From 6263c378f4ea2842631f6debfc171541de190328 Mon Sep 17 00:00:00 2001 From: guest400123064 Date: Thu, 10 Oct 2024 23:36:03 -0400 Subject: [PATCH] include landing page to docs --- .github/workflows/docs.yml | 3 +- README.md | 2 +- docs/ezgatr.html | 93 +++++++++++++++++++++++- docs/ezgatr/interfaces.html | 1 - docs/ezgatr/interfaces/plane.html | 1 - docs/ezgatr/interfaces/point.html | 1 - docs/ezgatr/interfaces/pseudoscalar.html | 1 - docs/ezgatr/interfaces/reflection.html | 1 - docs/ezgatr/interfaces/rotation.html | 1 - docs/ezgatr/interfaces/scalar.html | 1 - docs/ezgatr/interfaces/translation.html | 1 - docs/ezgatr/nets.html | 1 - docs/ezgatr/nets/mv_only_gatr.html | 1 - docs/ezgatr/nn.html | 1 - docs/ezgatr/nn/functional.html | 1 - docs/ezgatr/nn/modules.html | 1 - docs/ezgatr/utils.html | 1 - docs/ezgatr/utils/debug.html | 1 - docs/ezgatr/utils/logger.html | 1 - docs/search.js | 2 +- src/ezgatr/__init__.py | 3 + 21 files changed, 98 insertions(+), 21 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index f809fa9..af37de8 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -33,8 +33,7 @@ jobs: pdoc src/ezgatr \ -o docs \ -t docs/theme \ - --docformat numpy \ - --logo "https://raw.githubusercontent.com/Guest400123064/ezgatr/refs/heads/main/docs/images/ezgatr_logo.png" + --docformat numpy - uses: actions/upload-pages-artifact@v3 with: diff --git a/README.md b/README.md index c888b51..73ba9c2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@

- +

Geometric Algebra Transformer Made Easy diff --git a/docs/ezgatr.html b/docs/ezgatr.html index 567a960..3a29283 100644 --- a/docs/ezgatr.html +++ b/docs/ezgatr.html @@ -17,11 +17,19 @@
- +

Contents

+ +

Submodules

    @@ -45,8 +53,91 @@

    Submodules

    ezgatr

    +
    +

    + +

    +

    + Geometric Algebra Transformer Made Easy +

    + + +

    License: MIT +Python 3.10 +Ruff +Website

    + +

    What is EzGATr?

    + +

    EzGATr (Easy Geometric Algebra Transformer) intends to be a simple-to-use and lightweight Python library for building 3D Geometric Algebra Transformers (GATr). It is a collection of operators, modules, utilities, etc. build on top of PyTorch. In addition, EzGATr also seeks to bridge the gap between the mathematical formulations and corresponding implementations through extensive documentation and explanations to facilitate learning and potential future optimizations.

    + +

    Installation

    + +

    EzGATr is currently in development and not yet available on PyPI. To install it, you can clone the repository and install it using pip. Use the -e flag to install it in editable mode for quick changes.

    + +
    +
    $ git clone https://github.com/Guest400123064/ezgatr.git
+$ cd ezgatr
+$ pip install -e .
+
    +
    + +

    Usage

    + +

    The quick start example shown below demonstrates how to use EzGATr to build a equivariant network with one input/output channel (e.g., a point cloud) and four hidden channels. The mock input data contains a batch of eight samples, each with 256 3D objects embedded as multi-vectors.

    + +
    +
    import torch
+import torch.nn as nn
+
+from ezgatr.nn import EquiLinear
+from ezgatr.nn.functional import scaler_gated_gelu
+
+
+class SimpleNet(nn.Module):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.fc1 = EquiLinear(1, 4)
+        self.fc2 = EquiLinear(4, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = scaler_gated_gelu(x)
+        x = self.fc2(x)
+        return x
+
+
+net = SimpleNet()
+in_ = torch.randn(8, 256, 1, 16)
+out = net(in_)
+
    +
    + +

    One can refer to this example for how to build a full-fledged GATr model with EzGATr, involving equivariant geometric attention and geometric MLP.

    + +

    API References

    + +

    The complete API references for EzGATr can be found here. TL;DR, the package is organized as follows:

    + +
      +
    • ezgatr.nn: Contains the core modules and layers for building GATr models. It is organized similarly to PyTorch's torch.nn package, where the functional submodule contains lower-level operators and transformations.
      • +
    • ezgatr.interfaces: Contains the utility functions that help encode and decode 3D objects to and from multi-vectors.
      • +
    • ezgatr.nets: Contains off-of-the-shelf networks built with EzGATr building blocks. It can also be used as references for building custom networks with EzGATr.
      • +
    + +

    License

    + +

    EzGATr is distributed under the terms of the MIT license.

    +
    + + + + 
    1r"""
+2.. include:: ../../README.md
+3"""
+
    diff --git a/docs/ezgatr/interfaces.html b/docs/ezgatr/interfaces.html index 5d0bced..bf7939e 100644 --- a/docs/ezgatr/interfaces.html +++ b/docs/ezgatr/interfaces.html @@ -22,7 +22,6 @@  ezgatr - diff --git a/docs/ezgatr/interfaces/plane.html b/docs/ezgatr/interfaces/plane.html index 8054b51..8cb01c4 100644 --- a/docs/ezgatr/interfaces/plane.html +++ b/docs/ezgatr/interfaces/plane.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/point.html b/docs/ezgatr/interfaces/point.html index efc430e..d0499c0 100644 --- a/docs/ezgatr/interfaces/point.html +++ b/docs/ezgatr/interfaces/point.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/pseudoscalar.html b/docs/ezgatr/interfaces/pseudoscalar.html index 6aa1cfe..d3da9bf 100644 --- a/docs/ezgatr/interfaces/pseudoscalar.html +++ b/docs/ezgatr/interfaces/pseudoscalar.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/reflection.html b/docs/ezgatr/interfaces/reflection.html index 0053d01..37277c1 100644 --- a/docs/ezgatr/interfaces/reflection.html +++ b/docs/ezgatr/interfaces/reflection.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/rotation.html b/docs/ezgatr/interfaces/rotation.html index 2f66427..33092f3 100644 --- a/docs/ezgatr/interfaces/rotation.html +++ b/docs/ezgatr/interfaces/rotation.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/scalar.html b/docs/ezgatr/interfaces/scalar.html index 4eb16bc..bdf2e60 100644 --- a/docs/ezgatr/interfaces/scalar.html +++ b/docs/ezgatr/interfaces/scalar.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/interfaces/translation.html b/docs/ezgatr/interfaces/translation.html index 24b49a9..622345e 100644 --- a/docs/ezgatr/interfaces/translation.html +++ b/docs/ezgatr/interfaces/translation.html @@ -22,7 +22,6 @@  ezgatr.interfaces - diff --git a/docs/ezgatr/nets.html b/docs/ezgatr/nets.html index 6ed10c1..75300bf 100644 --- a/docs/ezgatr/nets.html +++ b/docs/ezgatr/nets.html @@ -22,7 +22,6 @@  ezgatr - diff --git a/docs/ezgatr/nets/mv_only_gatr.html b/docs/ezgatr/nets/mv_only_gatr.html index 77e7079..24d3452 100644 --- a/docs/ezgatr/nets/mv_only_gatr.html +++ b/docs/ezgatr/nets/mv_only_gatr.html @@ -22,7 +22,6 @@  ezgatr.nets - diff --git a/docs/ezgatr/nn.html b/docs/ezgatr/nn.html index 3e7230f..f1a7967 100644 --- a/docs/ezgatr/nn.html +++ b/docs/ezgatr/nn.html @@ -22,7 +22,6 @@  ezgatr - diff --git a/docs/ezgatr/nn/functional.html b/docs/ezgatr/nn/functional.html index ae43fb5..46dac5c 100644 --- a/docs/ezgatr/nn/functional.html +++ b/docs/ezgatr/nn/functional.html @@ -22,7 +22,6 @@  ezgatr.nn - diff --git a/docs/ezgatr/nn/modules.html b/docs/ezgatr/nn/modules.html index c5a77b6..b1eccfc 100644 --- a/docs/ezgatr/nn/modules.html +++ b/docs/ezgatr/nn/modules.html @@ -22,7 +22,6 @@  ezgatr.nn - diff --git a/docs/ezgatr/utils.html b/docs/ezgatr/utils.html index a54ae21..f727613 100644 --- a/docs/ezgatr/utils.html +++ b/docs/ezgatr/utils.html @@ -22,7 +22,6 @@  ezgatr - diff --git a/docs/ezgatr/utils/debug.html b/docs/ezgatr/utils/debug.html index 210e311..a259d75 100644 --- a/docs/ezgatr/utils/debug.html +++ b/docs/ezgatr/utils/debug.html @@ -22,7 +22,6 @@  ezgatr.utils - diff --git a/docs/ezgatr/utils/logger.html b/docs/ezgatr/utils/logger.html index e298255..c2c689f 100644 --- a/docs/ezgatr/utils/logger.html +++ b/docs/ezgatr/utils/logger.html @@ -22,7 +22,6 @@  ezgatr.utils - 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    Encode planes into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • normals (torch.Tensor):\nNormal vectors of the planes with shape (..., 3).
      • \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
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    Extract normal and translation vectors from multi-vectors with PGA.

