[chore] Copyrights and link fixes

docs/source/_templates/layout.html

@@ -303,6 +303,8 @@
             <li class="list-title"><a href="{{ theme_variables.external_urls['resources'] }}">Resources</a></li>
             <li><a href="{{ theme_variables.external_urls['docs'] }}">Docs</a></li>
             <li><a href="{{ theme_variables.external_urls['github_issues'] }}" target="_blank">Github Issues</a></li>
+            <li><a href="https://opensource.facebook.com/legal/terms">Terms of Use</a></li>
+            <li><a href="https://opensource.facebook.com/legal/privacy">Privacy Policy</a></li>
           </ul>
         </div>
       </div>

docs/source/conf.py

@@ -31,7 +31,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "xFormers"
-copyright = "2021, Facebook AI Research"
+copyright = "Copyright © 2021 Meta Platforms, Inc"
 author = "Facebook AI Research"
 
 # The full version, including alpha/beta/rc tags

stubs/numpy/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import (
     Any,
     Container,
@@ -50,13 +55,9 @@ class ndarray(_ArrayOrScalarCommon[DType, Unpack[Ts]], Iterable, Sized, Containe
         order: Optional[str] = ...,
     ) -> None: ...
     @overload
-    def __getitem__(
-        self: ndarray[DType, A1, A2], key: Literal[0]
-    ) -> ndarray[DType, A2]: ...
+    def __getitem__(self: ndarray[DType, A1, A2], key: Literal[0]) -> ndarray[DType, A2]: ...
     @overload
-    def __getitem__(
-        self: ndarray[DType, A1, A2], key: Literal[1]
-    ) -> ndarray[DType, A1]: ...
+    def __getitem__(self: ndarray[DType, A1, A2], key: Literal[1]) -> ndarray[DType, A1]: ...
     def __setitem__(self, key, value): ...
     @property
     def shape(self) -> Tuple[Unpack[Ts]]: ...
@@ -81,15 +82,11 @@ class ndarray(_ArrayOrScalarCommon[DType, Unpack[Ts]], Iterable, Sized, Containe
 # ===== BEGIN `empty` =====
 # `shape` as tuple, dtype="int64"
 @overload
-def empty(
-    shape: Tuple[Unpack[Ts]], dtype: Literal["int64"]
-) -> ndarray[int64, Unpack[Ts]]: ...
+def empty(shape: Tuple[Unpack[Ts]], dtype: Literal["int64"]) -> ndarray[int64, Unpack[Ts]]: ...
 
 # `shape` as tuple, dtype as e.g. np.float32
 @overload
-def empty(
-    shape: Tuple[Unpack[Ts]], dtype: Type[DType]
-) -> ndarray[DType, Unpack[Ts]]: ...
+def empty(shape: Tuple[Unpack[Ts]], dtype: Type[DType]) -> ndarray[DType, Unpack[Ts]]: ...
 
 # `shape` as integer, dtype as e.g. np.float32
 @overload

stubs/torch/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 import builtins
 from typing import (
     Any,

stubs/torch/autograd/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any, Optional, Sequence, Union
 
 import torch

stubs/torch/autograd/profiler.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 import contextlib
 from typing import Any
 

stubs/torch/cuda/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 def __getattr__(name) -> Any: ...

stubs/torch/fft/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Optional, Tuple, TypeVar, overload
 
 from pyre_extensions import TypeVarTuple, Unpack

stubs/torch/hub.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 download_url_to_file = Any

stubs/torch/linalg/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Optional, Tuple, TypeVar, overload
 
 from pyre_extensions import TypeVarTuple, Unpack
@@ -32,9 +37,7 @@ def qr(
     mode: L["complete"],
     *,
     out: Optional[Tensor] = ...,
-) -> Tuple[
-    Tensor[FloatOrDouble, Unpack[Ts], M, M], Tensor[FloatOrDouble, Unpack[Ts], M, N]
-]: ...
+) -> Tuple[Tensor[FloatOrDouble, Unpack[Ts], M, M], Tensor[FloatOrDouble, Unpack[Ts], M, N]]: ...
 
