, Callable
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from .common_types import _ratio_any_t
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# 'TypedDict' is a new accepted type that represents a dictionary with a fixed set of allowed keys.
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# It is standards-track but not in `typing` yet. We leave this hear to be uncommented once the feature
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# is wide-spread.
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# from mypy_extensions import TypedDict
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# GRID_SAMPLE_INTERPOLATION_MODES = TypedDict('GRID_SAMPLE_INTERPOLATION_MODES', {'bilinear': int, 'nearest': int})
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# GRID_SAMPLE_PADDING_MODES = TypedDict('GRID_SAMPLE_PADDING_MODES', {'zeros': int, 'border': int, 'reflection': int})
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GRID_SAMPLE_INTERPOLATION_MODES = Dict[str, int]
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GRID_SAMPLE_PADDING_MODES = Dict[str, int]
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# These stubs were generated by running stubgen (`stubgen --parse-only functional.py`), followed by manual cleaning.
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#
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# The 'BroadcastingList{1,2,3}' types were replaced by `_size` or _output_ratio, as appropriate.
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# This was necessary since the JIT uses BroadcastingList* types but static checking with mypy etc requires a `Sequence`
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# type. There is no way to express the expected lengths of these lists in the current Python typing system.
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#
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# Functions created via `_add_docstr` in `functional.py` where merely typed as `Any` by `stubgen`, so those were
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# deleted from the stub and replaced by generated declarations. See `gen_pyi` for the implementation of the code
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# generation logic for those functions. In the future, it might be worth looking into using the mypy plugin system
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# to encode the type semantics of `_add_docstr`, should that system ever become widespread.
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def fractional_max_pool2d_with_indices(input: Tensor, kernel_size: _size, output_size: Optional[_size] = ...,
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output_ratio: Optional[_ratio_any_t] = ..., return_indices: bool = ...,
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_random_samples: Optional[Tensor] = ...) -> Tuple[Tensor, Tensor]: ...
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def fractional_max_pool3d_with_indices(input: Tensor, kernel_size: _size, output_size: Optional[_size] = ...,
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output_ratio: Optional[_ratio_any_t] = ..., return_indices: bool = ...,
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_random_samples: Optional[Tensor] = ...) -> Tuple[Tensor, Tensor]: ...
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def max_pool1d_with_indices(input: Tensor, kernel_size: _size, stride: Optional[_size] = ..., padding: _size = ...,
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dilation: _size = ..., ceil_mode: bool = ..., return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def max_pool2d_with_indices(input: Tensor, kernel_size: _size, stride: Optional[_size] = ..., padding: _size = ...,
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dilation: _size = ..., ceil_mode: bool = ..., return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def max_pool3d_with_indices(input: Tensor, kernel_size: _size, stride: Optional[_size] = ..., padding: _size = ...,
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dilation: _size = ..., ceil_mode: bool = ..., return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def max_unpool1d(input: Tensor, indices: Tensor, kernel_size: _size, stride: Optional[_size] = ...,
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padding: _size = ..., output_size: Optional[_size] = ...) -> Tensor: ...
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def max_unpool2d(input: Tensor, indices: Tensor, kernel_size: _size, stride: Optional[_size] = ...,
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padding: _size = ..., output_size: Optional[_size] = ...) -> Tensor: ...
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def max_unpool3d(input: Tensor, indices: Tensor, kernel_size: _size, stride: Optional[_size] = ...,
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padding: _size = ..., output_size: Optional[_size] = ...) -> Tensor: ...
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def lp_pool2d(input: Tensor, norm_type: float, kernel_size: int, stride: Optional[_size] = ...,
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ceil_mode: bool = ...) -> Tensor: ...
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def lp_pool1d(input: Tensor, norm_type: float, kernel_size: int, stride: Optional[_size] = ...,
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ceil_mode: bool = ...) -> Tensor: ...
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def adaptive_max_pool1d_with_indices(input: Tensor, output_size: _size, return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def adaptive_max_pool2d_with_indices(input: Tensor, output_size: _size, return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def adaptive_max_pool3d_with_indices(input: Tensor, output_size: _size, return_indices: bool = ...) -> Tuple[
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Tensor, Tensor]: ...