    \n\n

    Note that the translation vectors associated with each plane is not unique.\nIn this case, we use the PGA convention that e_0 coefficients are the\ndistances from the origin to the plane along the normal direction to determine\nthe translation vectors.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Normal vectors and end positions of the translation vectors;\ntensors of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.interfaces.point": {"fullname": "ezgatr.interfaces.point", "modulename": "ezgatr.interfaces.point", "kind": "module", "doc": "

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    Encode 3D points into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • points (torch.Tensor):\n3D points with xyz coordinates; tensor of shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
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    Extract 3D points from multi-vectors with PGA.

    \n\n

    One needs to divide the coordinates by the homogeneous coordinate to obtain\nthe 3D points. In this case, to prevent numerical instability, we set a threshold\nto the homogeneous coordinate, so that the division will only be performed when\nthe absolute value of the homogeneous coordinate is above the threshold.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    • eps (float, default to 1e-6):\nMinimum value of the additional, un-physical component. Necessary to avoid\nexploding values or NaNs when this un-physical component of the homogeneous\ncoordinates becomes too close to zero.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: 3D points with xyz coordinates; tensor of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor, eps: float = 1e-06) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.pseudoscalar": {"fullname": "ezgatr.interfaces.pseudoscalar", "modulename": "ezgatr.interfaces.pseudoscalar", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.pseudoscalar.encode_pga": {"fullname": "ezgatr.interfaces.pseudoscalar.encode_pga", "modulename": "ezgatr.interfaces.pseudoscalar", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode scalars into the pseudoscalar dimension of multi-vectors with PGA.

    \n\n

    This function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.

    \n\n
    Parameters
    \n\n
      \n
    • scalars (torch.Tensor):\nScalars with shape (..., 1).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(pseudoscalars: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.pseudoscalar.decode_pga": {"fullname": "ezgatr.interfaces.pseudoscalar.decode_pga", "modulename": "ezgatr.interfaces.pseudoscalar", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract the pseudoscalar values from multi-vectors with PGA.

    \n\n

    This function do not automatically squeeze the last dimension when returning\nthe scalar tensor.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Scalars with shape (..., 1).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.reflection": {"fullname": "ezgatr.interfaces.reflection", "modulename": "ezgatr.interfaces.reflection", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.reflection.encode_pga": {"fullname": "ezgatr.interfaces.reflection.encode_pga", "modulename": "ezgatr.interfaces.reflection", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode reflection into multi-vectors with PGA.

    \n\n

    Plane serves as both the geometric object and the reflection operation in\nPGA. Therefore, we encode reflection over a plane as a plane itself.

    \n\n
    Parameters
    \n\n
      \n
    • normals (torch.Tensor):\nNormal vectors of the reflection planes with shape (..., 3).
      • \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(normals: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.reflection.decode_pga": {"fullname": "ezgatr.interfaces.reflection.decode_pga", "modulename": "ezgatr.interfaces.reflection", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract normal and position vectors of the reflection plane with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Normal vectors and end positions of the translation vectors;\ntensors of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.interfaces.rotation": {"fullname": "ezgatr.interfaces.rotation", "modulename": "ezgatr.interfaces.rotation", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.rotation.encode_pga": {"fullname": "ezgatr.interfaces.rotation.encode_pga", "modulename": "ezgatr.interfaces.rotation", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode 3D rotation (as quaternions) into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • quaternions (torch.Tensor with shape (..., 4)):\nQuaternions in ijkw order and Hamilton convention (ijk = -1)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor with shape (..., 16): Embedding into multivector.
      • \n
    \n", "signature": "(quaternions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.rotation.decode_pga": {"fullname": "ezgatr.interfaces.rotation.decode_pga", "modulename": "ezgatr.interfaces.rotation", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract quaternions from multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor with shape (..., 16)):\nMultivector.
      • \n
    • normalize (bool, default to False):\nWhether to normalize the quaternion to unit norm.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor with shape (..., 4): Quaternions in ijkw order and Hamilton convention (ijk = -1)
      • \n
    \n", "signature": "(mvs: torch.Tensor, normalize: bool = False) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.scalar": {"fullname": "ezgatr.interfaces.scalar", "modulename": "ezgatr.interfaces.scalar", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.scalar.encode_pga": {"fullname": "ezgatr.interfaces.scalar.encode_pga", "modulename": "ezgatr.interfaces.scalar", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode scalars into multi-vectors with PGA.

    \n\n

    This function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.

    \n\n
    Parameters
    \n\n
      \n
    • scalars (torch.Tensor):\nScalars with shape (..., 1).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(scalars: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.scalar.decode_pga": {"fullname": "ezgatr.interfaces.scalar.decode_pga", "modulename": "ezgatr.interfaces.scalar", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract scalars from multi-vectors with PGA.

    \n\n

    This function do not automatically squeeze the last dimension when returning\nthe scalar tensor.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Scalars with shape (..., 1).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.translation": {"fullname": "ezgatr.interfaces.translation", "modulename": "ezgatr.interfaces.translation", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.translation.encode_pga": {"fullname": "ezgatr.interfaces.translation.encode_pga", "modulename": "ezgatr.interfaces.translation", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode translations into multi-vectors with PGA.

    \n\n

    We use the convention that translations are represented by the xyz coordinates\nof the translation vectors in 3D Euclidean space, i.e., end positions.

    \n\n
    Parameters
    \n\n
      \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(positions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.translation.decode_pga": {"fullname": "ezgatr.interfaces.translation.decode_pga", "modulename": "ezgatr.interfaces.translation", "qualname": "decode_pga", "kind": "function", "doc": "

    \n", "signature": "(_: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets": {"fullname": "ezgatr.nets", "modulename": "ezgatr.nets", "kind": "module", "doc": "

    \n"}, "ezgatr.nets.mv_only_gatr": {"fullname": "ezgatr.nets.mv_only_gatr", "modulename": "ezgatr.nets.mv_only_gatr", "kind": "module", "doc": "

    \n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig", "kind": "class", "doc": "

    Configuration class for the MVOnlyGATr model.

    \n\n
    Parameters
    \n\n
      \n
    • num_layers (int, default to 4):\nNumber of GATr blocks in the network.
      • \n
    • size_context (int, default to 2048):\nNumber of elements, e.g., number of points in a point cloud,\nin the input sequence. This argument is not actually used in\nthe model, but is kept for compatibility.
      • \n
    • size_channels_in (int, default to 1):\nNumber of input channels.
      • \n
    • size_channels_out (int, default to 1):\nNumber of output channels.
      • \n
    • size_channels_hidden (int, default to 32):\nNumber of hidden representation channels throughout the network, i.e.,\nthe input/output number of channels of the next layer, block, or module.
      • \n
    • size_channels_intermediate (int, default to 32):\nNumber of intermediate channels for the geometric bilinear operation.\nMust be even. This intermediate size should not be confused with the size\nof hidden representations throughout the network. It only refers to the\nhidden sizes used for the equivariant join and geometric product operations.
      • \n
    • norm_eps (float, optional):\nSmall value to prevent division by zero in the normalization layer.
      • \n
    • norm_channelwise_rescale (bool, default to True):\nApply learnable channel-wise rescaling weights to the normalized multi-vector\ninputs. Initialized to ones if set to True.
      • \n
    • gelu_approximate (str, default to \"tanh\"):\nApproximation method for the GeLU function. Default to \"tanh\".
      • \n
    \n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.__init__", "kind": "function", "doc": "