 # The return type should use k=min(m, n), but we don't have that operator yet.
 # Assume that M <= N for now.
@@ -44,10 +47,6 @@ def qr(
     mode: L["reduced"] = ...,
     *,
     out: Optional[Tensor] = ...,
-) -> Tuple[
-    Tensor[FloatOrDouble, Unpack[Ts], M, M], Tensor[FloatOrDouble, Unpack[Ts], M, N]
-]: ...
+) -> Tuple[Tensor[FloatOrDouble, Unpack[Ts], M, M], Tensor[FloatOrDouble, Unpack[Ts], M, N]]: ...
 @overload
-def norm(
-    A: Tensor[DType, Unpack[Ts], N, M], dim: Tuple[L[-2], L[-1]]
-) -> Tensor[DType, Unpack[Ts]]: ...
+def norm(A: Tensor[DType, Unpack[Ts], N, M], dim: Tuple[L[-2], L[-1]]) -> Tensor[DType, Unpack[Ts]]: ...

stubs/torch/nn/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 import builtins
 from typing import (
     Any,
@@ -42,21 +47,15 @@ class Module:
     training: bool = ...
 
 class LSTMCell(Module, Generic[InputSize, HiddenSize]):
-    def __init__(
-        self, input_size: InputSize, hidden_size: HiddenSize, bias: bool = ...
-    ) -> None: ...
+    def __init__(self, input_size: InputSize, hidden_size: HiddenSize, bias: bool = ...) -> None: ...
     def __call__(
         self,
         input: Tensor[DType, Batch, InputSize],
-        hidden: Tuple[
-            Tensor[DType, Batch, HiddenSize], Tensor[DType, Batch, HiddenSize]
-        ] = ...,
+        hidden: Tuple[Tensor[DType, Batch, HiddenSize], Tensor[DType, Batch, HiddenSize]] = ...,
     ) -> Tuple[Tensor[DType, Batch, HiddenSize], Tensor[DType, Batch, HiddenSize]]: ...
 
 class Linear(Module, Generic[InputSize, OutputSize]):
-    def __init__(
-        self, in_features: InputSize, out_features: OutputSize, bias: bool = ...
-    ) -> None: ...
+    def __init__(self, in_features: InputSize, out_features: OutputSize, bias: bool = ...) -> None: ...
     def __call__(
         self,
         input: Tensor[DType, N, Unpack[Ts], InputSize],
@@ -123,13 +122,7 @@ class ReflectionPad2d(Module, Generic[Padding]):
     def __call__(
         self,
         input: Tensor[DType, Batch, Channels, Height, Width],
-    ) -> Tensor[
-        DType,
-        Batch,
-        Channels,
-        Add[Add[Height, Padding], Padding],
-        Add[Add[Width, Padding], Padding],
-    ]: ...
+    ) -> Tensor[DType, Batch, Channels, Add[Add[Height, Padding], Padding], Add[Add[Width, Padding], Padding],]: ...
 
 class InstanceNorm2d(Generic[Channels]):
     def __init__(self, num_features: Channels, affine: bool = False) -> None: ...
@@ -139,9 +132,7 @@ class InstanceNorm2d(Generic[Channels]):
 
 class LeakyReLU(Module):
     def __init__(self, negative_slope: float = ..., inplace: bool = ...) -> None: ...
-    def __call__(
-        self, input: Tensor[DType, N, Unpack[Ts]]
-    ) -> Tensor[DType, N, Unpack[Ts]]: ...
+    def __call__(self, input: Tensor[DType, N, Unpack[Ts]]) -> Tensor[DType, N, Unpack[Ts]]: ...
 
 class ReLU(Module):
     def __call__(
@@ -155,9 +146,7 @@ class GELU(Module):
 
 class Dropout(Module):
     def __init__(self, p: float, inplace: bool = ...) -> None: ...
-    def __call__(
-        self, input: Tensor[DType, Unpack[Ts]]
-    ) -> Tensor[DType, Unpack[Ts]]: ...
+    def __call__(self, input: Tensor[DType, Unpack[Ts]]) -> Tensor[DType, Unpack[Ts]]: ...
 
 class Embedding(Module, Generic[N, EmbeddingDimension]):
     def __init__(
@@ -194,12 +183,8 @@ class Embedding(Module, Generic[N, EmbeddingDimension]):
         scale_grad_by_freq: bool = False,
         sparse: bool = False,
     ) -> Embedding[N, EmbeddingDimension]: ...
-    def forward(
-        self, x: Tensor[DType, Unpack[Ts]]
-    ) -> Tensor[DType, Unpack[Ts], EmbeddingDimension]: ...
-    def __call__(
-        self, x: Tensor[DType, Unpack[Ts]]
-    ) -> Tensor[DType, Unpack[Ts], EmbeddingDimension]: ...
+    def forward(self, x: Tensor[DType, Unpack[Ts]]) -> Tensor[DType, Unpack[Ts], EmbeddingDimension]: ...
+    def __call__(self, x: Tensor[DType, Unpack[Ts]]) -> Tensor[DType, Unpack[Ts], EmbeddingDimension]: ...
 
 _shape_t = Union[int, List[int], Tuple[Any, ...]]
 