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def adaptive_avg_pool2d(input: Tensor, output_size: _size) -> Tensor: ...
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def adaptive_avg_pool3d(input: Tensor, output_size: _size) -> Tensor: ...
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def dropout(input: Tensor, p: float = ..., training: bool = ..., inplace: bool = ...) -> Tensor: ...
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def alpha_dropout(input: Tensor, p: float = ..., training: bool = ..., inplace: bool = ...) -> Tensor: ...
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def dropout2d(input: Tensor, p: float = ..., training: bool = ..., inplace: bool = ...) -> Tensor: ...
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def dropout3d(input: Tensor, p: float = ..., training: bool = ..., inplace: bool = ...) -> Tensor: ...
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def feature_alpha_dropout(input: Tensor, p: float = ..., training: bool = ..., inplace: bool = ...) -> Tensor: ...
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def threshold(input: Tensor, threshold: float, value: float, inplace: bool = ...) -> Tensor: ...
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def relu(input: Tensor, inplace: bool = ...) -> Tensor: ...
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def glu(input: Tensor, dim: int = ...) -> Tensor: ...
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def hardtanh(input: Tensor, min_val: float = ..., max_val: float = ..., inplace: bool = ...) -> Tensor: ...
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def relu6(input: Tensor, inplace: bool = ...) -> Tensor: ...
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def elu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
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def selu(input: Tensor, inplace: bool = ...) -> Tensor: ...
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def celu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
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def leaky_relu(input: Tensor, negative_slope: float = ..., inplace: bool = ...) -> Tensor: ...
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def prelu(input: Tensor, weight: Tensor) -> Tensor: ...
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def rrelu(input: Tensor, lower: float = ..., upper: float = ..., training: bool = ...,
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inplace: bool = ...) -> Tensor: ...
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def gelu(input: Any): ...
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def hardshrink(input: Tensor, lambd: float = ...) -> Tensor: ...
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def tanhshrink(input: Any): ...
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def softsign(input: Any): ...
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def softmin(input: Tensor, dim: Optional[int] = ..., _stacklevel: int = ..., dtype: Optional[int] = ...) -> Tensor: ...
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def softmax(input: Tensor, dim: Optional[int] = ..., _stacklevel: int = ..., dtype: Optional[int] = ...) -> Tensor: ...
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def gumbel_softmax(logits: Tensor, tau: float = ..., hard: bool = ..., eps: float = ..., dim: int = ...) -> Tensor: ...
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def log_softmax(input: Tensor, dim: Optional[int] = ..., _stacklevel: int = ...,
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dtype: Optional[int] = ...) -> Tensor: ...
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def tanh(input: Any): ...
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def sigmoid(input: Any): ...
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def linear(input: Tensor, weight: Tensor, bias: Optional[Tensor] = ...) -> Tensor: ...
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def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor] = ...) -> Tensor: ...
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def embedding(input: Tensor, weight: Tensor, padding_idx: Optional[int] = ..., max_norm: Optional[float] = ...,
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norm_type: float = ..., scale_grad_by_freq: bool = ..., sparse: bool = ...) -> Tensor: ...
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def embedding_bag(input: Tensor, weight: Tensor, offsets: Optional[Tensor] = ..., max_norm: Optional[float] = ...,
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norm_type: float = ..., scale_grad_by_freq: bool = ..., mode: str = ...,
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sparse: bool = ...) -> Tensor: ...
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def batch_norm(input: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor],
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weight: Optional[Tensor] = ..., bias: Optional[Tensor] = ..., training: bool = ...,
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momentum: float = ..., eps: float = ...) -> Tensor: ...
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def instance_norm(input: Tensor, running_mean: Optional[Tensor] = ..., running_var: Optional[Tensor] = ...,
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weight: Optional[Tensor] = ..., bias: Optional[Tensor] = ..., use_input_stats: bool = ...,
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momentum: float = ..., eps: float = ...) -> Tensor: ...
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def layer_norm(input: Tensor, normalized_shape: List[int], weight: Optional[Tensor] = ..., bias: Optional[Tensor] = ...,
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eps: float = ...) -> Tensor: ...