    \n", "signature": "(\tnum_layers: int = 4,\tsize_context: int = 2048,\tsize_channels_in: int = 1,\tsize_channels_out: int = 1,\tsize_channels_hidden: int = 32,\tsize_channels_intermediate: int = 32,\tattn_num_heads: int = 4,\tattn_kinds: dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None] = <factory>,\tattn_dropout_p: float = 0.0,\tattn_is_causal: bool = True,\tattn_scale: float | None = None,\tnorm_eps: float | None = None,\tnorm_channelwise_rescale: bool = True,\tgelu_approximate: Literal['none', 'tanh'] = 'tanh')"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.num_layers", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "4"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_context", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "2048"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_in", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "1"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_out", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "1"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_hidden", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "32"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_intermediate": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_intermediate", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_intermediate", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "32"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_num_heads": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_num_heads", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_num_heads", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "4"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_kinds": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_kinds", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_kinds", "kind": "variable", "doc": "

    \n", "annotation": ": dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_dropout_p": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_dropout_p", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_dropout_p", "kind": "variable", "doc": "

    \n", "annotation": ": float", "default_value": "0.0"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_is_causal": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_is_causal", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_is_causal", "kind": "variable", "doc": "

    \n", "annotation": ": bool", "default_value": "True"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_scale": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_scale", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_scale", "kind": "variable", "doc": "

    \n", "annotation": ": float | None", "default_value": "None"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_eps": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_eps", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.norm_eps", "kind": "variable", "doc": "

    \n", "annotation": ": float | None", "default_value": "None"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_channelwise_rescale": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_channelwise_rescale", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.norm_channelwise_rescale", "kind": "variable", "doc": "

    \n", "annotation": ": bool", "default_value": "True"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.gelu_approximate": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.gelu_approximate", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.gelu_approximate", "kind": "variable", "doc": "

    \n", "annotation": ": Literal['none', 'tanh']", "default_value": "'tanh'"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding", "kind": "class", "doc": "

    Embedding layer to project input number of channels to hidden channels.

    \n\n

    This layer corresponds to the very first equivariant linear layer of the\noriginal design mentioned in the GATr paper.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.embedding", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear", "kind": "class", "doc": "

    Implements the geometric bilinear sub-layer of the geometric MLP.

    \n\n

    Geometric bilinear operation consists of geometric product and equivariant\njoin operations. The results of two operations are concatenated along the\nhidden channel axis and passed through a final equivariant linear projection\nbefore being passed to the next layer, block, or module.

    \n\n

    In both geometric product and equivariant join operations, the input\nmulti-vectors are first projected to a hidden space with the same number of\nchannels, i.e., left and right. Then, the results of each operation are\nderived from the interaction of left and right hidden representations, each\nwith half number of size_channels_intermediate.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_bil", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_out", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.forward", "kind": "function", "doc": "

    Forward pass of the geometric bilinear sub-layer.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • reference (torch.Tensor, optional):\nReference tensor for the equivariant join operation.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP", "kind": "class", "doc": "

    Geometric MLP block without scaler channels.

    \n\n

    Here we fix the structure of the MLP block to be a single equivariant linear\nprojection followed by a gated GELU activation function. In addition, the\nequivariant normalization layer can be configured to be learnable, so the\nnormalization layer needs to be included in the block instead of being shared\nacross the network.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.layer_norm", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.equi_bil", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.proj_out", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.forward", "kind": "function", "doc": "

    Forward pass of the geometric MLP block.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • reference (torch.Tensor, optional):\nReference tensor for the equivariant join operation.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention", "kind": "class", "doc": "

    Geometric attention block without scaler channels.

    \n\n

    The GATr attention calculation is slightly different from the original\ntransformers implementation in that each head has the sample number of\nchannels as the input tensor, instead of dividing into smaller chunks.\nIn this case, the final output linear transformation maps from\nsize_channels_hidden * attn_num_heads to size_channels_hidden.

    \n\n

    One additional note here is that the attn_mix parameter is a dictionary\nof learnable weighting parameter LOGITS for each attention kind.\nThey will be exponentiated before being used in the attention calculation.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.layer_norm", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.attn_mix", "kind": "variable", "doc": "

    \n", "annotation": ": dict[str, torch.Tensor]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_qkv", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_out", "kind": "variable", "doc": "

    \n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.forward", "kind": "function", "doc": "

    Forward pass of the geometric attention block.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • attn_mask (torch.Tensor, optional):\nAttention mask tensor for the attention operation. Usually\nused if any specific attention constraints are needed within\na single sequence, such as padding mask or for discriminating\ndifferent subsequences.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock", "kind": "class", "doc": "

    GATr block without scaler channels.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    • layer_id (int):\nIndex of the current block in the network.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig, layer_id: int)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.layer_id", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.mlp", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.attn", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel", "kind": "class", "doc": "

    Multi-Vector only GATr model.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.embedding", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.blocks", "kind": "variable", "doc": "

    \n", "annotation": ": torch.nn.modules.container.ModuleList"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.head", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn": {"fullname": "ezgatr.nn", "modulename": "ezgatr.nn", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.functional": {"fullname": "ezgatr.nn.functional", "modulename": "ezgatr.nn.functional", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.functional.compute_qk_for_daa": {"fullname": "ezgatr.nn.functional.compute_qk_for_daa", "modulename": "ezgatr.nn.functional", "qualname": "compute_qk_for_daa", "kind": "function", "doc": "

    Compute the query and key tensors for the distance-aware attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (torch.Tensor):\nQuery tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • key (torch.Tensor):\nKey tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • eps (float, optional):\nSmall value to avoid division by zero used in the linear square normalizer.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Query and key tensors for the equivariant distance-aware attention.\nBlade dimensions are NOT flattened.
      • \n
    \n", "signature": "(\tquery: torch.Tensor,\tkey: torch.Tensor,\teps: float | None = None) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.nn.functional.compute_qk_for_ipa": {"fullname": "ezgatr.nn.functional.compute_qk_for_ipa", "modulename": "ezgatr.nn.functional", "qualname": "compute_qk_for_ipa", "kind": "function", "doc": "

    Compute the query and key tensors for the inner product attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (torch.Tensor):\nQuery tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • key (torch.Tensor):\nKey tensor with shape (B, H, T, qk_channels, 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Query and key tensors for the equivariant inner product attention.\nBlade dimensions are NOT flattened.
      • \n
    \n", "signature": "(\tquery: torch.Tensor,\tkey: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.nn.functional.equi_dual": {"fullname": "ezgatr.nn.functional.equi_dual", "modulename": "ezgatr.nn.functional", "qualname": "equi_dual", "kind": "function", "doc": "

    Compute the dual of the input multi-vector.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Corresponding dual with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_geometric_attention": {"fullname": "ezgatr.nn.functional.equi_geometric_attention", "modulename": "ezgatr.nn.functional", "qualname": "equi_geometric_attention", "kind": "function", "doc": "

    Equivariant geometric attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (GeometricQKVType):\nMulti-vector query tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • key (GeometricQKVType):\nMulti-vector key tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • value (GeometricQKVType):\nMulti-vector value tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • kinds (dict[GeometricAttnKindType, dict[str, Any] | None]):\nKinds of similarity measures to consider in the attention calculation\nalong with additional configuration/parameters sent to the corresponding\nquery-key generating function. One should supply a dictionary mapping\nfrom the kind to parameters in addition to query and key tensors. Use\nNone to denote no additional parameters supplied. Available options:\n
        \n
      • \"ipa\": Inner product attention
        • \n
      • \"daa\": Distance-aware attention
        • \n
      • \n
    • weight (list[torch.Tensor | float], optional):\nWeight tensor for the attention kinds. If not provided, the weights are\nset to 1.0 for all kinds to represent equal importance. Note that the\nweight tensors are NOT applied to the scalar inputs (if provided). If\nscalar channel tensors are supplied, weights are fixed to 1.0.
      • \n
    • attn_mask (torch.Tensor, optional):\nAttention mask tensor.
      • \n
    • dropout_p (float, default to 0.0):\nDropout probability.
      • \n
    • is_causal (bool, default to False):\nWhether to apply causal masking.
      • \n
    • scale (float, optional):\nScaling factor for the attention scores, overwriting the default scale\ndetermined by the hidden dimension.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output tensor with shape (B, H, T, v_channels, 16).
      • \n
    \n", "signature": "(\tquery: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tkey: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tvalue: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tkinds: dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None],\tweight: list[torch.Tensor | float] | None = None,\tattn_mask: torch.Tensor | None = None,\tdropout_p: float = 0.0,\tis_causal: bool = False,\tscale: float | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_join": {"fullname": "ezgatr.nn.functional.equi_join", "modulename": "ezgatr.nn.functional", "qualname": "equi_join", "kind": "function", "doc": "

    Compute the equivariant join of two multi-vectors given the reference.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • reference (torch.Tensor, optional):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Equivariant join result multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(\tx: torch.Tensor,\ty: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_linear": {"fullname": "ezgatr.nn.functional.equi_linear", "modulename": "ezgatr.nn.functional", "qualname": "equi_linear", "kind": "function", "doc": "

    Perform Pin-equivariant linear map defined by weight on input x.