@@ -256,8 +241,6 @@ class ModuleList(Module):
     def __len__(self) -> int: ...
 
 class Parameter(Tensor[DType, Unpack[Ts]]):
-    def __init__(
-        self, data: Tensor[DType, Unpack[Ts]] = ..., requires_grad: builtins.bool = ...
-    ) -> None: ...
+    def __init__(self, data: Tensor[DType, Unpack[Ts]] = ..., requires_grad: builtins.bool = ...) -> None: ...
 
 Sequential: Any = ...

stubs/torch/nn/functional.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Tuple, TypeVar, overload
 
 import torch

stubs/torch/nn/functional/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Tuple, TypeVar, overload
 
 import torch

stubs/torch/nn/init.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Tuple, TypeVar
 
 import torch
@@ -8,17 +13,11 @@ Ts = TypeVarTuple("Ts")
 DType = TypeVar("DType")
 
 def _calculate_fan_in_and_fan_out(tensor: Tensor) -> Tuple[int, int]: ...
-def constant_(
-    tensor: Tensor[DType, Unpack[Ts]], val: float
-) -> Tensor[DType, Unpack[Ts]]: ...
+def constant_(tensor: Tensor[DType, Unpack[Ts]], val: float) -> Tensor[DType, Unpack[Ts]]: ...
 def kaiming_uniform_(
     tensor: Tensor[DType, Unpack[Ts]], a=0, mode="fan_in", nonlinearity="leaky_relu"
 ) -> Tensor[DType, Unpack[Ts]]: ...
-def normal_(
-    tensor: Tensor[DType, Unpack[Ts]], mean: float = ..., std: float = ...
-) -> Tensor[DType, Unpack[Ts]]: ...
-def uniform_(
-    tensor: Tensor[DType, Unpack[Ts]], a: float = ..., b: float = ...
-) -> Tensor[DType, Unpack[Ts]]: ...
+def normal_(tensor: Tensor[DType, Unpack[Ts]], mean: float = ..., std: float = ...) -> Tensor[DType, Unpack[Ts]]: ...
+def uniform_(tensor: Tensor[DType, Unpack[Ts]], a: float = ..., b: float = ...) -> Tensor[DType, Unpack[Ts]]: ...
 def _no_grad_uniform_(tensor: Tensor, a, b): ...
 def xavier_uniform_(tensor: Tensor, gain: float = ...) -> Tensor: ...

stubs/torch/nn/utils/__init__.pyi

@@ -1,7 +1,12 @@
-from typing import Iterable, TypeVar, Union, overload
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
 
-from pyre_extensions import TypeVarTuple, Unpack
-from torch import Tensor, complex64
+from typing import Iterable, TypeVar, Union
+
+from pyre_extensions import TypeVarTuple
+from torch import Tensor
 
 DType = TypeVar("DType")
 

stubs/torch/onnx.pyi

@@ -0,0 +1,4 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.

stubs/torch/ops.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 def __getattr__(name) -> Any: ...

stubs/torch/optim/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 def __getattr__(name) -> Any: ...

stubs/torch/profiler/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 def __getattr__(name) -> Any: ...

stubs/torch/random/__init__.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 def initial_seed() -> int: ...

stubs/torch/sparse/__init__.pyi

@@ -1,6 +1,10 @@
-from typing import Optional, Type, TypeVar, overload
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional, TypeVar, overload
 
-from numpy import ndarray
 from pyre_extensions import TypeVarTuple, Unpack
 from torch import Tensor
 from typing_extensions import Literal as L
@@ -12,10 +16,6 @@ DType2 = TypeVar("DType2")
 
 Ts = TypeVarTuple("Ts")
 @overload
-def softmax(
-    input: Tensor[DType, Unpack[Ts]], dim: int, dtype: Optional[DType2]
-) -> Tensor[DType2, Unpack[Ts]]: ...
+def softmax(input: Tensor[DType, Unpack[Ts]], dim: int, dtype: Optional[DType2]) -> Tensor[DType2, Unpack[Ts]]: ...
 @overload
-def softmax(
-    input: Tensor[DType, Unpack[Ts]], dim: int, dtype: Optional[DType] = ...
-) -> Tensor[DType, Unpack[Ts]]: ...
+def softmax(input: Tensor[DType, Unpack[Ts]], dim: int, dtype: Optional[DType] = ...) -> Tensor[DType, Unpack[Ts]]: ...

stubs/torch/utils/data.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 DataLoader = Any

stubs/torch/utils/model_zoo.pyi

@@ -1,3 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
 from typing import Any
 
 _download_url_to_file = Any