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def group_norm(input: Tensor, num_groups: int, weight: Optional[Tensor] = ..., bias: Optional[Tensor] = ...,
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eps: float = ...) -> Tensor: ...
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def local_response_norm(input: Tensor, size: int, alpha: float = ..., beta: float = ..., k: float = ...) -> Tensor: ...
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def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: int = ...,
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reduction: str = ..., zero_infinity: bool = ...) -> Tensor: ...
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def nll_loss(input: Tensor, target: Tensor, weight: Optional[Tensor] = ..., size_average: Optional[bool] = ...,
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ignore_index: int = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
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def poisson_nll_loss(input: Tensor, target: Tensor, log_input: bool = ..., full: bool = ...,
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size_average: Optional[bool] = ..., eps: float = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def kl_div(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ..., log_target: bool = ...) -> Tensor: ...
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def cross_entropy(input: Tensor, target: Tensor, weight: Optional[Tensor] = ..., size_average: Optional[bool] = ...,
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ignore_index: int = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
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def binary_cross_entropy(input: Tensor, target: Tensor, weight: Optional[Tensor] = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def binary_cross_entropy_with_logits(input: Tensor, target: Tensor, weight: Optional[Tensor] = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ..., pos_weight: Optional[Tensor] = ...) -> Tensor: ...
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def smooth_l1_loss(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def l1_loss(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def mse_loss(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def margin_ranking_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: float = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def hinge_embedding_loss(input: Tensor, target: Tensor, margin: float = ..., size_average: Optional[bool] = ...,
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reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
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def multilabel_margin_loss(input: Tensor, target: Tensor, size_average: Optional[bool] = ...,
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reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
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def soft_margin_loss(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def multilabel_soft_margin_loss(input: Tensor, target: Tensor, weight: Optional[Tensor] = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def cosine_embedding_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: float = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def multi_margin_loss(input: Tensor, target: Tensor, p: int = ..., margin: float = ..., weight: Optional[Tensor] = ...,
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size_average: Optional[bool] = ..., reduce: Optional[bool] = ...,
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reduction: str = ...) -> Tensor: ...
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def upsample(input: Any, size: Optional[Any] = ..., scale_factor: Optional[Any] = ..., mode: str = ...,
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align_corners: Optional[Any] = ...): ...
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def interpolate(input: Any, size: Optional[Any] = ..., scale_factor: Optional[Any] = ..., mode: str = ...,
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align_corners: Optional[Any] = ...): ...
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def upsample_nearest(input: Any, size: Optional[Any] = ..., scale_factor: Optional[Any] = ...): ...
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def upsample_bilinear(input: Any, size: Optional[Any] = ..., scale_factor: Optional[Any] = ...): ...
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def grid_sample(input: Tensor, grid: Tensor, mode: str = ..., padding_mode: str = ...,
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align_corners: Optional[Any] = ...) -> Tensor: ...
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def affine_grid(theta: Tensor, size: List[int], align_corners: Optional[Any] = ...) -> Tensor: ...
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def pad(input: Tensor, pad: List[int], mode: str = ..., value: float = ...) -> Tensor: ...
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def pairwise_distance(x1: Tensor, x2: Tensor, p: float = ..., eps: float = ..., keepdim: bool = ...) -> Tensor: ...
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def triplet_margin_loss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: float = ..., p: float = ...,
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eps: float = ..., swap: bool = ..., size_average: Optional[bool] = ...,
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reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
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def normalize(input: Tensor, p: float = ..., dim: int = ..., eps: float = ...,
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out: Optional[Tensor] = ...) -> Tensor: ...
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def assert_int_or_pair(arg: Any, arg_name: Any, message: Any) -> None: ...
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def unfold(input: Tensor, kernel_size: _size, dilation: _size = ..., padding: _size = ...,
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stride: _size = ...) -> Tensor: ...
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def fold(input: Tensor, output_size: _size, kernel_size: _size, dilation: _size = ..., padding: _size = ...,
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stride: _size = ...) -> Tensor: ...
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${imported_hints}
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${dispatched_hints}
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