    \n\n

    One way to think of the equivariant linear map is a channel-wise\n\"map-reduce\", where the same weight (of one neuron) are applied to\nall channels of the input multi-vector and the results are summed up\nalong the basis/blade axis. In other words, the map is a channel-mixing\noperation. Using a parallel analogy with a regular nn.Linear layer,\neach channel of a input multi-vector corresponds to a \"feature value\"\nof a simple hidden representation, and the number of output channels\nis the number of neurons in the hidden layer.

    \n\n

    Within each channel, similar to the geometric product implementation,\nthe linear operation starts with a matrix multiplication between the\n(weighted, if normalized) 16-by-16 computation graph, i.e., the basis,\nand the input multi-vector to take into account the effect that blades\ncontaining e_0 will be \"downgraded\" to a lower grade after the map.\nAgain, a map from \"source-to-destination\" style. Note that we may want\nto optimize this operation as the basis is pretty sparse.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor with shape (..., in_channels, 16)):\nInput multivector. Batch dimensions must be broadcastable between\nx and weight.
      • \n
    • weight (torch.Tensor with shape (out_channels, in_channels, 9)):\nCoefficients for the 9 basis elements. Batch dimensions must be\nbroadcastable between x and weight.
      • \n
    • bias (torch.Tensor with shape (out_channels,)):\nBias for the linear map. The bias values are only added to the\nscalar blades (i.e., index position 0) of each output channel.
      • \n
    • normalize_basis (bool):\nWhether to normalize the basis elements according to\nthe number of \"inflow paths\" of each blade.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output with shape (..., out_channels, 16).
      • \n
    \n", "signature": "(\tx: torch.Tensor,\tweight: torch.Tensor,\tbias: torch.Tensor | None = None,\tnormalize_basis: bool = True) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_rms_norm": {"fullname": "ezgatr.nn.functional.equi_rms_norm", "modulename": "ezgatr.nn.functional", "qualname": "equi_rms_norm", "kind": "function", "doc": "

    Compute PGA inner-induced RMS norm of multi-vectors.

    \n\n

    Although the original GATr paper 1 named the normalization operation\nas E(3)-equivariant LayerNorm, we find the actual implementation more\nsimilar to RMSNorm 2 by substituting the scalar squaring with the inner\nproduct of multi-vectors, i.e., PGA inner-induced RMSNorm.

    \n\n

    We find the adaptation more intuitive by viewing each channel of the input\nmulti-vector as an \"imaginary number\", so that the inner product of the\nmulti-vector with itself is the squared \"modulus\". And, in turn, instead of\nthinking the input tensor as channels-of-multi-vectors, we can think of it\nas feature-vector-of-imaginary-numbers. And the normalization operation is\nthe same as the RMSNorm operation on the scalar-valued feature vectors.

    \n\n

    NOTE: THIS DESIGN IS PROBABLY QUESTIONABLE. The inner product do not\nconsider coefficients associated with blades containing e_0 while\nthe scaling is also applied to these blades.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nInput multi-vectors with shape (..., n_channels, 16).
      • \n
    • weight (torch.Tensor, optional):\nweight for re-scaling the normalized input. It can be both\nstatic or learnable, depending on how weight are initialized\noutside of the function.
      • \n
    • eps (float, optional):\nSmall value to prevent division by zero.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Normalized input with shape (..., n_channels, 16).
      • \n
    \n\n
    Reference
    \n\n
    \n
    \n
      \n
    1. \n

      \"Root Mean Square Layer Normalization\", Biao Zhang and Rico Sennrich,\n ↩

      \n
      2. \n\n
    2. \n

      \"Geometric Algebra Transformer\", Johann Brehmer, et al.,\n ↩

      \n
      3. \n
    \n
    \n", "signature": "(\tx: torch.Tensor,\tweight: torch.Tensor | None = None,\teps: float | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.geometric_product": {"fullname": "ezgatr.nn.functional.geometric_product", "modulename": "ezgatr.nn.functional", "qualname": "geometric_product", "kind": "function", "doc": "

    Geometric product between two batches of multi-vectors.

    \n\n

    The input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels.\nWhen channel dimensions are present, the geometric product is calculated\nchannel-wise (and batch-wise). For instance, the first channel of x[0]\nis multiplied with the first channel of y[0], and so on. No channel-mixing\nhere.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.inner_product": {"fullname": "ezgatr.nn.functional.inner_product", "modulename": "ezgatr.nn.functional", "qualname": "inner_product", "kind": "function", "doc": "

    Compute the PGA inner product between two multi-vectors.

    \n\n

    Similarly, the PGA inner product is calculated channel-wise (and batch-wise).\nNo channel-mixing here.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Inner product results of the multi-vectors with shape (..., 1).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.outer_product": {"fullname": "ezgatr.nn.functional.outer_product", "modulename": "ezgatr.nn.functional", "qualname": "outer_product", "kind": "function", "doc": "

    Outer product between two batches of multi-vectors.

    \n\n

    The input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels. When\nchannel dimensions are present, the outer product is calculated channel-wise\n(and batch-wise). For instance, the first channel of x[0] is multiplied\nwith the first channel of y[0], and so on. No channel-mixing here.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.scaler_gated_gelu": {"fullname": "ezgatr.nn.functional.scaler_gated_gelu", "modulename": "ezgatr.nn.functional", "qualname": "scaler_gated_gelu", "kind": "function", "doc": "

    Compute scaler-gated GeLU activation function.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nInput batch of multi-vectors.
      • \n
    • approximate (Literal[\"none\", \"tanh\"], default to \"tanh\"):\nApproximation method for the GeLU function. Default to \"tanh\".
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output batch of multi-vectors gated by scalar blade.
      • \n
    \n", "signature": "(\tx: torch.Tensor,\tapproximate: Literal['none', 'tanh'] = 'tanh') -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules": {"fullname": "ezgatr.nn.modules", "modulename": "ezgatr.nn.modules", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.modules.EquiLinear": {"fullname": "ezgatr.nn.modules.EquiLinear", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear", "kind": "class", "doc": "

    Pin(3, 0, 1)-equivariant linear map.

    \n\n
    Parameters
    \n\n
      \n
    • in_channels (int):\nNumber of input channels.
      • \n
    • out_channels (int):\nNumber of output channels.
      • \n
    • normalize_basis (bool):\nWhether to normalize the basis elements according to the number of\n\"inflow paths\" for each blade.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nn.modules.EquiLinear.__init__": {"fullname": "ezgatr.nn.modules.EquiLinear.__init__", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(\tin_channels: int,\tout_channels: int,\tbias: bool = True,\tnormalize_basis: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiLinear.in_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.in_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.out_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.out_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.out_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.normalize_basis": {"fullname": "ezgatr.nn.modules.EquiLinear.normalize_basis", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.normalize_basis", "kind": "variable", "doc": "

    \n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiLinear.weight": {"fullname": "ezgatr.nn.modules.EquiLinear.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.weight", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor"}, "ezgatr.nn.modules.EquiLinear.bias": {"fullname": "ezgatr.nn.modules.EquiLinear.bias", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.bias", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiLinear.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiLinear.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.reset_parameters", "kind": "function", "doc": "

    \n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.forward": {"fullname": "ezgatr.nn.modules.EquiLinear.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.extra_repr": {"fullname": "ezgatr.nn.modules.EquiLinear.extra_repr", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.extra_repr", "kind": "function", "doc": "

    Set the extra representation of the module.

    \n\n

    To print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.

    \n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm": {"fullname": "ezgatr.nn.modules.EquiRMSNorm", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm", "kind": "class", "doc": "

    Applies PGA inner-induced RMS norm to a batch of multi-vectors.

    \n\n
    Parameters
    \n\n
      \n
    • in_channels (int):\nNumber of input multi-vector channels.
      • \n
    • eps (float, optional):\nSmall value to prevent division by zero.
      • \n
    • channelwise_rescale (bool, default to True):\nApply learnable channel-wise rescaling weights to the normalized\nmulti-vector inputs. Initialized to ones.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nn.modules.EquiRMSNorm.__init__": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.__init__", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(\tin_channels: int,\teps: float | None = None,\tchannelwise_rescale: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiRMSNorm.in_channels": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.in_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiRMSNorm.eps": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.eps", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.eps", "kind": "variable", "doc": "

    \n", "annotation": ": float | None"}, "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.channelwise_rescale", "kind": "variable", "doc": "

    \n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiRMSNorm.weight": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.weight", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiRMSNorm.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.reset_parameters", "kind": "function", "doc": "

    \n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.forward": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.extra_repr": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.extra_repr", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.extra_repr", "kind": "function", "doc": "

    Set the extra representation of the module.

    \n\n

    To print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.

    \n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.utils": {"fullname": "ezgatr.utils", "modulename": "ezgatr.utils", "kind": "module", "doc": "

    \n"}, "ezgatr.utils.debug": {"fullname": "ezgatr.utils.debug", "modulename": "ezgatr.utils.debug", "kind": "module", "doc": "

    \n"}, "ezgatr.utils.debug.time_cuda_exec": {"fullname": "ezgatr.utils.debug.time_cuda_exec", "modulename": "ezgatr.utils.debug", "qualname": "time_cuda_exec", "kind": "function", "doc": "

    Time the execution of a function involving CUDA.

    \n", "signature": "(n_exec: int = 1000, report_avg: bool = True):", "funcdef": "def"}, "ezgatr.utils.logger": {"fullname": "ezgatr.utils.logger", "modulename": "ezgatr.utils.logger", "kind": "module", "doc": "

    \n"}}, "docInfo": {"ezgatr": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 75}, "ezgatr.interfaces.plane.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 114}, "ezgatr.interfaces.point": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.point.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 61}, "ezgatr.interfaces.point.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 155}, "ezgatr.interfaces.pseudoscalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.pseudoscalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 84}, "ezgatr.interfaces.pseudoscalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 72}, "ezgatr.interfaces.reflection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.reflection.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 103}, "ezgatr.interfaces.reflection.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 68}, "ezgatr.interfaces.rotation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.rotation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 66}, "ezgatr.interfaces.rotation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 80}, "ezgatr.interfaces.scalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.scalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 78}, "ezgatr.interfaces.scalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 70}, "ezgatr.interfaces.translation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.translation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 87}, "ezgatr.interfaces.translation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 30, "bases": 0, "doc": 3}, "ezgatr.nets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 281}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 381, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_intermediate": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_num_heads": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_kinds": {"qualname": 3, "fullname": 8, "annotation": 15, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_dropout_p": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_is_causal": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_scale": {"qualname": 3, "fullname": 8, "annotation": 4, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_eps": {"qualname": 3, "fullname": 8, "annotation": 4, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_channelwise_rescale": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.gelu_approximate": {"qualname": 3, "fullname": 8, "annotation": 9, "default_value": 5, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 5, "doc": 63}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.__init__": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 31, "bases": 0, "doc": 14}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config": {"qualname": 2, "fullname": 7, "annotation": 7, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding": {"qualname": 2, "fullname": 7, "annotation": 6, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 34, "bases": 0, "doc": 67}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 5, "doc": 144}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.__init__": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 31, "bases": 0, "doc": 14}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config": {"qualname": 2, "fullname": 7, "annotation": 7, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil": {"qualname": 3, "fullname": 8, "annotation": 6, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out": {"qualname": 3, "fullname": 8, "annotation": 6, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, 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    \n \n

    \n

    \n Geometric Algebra Transformer Made Easy\n

    \n\n\n

    \"License:\n\"Python\n\"Ruff\"\n\"Website\"

    \n\n

    What is EzGATr?

    \n\n

    EzGATr (Easy Geometric Algebra Transformer) intends to be a simple-to-use and lightweight Python library for building 3D Geometric Algebra Transformers (GATr). It is a collection of operators, modules, utilities, etc. build on top of PyTorch. In addition, EzGATr also seeks to bridge the gap between the mathematical formulations and corresponding implementations through extensive documentation and explanations to facilitate learning and potential future optimizations.

    \n\n

    Installation

    \n\n

    EzGATr is currently in development and not yet available on PyPI. To install it, you can clone the repository and install it using pip. Use the -e flag to install it in editable mode for quick changes.

    \n\n
    \n
    $ git clone https://github.com/Guest400123064/ezgatr.git\n$ cd ezgatr\n$ pip install -e .\n
    \n
    \n\n

    Usage

    \n\n

    The quick start example shown below demonstrates how to use EzGATr to build a equivariant network with one input/output channel (e.g., a point cloud) and four hidden channels. The mock input data contains a batch of eight samples, each with 256 3D objects embedded as multi-vectors.

    \n\n
    \n
    import torch\nimport torch.nn as nn\n\nfrom ezgatr.nn import EquiLinear\nfrom ezgatr.nn.functional import scaler_gated_gelu\n\n\nclass SimpleNet(nn.Module):\n    def __init__(self):\n        super(SimpleNet, self).__init__()\n        self.fc1 = EquiLinear(1, 4)\n        self.fc2 = EquiLinear(4, 1)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = scaler_gated_gelu(x)\n        x = self.fc2(x)\n        return x\n\n\nnet = SimpleNet()\nin_ = torch.randn(8, 256, 1, 16)\nout = net(in_)\n
    \n
    \n\n

    One can refer to this example for how to build a full-fledged GATr model with EzGATr, involving equivariant geometric attention and geometric MLP.

    \n\n

    API References

    \n\n

    The complete API references for EzGATr can be found here. TL;DR, the package is organized as follows:

    \n\n
      \n
    • ezgatr.nn: Contains the core modules and layers for building GATr models. It is organized similarly to PyTorch's torch.nn package, where the functional submodule contains lower-level operators and transformations.
      • \n
    • ezgatr.interfaces: Contains the utility functions that help encode and decode 3D objects to and from multi-vectors.
      • \n
    • ezgatr.nets: Contains off-of-the-shelf networks built with EzGATr building blocks. It can also be used as references for building custom networks with EzGATr.
      • \n
    \n\n

    License

    \n\n

    EzGATr is distributed under the terms of the MIT license.

    \n"}, "ezgatr.interfaces": {"fullname": "ezgatr.interfaces", "modulename": "ezgatr.interfaces", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.plane": {"fullname": "ezgatr.interfaces.plane", "modulename": "ezgatr.interfaces.plane", "kind": "module", "doc": "

    \n"}, "ezgatr.interfaces.plane.encode_pga": {"fullname": "ezgatr.interfaces.plane.encode_pga", "modulename": "ezgatr.interfaces.plane", "qualname": "encode_pga", "kind": "function", "doc": "

    Encode planes into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • normals (torch.Tensor):\nNormal vectors of the planes with shape (..., 3).
      • \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(normals: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.plane.decode_pga": {"fullname": "ezgatr.interfaces.plane.decode_pga", "modulename": "ezgatr.interfaces.plane", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract normal and translation vectors from multi-vectors with PGA.

    \n\n

    Note that the translation vectors associated with each plane is not unique.\nIn this case, we use the PGA convention that e_0 coefficients are the\ndistances from the origin to the plane along the normal direction to determine\nthe translation vectors.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Normal vectors and end positions of the translation vectors;\ntensors of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.interfaces.point": {"fullname": "ezgatr.interfaces.point", "modulename": "ezgatr.interfaces.point", "kind": "module", "doc": "

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    Encode 3D points into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • points (torch.Tensor):\n3D points with xyz coordinates; tensor of shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(points: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.point.decode_pga": {"fullname": "ezgatr.interfaces.point.decode_pga", "modulename": "ezgatr.interfaces.point", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract 3D points from multi-vectors with PGA.

    \n\n

    One needs to divide the coordinates by the homogeneous coordinate to obtain\nthe 3D points. In this case, to prevent numerical instability, we set a threshold\nto the homogeneous coordinate, so that the division will only be performed when\nthe absolute value of the homogeneous coordinate is above the threshold.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    • eps (float, default to 1e-6):\nMinimum value of the additional, un-physical component. Necessary to avoid\nexploding values or NaNs when this un-physical component of the homogeneous\ncoordinates becomes too close to zero.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: 3D points with xyz coordinates; tensor of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor, eps: float = 1e-06) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.pseudoscalar": {"fullname": "ezgatr.interfaces.pseudoscalar", "modulename": "ezgatr.interfaces.pseudoscalar", "kind": "module", "doc": "

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    Encode scalars into the pseudoscalar dimension of multi-vectors with PGA.

    \n\n

    This function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.

    \n\n
    Parameters
    \n\n
      \n
    • scalars (torch.Tensor):\nScalars with shape (..., 1).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(pseudoscalars: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.pseudoscalar.decode_pga": {"fullname": "ezgatr.interfaces.pseudoscalar.decode_pga", "modulename": "ezgatr.interfaces.pseudoscalar", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract the pseudoscalar values from multi-vectors with PGA.

    \n\n

    This function do not automatically squeeze the last dimension when returning\nthe scalar tensor.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Scalars with shape (..., 1).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.reflection": {"fullname": "ezgatr.interfaces.reflection", "modulename": "ezgatr.interfaces.reflection", "kind": "module", "doc": "

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    Encode reflection into multi-vectors with PGA.

    \n\n

    Plane serves as both the geometric object and the reflection operation in\nPGA. Therefore, we encode reflection over a plane as a plane itself.

    \n\n
    Parameters
    \n\n
      \n
    • normals (torch.Tensor):\nNormal vectors of the reflection planes with shape (..., 3).
      • \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(normals: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.reflection.decode_pga": {"fullname": "ezgatr.interfaces.reflection.decode_pga", "modulename": "ezgatr.interfaces.reflection", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract normal and position vectors of the reflection plane with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Normal vectors and end positions of the translation vectors;\ntensors of shape (..., 3).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.interfaces.rotation": {"fullname": "ezgatr.interfaces.rotation", "modulename": "ezgatr.interfaces.rotation", "kind": "module", "doc": "

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    Encode 3D rotation (as quaternions) into multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • quaternions (torch.Tensor with shape (..., 4)):\nQuaternions in ijkw order and Hamilton convention (ijk = -1)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor with shape (..., 16): Embedding into multivector.
      • \n
    \n", "signature": "(quaternions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.rotation.decode_pga": {"fullname": "ezgatr.interfaces.rotation.decode_pga", "modulename": "ezgatr.interfaces.rotation", "qualname": "decode_pga", "kind": "function", "doc": "

    Extract quaternions from multi-vectors with PGA.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor with shape (..., 16)):\nMultivector.
      • \n
    • normalize (bool, default to False):\nWhether to normalize the quaternion to unit norm.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor with shape (..., 4): Quaternions in ijkw order and Hamilton convention (ijk = -1)
      • \n
    \n", "signature": "(mvs: torch.Tensor, normalize: bool = False) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.scalar": {"fullname": "ezgatr.interfaces.scalar", "modulename": "ezgatr.interfaces.scalar", "kind": "module", "doc": "

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    Encode scalars into multi-vectors with PGA.

    \n\n

    This function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.

    \n\n
    Parameters
    \n\n
      \n
    • scalars (torch.Tensor):\nScalars with shape (..., 1).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
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    Extract scalars from multi-vectors with PGA.

    \n\n

    This function do not automatically squeeze the last dimension when returning\nthe scalar tensor.

    \n\n
    Parameters
    \n\n
      \n
    • mvs (torch.Tensor):\nMulti-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Scalars with shape (..., 1).
      • \n
    \n", "signature": "(mvs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.translation": {"fullname": "ezgatr.interfaces.translation", "modulename": "ezgatr.interfaces.translation", "kind": "module", "doc": "

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    Encode translations into multi-vectors with PGA.

    \n\n

    We use the convention that translations are represented by the xyz coordinates\nof the translation vectors in 3D Euclidean space, i.e., end positions.

    \n\n
    Parameters
    \n\n
      \n
    • positions (torch.Tensor):\nEnd positions of the translation vectors with shape (..., 3).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with PGA representation; tensor of shape (..., 16).
      • \n
    \n", "signature": "(positions: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.interfaces.translation.decode_pga": {"fullname": "ezgatr.interfaces.translation.decode_pga", "modulename": "ezgatr.interfaces.translation", "qualname": "decode_pga", "kind": "function", "doc": "

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    Configuration class for the MVOnlyGATr model.

    \n\n
    Parameters
    \n\n
      \n
    • num_layers (int, default to 4):\nNumber of GATr blocks in the network.
      • \n
    • size_context (int, default to 2048):\nNumber of elements, e.g., number of points in a point cloud,\nin the input sequence. This argument is not actually used in\nthe model, but is kept for compatibility.
      • \n
    • size_channels_in (int, default to 1):\nNumber of input channels.
      • \n
    • size_channels_out (int, default to 1):\nNumber of output channels.
      • \n
    • size_channels_hidden (int, default to 32):\nNumber of hidden representation channels throughout the network, i.e.,\nthe input/output number of channels of the next layer, block, or module.
      • \n
    • size_channels_intermediate (int, default to 32):\nNumber of intermediate channels for the geometric bilinear operation.\nMust be even. This intermediate size should not be confused with the size\nof hidden representations throughout the network. It only refers to the\nhidden sizes used for the equivariant join and geometric product operations.
      • \n
    • norm_eps (float, optional):\nSmall value to prevent division by zero in the normalization layer.
      • \n
    • norm_channelwise_rescale (bool, default to True):\nApply learnable channel-wise rescaling weights to the normalized multi-vector\ninputs. Initialized to ones if set to True.
      • \n
    • gelu_approximate (str, default to \"tanh\"):\nApproximation method for the GeLU function. Default to \"tanh\".
      • \n
    \n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.__init__", "kind": "function", "doc": "

    \n", "signature": "(\tnum_layers: int = 4,\tsize_context: int = 2048,\tsize_channels_in: int = 1,\tsize_channels_out: int = 1,\tsize_channels_hidden: int = 32,\tsize_channels_intermediate: int = 32,\tattn_num_heads: int = 4,\tattn_kinds: dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None] = <factory>,\tattn_dropout_p: float = 0.0,\tattn_is_causal: bool = True,\tattn_scale: float | None = None,\tnorm_eps: float | None = None,\tnorm_channelwise_rescale: bool = True,\tgelu_approximate: Literal['none', 'tanh'] = 'tanh')"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.num_layers", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "4"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_context", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "2048"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_in", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "1"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_out", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "1"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_hidden", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "32"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_intermediate": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_intermediate", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.size_channels_intermediate", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "32"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_num_heads": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_num_heads", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_num_heads", "kind": "variable", "doc": "

    \n", "annotation": ": int", "default_value": "4"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_kinds": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_kinds", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_kinds", "kind": "variable", "doc": "

    \n", "annotation": ": dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_dropout_p": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_dropout_p", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_dropout_p", "kind": "variable", "doc": "

    \n", "annotation": ": float", "default_value": "0.0"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_is_causal": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_is_causal", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_is_causal", "kind": "variable", "doc": "

    \n", "annotation": ": bool", "default_value": "True"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_scale": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.attn_scale", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.attn_scale", "kind": "variable", "doc": "

    \n", "annotation": ": float | None", "default_value": "None"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_eps": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_eps", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.norm_eps", "kind": "variable", "doc": "

    \n", "annotation": ": float | None", "default_value": "None"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_channelwise_rescale": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.norm_channelwise_rescale", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.norm_channelwise_rescale", "kind": "variable", "doc": "

    \n", "annotation": ": bool", "default_value": "True"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.gelu_approximate": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.gelu_approximate", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrConfig.gelu_approximate", "kind": "variable", "doc": "

    \n", "annotation": ": Literal['none', 'tanh']", "default_value": "'tanh'"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding", "kind": "class", "doc": "

    Embedding layer to project input number of channels to hidden channels.

    \n\n

    This layer corresponds to the very first equivariant linear layer of the\noriginal design mentioned in the GATr paper.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.embedding", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear", "kind": "class", "doc": "

    Implements the geometric bilinear sub-layer of the geometric MLP.

    \n\n

    Geometric bilinear operation consists of geometric product and equivariant\njoin operations. The results of two operations are concatenated along the\nhidden channel axis and passed through a final equivariant linear projection\nbefore being passed to the next layer, block, or module.

    \n\n

    In both geometric product and equivariant join operations, the input\nmulti-vectors are first projected to a hidden space with the same number of\nchannels, i.e., left and right. Then, the results of each operation are\nderived from the interaction of left and right hidden representations, each\nwith half number of size_channels_intermediate.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_bil", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_out", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.forward", "kind": "function", "doc": "

    Forward pass of the geometric bilinear sub-layer.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • reference (torch.Tensor, optional):\nReference tensor for the equivariant join operation.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP", "kind": "class", "doc": "

    Geometric MLP block without scaler channels.

    \n\n

    Here we fix the structure of the MLP block to be a single equivariant linear\nprojection followed by a gated GELU activation function. In addition, the\nequivariant normalization layer can be configured to be learnable, so the\nnormalization layer needs to be included in the block instead of being shared\nacross the network.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.layer_norm", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.equi_bil", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.proj_out", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.forward", "kind": "function", "doc": "

    Forward pass of the geometric MLP block.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • reference (torch.Tensor, optional):\nReference tensor for the equivariant join operation.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention", "kind": "class", "doc": "

    Geometric attention block without scaler channels.

    \n\n

    The GATr attention calculation is slightly different from the original\ntransformers implementation in that each head has the sample number of\nchannels as the input tensor, instead of dividing into smaller chunks.\nIn this case, the final output linear transformation maps from\nsize_channels_hidden * attn_num_heads to size_channels_hidden.

    \n\n

    One additional note here is that the attn_mix parameter is a dictionary\nof learnable weighting parameter LOGITS for each attention kind.\nThey will be exponentiated before being used in the attention calculation.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.layer_norm", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.attn_mix", "kind": "variable", "doc": "

    \n", "annotation": ": dict[str, torch.Tensor]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_qkv", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_out", "kind": "variable", "doc": "

    \n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.forward", "kind": "function", "doc": "

    Forward pass of the geometric attention block.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nBatch of input hidden multi-vector representation tensor.
      • \n
    • attn_mask (torch.Tensor, optional):\nAttention mask tensor for the attention operation. Usually\nused if any specific attention constraints are needed within\na single sequence, such as padding mask or for discriminating\ndifferent subsequences.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Batch of output hidden multi-vector representation tensor of the\nsame number of hidden channels.
      • \n
    \n", "signature": "(\tself,\tx: torch.Tensor,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock", "kind": "class", "doc": "

    GATr block without scaler channels.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    • layer_id (int):\nIndex of the current block in the network.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig, layer_id: int)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.layer_id", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.mlp", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.attn", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel", "kind": "class", "doc": "

    Multi-Vector only GATr model.

    \n\n
    Parameters
    \n\n
      \n
    • config (MVOnlyGATrConfig):\nConfiguration object for the model. See MVOnlyGATrConfig for more details.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.__init__": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.__init__", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.config", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.embedding", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.blocks", "kind": "variable", "doc": "

    \n", "annotation": ": torch.nn.modules.container.ModuleList"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.head", "kind": "variable", "doc": "

    \n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(\tself,\tx: torch.Tensor,\treference: torch.Tensor | None = None,\tattn_mask: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn": {"fullname": "ezgatr.nn", "modulename": "ezgatr.nn", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.functional": {"fullname": "ezgatr.nn.functional", "modulename": "ezgatr.nn.functional", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.functional.compute_qk_for_daa": {"fullname": "ezgatr.nn.functional.compute_qk_for_daa", "modulename": "ezgatr.nn.functional", "qualname": "compute_qk_for_daa", "kind": "function", "doc": "

    Compute the query and key tensors for the distance-aware attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (torch.Tensor):\nQuery tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • key (torch.Tensor):\nKey tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • eps (float, optional):\nSmall value to avoid division by zero used in the linear square normalizer.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Query and key tensors for the equivariant distance-aware attention.\nBlade dimensions are NOT flattened.
      • \n
    \n", "signature": "(\tquery: torch.Tensor,\tkey: torch.Tensor,\teps: float | None = None) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.nn.functional.compute_qk_for_ipa": {"fullname": "ezgatr.nn.functional.compute_qk_for_ipa", "modulename": "ezgatr.nn.functional", "qualname": "compute_qk_for_ipa", "kind": "function", "doc": "

    Compute the query and key tensors for the inner product attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (torch.Tensor):\nQuery tensor with shape (B, H, T, qk_channels, 16).
      • \n
    • key (torch.Tensor):\nKey tensor with shape (B, H, T, qk_channels, 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • tuple[torch.Tensor, torch.Tensor]: Query and key tensors for the equivariant inner product attention.\nBlade dimensions are NOT flattened.
      • \n
    \n", "signature": "(\tquery: torch.Tensor,\tkey: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:", "funcdef": "def"}, "ezgatr.nn.functional.equi_dual": {"fullname": "ezgatr.nn.functional.equi_dual", "modulename": "ezgatr.nn.functional", "qualname": "equi_dual", "kind": "function", "doc": "

    Compute the dual of the input multi-vector.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Corresponding dual with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_geometric_attention": {"fullname": "ezgatr.nn.functional.equi_geometric_attention", "modulename": "ezgatr.nn.functional", "qualname": "equi_geometric_attention", "kind": "function", "doc": "

    Equivariant geometric attention.

    \n\n
    Parameters
    \n\n
      \n
    • query (GeometricQKVType):\nMulti-vector query tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • key (GeometricQKVType):\nMulti-vector key tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • value (GeometricQKVType):\nMulti-vector value tensor with shape (B, H, T, qk_channels, 16). If\nscalar channel tensors are supplied, they should be included in a tuple\nwith the multi-vector tensors.
      • \n
    • kinds (dict[GeometricAttnKindType, dict[str, Any] | None]):\nKinds of similarity measures to consider in the attention calculation\nalong with additional configuration/parameters sent to the corresponding\nquery-key generating function. One should supply a dictionary mapping\nfrom the kind to parameters in addition to query and key tensors. Use\nNone to denote no additional parameters supplied. Available options:\n
        \n
      • \"ipa\": Inner product attention
        • \n
      • \"daa\": Distance-aware attention
        • \n
      • \n
    • weight (list[torch.Tensor | float], optional):\nWeight tensor for the attention kinds. If not provided, the weights are\nset to 1.0 for all kinds to represent equal importance. Note that the\nweight tensors are NOT applied to the scalar inputs (if provided). If\nscalar channel tensors are supplied, weights are fixed to 1.0.
      • \n
    • attn_mask (torch.Tensor, optional):\nAttention mask tensor.
      • \n
    • dropout_p (float, default to 0.0):\nDropout probability.
      • \n
    • is_causal (bool, default to False):\nWhether to apply causal masking.
      • \n
    • scale (float, optional):\nScaling factor for the attention scores, overwriting the default scale\ndetermined by the hidden dimension.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output tensor with shape (B, H, T, v_channels, 16).
      • \n
    \n", "signature": "(\tquery: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tkey: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tvalue: torch.Tensor | tuple[torch.Tensor, torch.Tensor],\tkinds: dict[typing.Literal['ipa', 'daa'], dict[str, typing.Any] | None],\tweight: list[torch.Tensor | float] | None = None,\tattn_mask: torch.Tensor | None = None,\tdropout_p: float = 0.0,\tis_causal: bool = False,\tscale: float | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_join": {"fullname": "ezgatr.nn.functional.equi_join", "modulename": "ezgatr.nn.functional", "qualname": "equi_join", "kind": "function", "doc": "

    Compute the equivariant join of two multi-vectors given the reference.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • reference (torch.Tensor, optional):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Equivariant join result multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(\tx: torch.Tensor,\ty: torch.Tensor,\treference: torch.Tensor | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_linear": {"fullname": "ezgatr.nn.functional.equi_linear", "modulename": "ezgatr.nn.functional", "qualname": "equi_linear", "kind": "function", "doc": "

    Perform Pin-equivariant linear map defined by weight on input x.

    \n\n

    One way to think of the equivariant linear map is a channel-wise\n\"map-reduce\", where the same weight (of one neuron) are applied to\nall channels of the input multi-vector and the results are summed up\nalong the basis/blade axis. In other words, the map is a channel-mixing\noperation. Using a parallel analogy with a regular nn.Linear layer,\neach channel of a input multi-vector corresponds to a \"feature value\"\nof a simple hidden representation, and the number of output channels\nis the number of neurons in the hidden layer.

    \n\n

    Within each channel, similar to the geometric product implementation,\nthe linear operation starts with a matrix multiplication between the\n(weighted, if normalized) 16-by-16 computation graph, i.e., the basis,\nand the input multi-vector to take into account the effect that blades\ncontaining e_0 will be \"downgraded\" to a lower grade after the map.\nAgain, a map from \"source-to-destination\" style. Note that we may want\nto optimize this operation as the basis is pretty sparse.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor with shape (..., in_channels, 16)):\nInput multivector. Batch dimensions must be broadcastable between\nx and weight.
      • \n
    • weight (torch.Tensor with shape (out_channels, in_channels, 9)):\nCoefficients for the 9 basis elements. Batch dimensions must be\nbroadcastable between x and weight.
      • \n
    • bias (torch.Tensor with shape (out_channels,)):\nBias for the linear map. The bias values are only added to the\nscalar blades (i.e., index position 0) of each output channel.
      • \n
    • normalize_basis (bool):\nWhether to normalize the basis elements according to\nthe number of \"inflow paths\" of each blade.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output with shape (..., out_channels, 16).
      • \n
    \n", "signature": "(\tx: torch.Tensor,\tweight: torch.Tensor,\tbias: torch.Tensor | None = None,\tnormalize_basis: bool = True) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.equi_rms_norm": {"fullname": "ezgatr.nn.functional.equi_rms_norm", "modulename": "ezgatr.nn.functional", "qualname": "equi_rms_norm", "kind": "function", "doc": "

    Compute PGA inner-induced RMS norm of multi-vectors.

    \n\n

    Although the original GATr paper 1 named the normalization operation\nas E(3)-equivariant LayerNorm, we find the actual implementation more\nsimilar to RMSNorm 2 by substituting the scalar squaring with the inner\nproduct of multi-vectors, i.e., PGA inner-induced RMSNorm.

    \n\n

    We find the adaptation more intuitive by viewing each channel of the input\nmulti-vector as an \"imaginary number\", so that the inner product of the\nmulti-vector with itself is the squared \"modulus\". And, in turn, instead of\nthinking the input tensor as channels-of-multi-vectors, we can think of it\nas feature-vector-of-imaginary-numbers. And the normalization operation is\nthe same as the RMSNorm operation on the scalar-valued feature vectors.

    \n\n

    NOTE: THIS DESIGN IS PROBABLY QUESTIONABLE. The inner product do not\nconsider coefficients associated with blades containing e_0 while\nthe scaling is also applied to these blades.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nInput multi-vectors with shape (..., n_channels, 16).
      • \n
    • weight (torch.Tensor, optional):\nweight for re-scaling the normalized input. It can be both\nstatic or learnable, depending on how weight are initialized\noutside of the function.
      • \n
    • eps (float, optional):\nSmall value to prevent division by zero.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Normalized input with shape (..., n_channels, 16).
      • \n
    \n\n
    Reference
    \n\n
    \n
    \n
      \n
    1. \n

      \"Root Mean Square Layer Normalization\", Biao Zhang and Rico Sennrich,\n ↩

      \n
      2. \n\n
    2. \n

      \"Geometric Algebra Transformer\", Johann Brehmer, et al.,\n ↩

      \n
      3. \n
    \n
    \n", "signature": "(\tx: torch.Tensor,\tweight: torch.Tensor | None = None,\teps: float | None = None) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.geometric_product": {"fullname": "ezgatr.nn.functional.geometric_product", "modulename": "ezgatr.nn.functional", "qualname": "geometric_product", "kind": "function", "doc": "

    Geometric product between two batches of multi-vectors.

    \n\n

    The input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels.\nWhen channel dimensions are present, the geometric product is calculated\nchannel-wise (and batch-wise). For instance, the first channel of x[0]\nis multiplied with the first channel of y[0], and so on. No channel-mixing\nhere.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.inner_product": {"fullname": "ezgatr.nn.functional.inner_product", "modulename": "ezgatr.nn.functional", "qualname": "inner_product", "kind": "function", "doc": "

    Compute the PGA inner product between two multi-vectors.

    \n\n

    Similarly, the PGA inner product is calculated channel-wise (and batch-wise).\nNo channel-mixing here.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Inner product results of the multi-vectors with shape (..., 1).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.outer_product": {"fullname": "ezgatr.nn.functional.outer_product", "modulename": "ezgatr.nn.functional", "qualname": "outer_product", "kind": "function", "doc": "

    Outer product between two batches of multi-vectors.

    \n\n

    The input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels. When\nchannel dimensions are present, the outer product is calculated channel-wise\n(and batch-wise). For instance, the first channel of x[0] is multiplied\nwith the first channel of y[0], and so on. No channel-mixing here.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    • y (torch.Tensor):\nMulti-vectors with shape (..., 16).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Multi-vectors with shape (..., 16).
      • \n
    \n", "signature": "(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.functional.scaler_gated_gelu": {"fullname": "ezgatr.nn.functional.scaler_gated_gelu", "modulename": "ezgatr.nn.functional", "qualname": "scaler_gated_gelu", "kind": "function", "doc": "

    Compute scaler-gated GeLU activation function.

    \n\n
    Parameters
    \n\n
      \n
    • x (torch.Tensor):\nInput batch of multi-vectors.
      • \n
    • approximate (Literal[\"none\", \"tanh\"], default to \"tanh\"):\nApproximation method for the GeLU function. Default to \"tanh\".
      • \n
    \n\n
    Returns
    \n\n
      \n
    • torch.Tensor: Output batch of multi-vectors gated by scalar blade.
      • \n
    \n", "signature": "(\tx: torch.Tensor,\tapproximate: Literal['none', 'tanh'] = 'tanh') -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules": {"fullname": "ezgatr.nn.modules", "modulename": "ezgatr.nn.modules", "kind": "module", "doc": "

    \n"}, "ezgatr.nn.modules.EquiLinear": {"fullname": "ezgatr.nn.modules.EquiLinear", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear", "kind": "class", "doc": "

    Pin(3, 0, 1)-equivariant linear map.

    \n\n
    Parameters
    \n\n
      \n
    • in_channels (int):\nNumber of input channels.
      • \n
    • out_channels (int):\nNumber of output channels.
      • \n
    • normalize_basis (bool):\nWhether to normalize the basis elements according to the number of\n\"inflow paths\" for each blade.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nn.modules.EquiLinear.__init__": {"fullname": "ezgatr.nn.modules.EquiLinear.__init__", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(\tin_channels: int,\tout_channels: int,\tbias: bool = True,\tnormalize_basis: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiLinear.in_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.in_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.out_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.out_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.out_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.normalize_basis": {"fullname": "ezgatr.nn.modules.EquiLinear.normalize_basis", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.normalize_basis", "kind": "variable", "doc": "

    \n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiLinear.weight": {"fullname": "ezgatr.nn.modules.EquiLinear.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.weight", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor"}, "ezgatr.nn.modules.EquiLinear.bias": {"fullname": "ezgatr.nn.modules.EquiLinear.bias", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.bias", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiLinear.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiLinear.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.reset_parameters", "kind": "function", "doc": "

    \n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.forward": {"fullname": "ezgatr.nn.modules.EquiLinear.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.extra_repr": {"fullname": "ezgatr.nn.modules.EquiLinear.extra_repr", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.extra_repr", "kind": "function", "doc": "

    Set the extra representation of the module.

    \n\n

    To print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.

    \n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm": {"fullname": "ezgatr.nn.modules.EquiRMSNorm", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm", "kind": "class", "doc": "

    Applies PGA inner-induced RMS norm to a batch of multi-vectors.

    \n\n
    Parameters
    \n\n
      \n
    • in_channels (int):\nNumber of input multi-vector channels.
      • \n
    • eps (float, optional):\nSmall value to prevent division by zero.
      • \n
    • channelwise_rescale (bool, default to True):\nApply learnable channel-wise rescaling weights to the normalized\nmulti-vector inputs. Initialized to ones.
      • \n
    \n", "bases": "torch.nn.modules.module.Module"}, "ezgatr.nn.modules.EquiRMSNorm.__init__": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.__init__", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.__init__", "kind": "function", "doc": "

    Initialize internal Module state, shared by both nn.Module and ScriptModule.

    \n", "signature": "(\tin_channels: int,\teps: float | None = None,\tchannelwise_rescale: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiRMSNorm.in_channels": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.in_channels", "kind": "variable", "doc": "

    \n", "annotation": ": int"}, "ezgatr.nn.modules.EquiRMSNorm.eps": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.eps", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.eps", "kind": "variable", "doc": "

    \n", "annotation": ": float | None"}, "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.channelwise_rescale", "kind": "variable", "doc": "

    \n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiRMSNorm.weight": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.weight", "kind": "variable", "doc": "

    \n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiRMSNorm.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.reset_parameters", "kind": "function", "doc": "

    \n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.forward": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.forward", "kind": "function", "doc": "

    Define the computation performed at every call.

    \n\n

    Should be overridden by all subclasses.

    \n\n
    \n\n

    Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

    \n\n
    \n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.extra_repr": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.extra_repr", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.extra_repr", "kind": "function", "doc": "

    Set the extra representation of the module.

    \n\n

    To print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.

    \n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.utils": {"fullname": "ezgatr.utils", "modulename": "ezgatr.utils", "kind": "module", "doc": "

    \n"}, "ezgatr.utils.debug": {"fullname": "ezgatr.utils.debug", "modulename": "ezgatr.utils.debug", "kind": "module", "doc": "

    \n"}, "ezgatr.utils.debug.time_cuda_exec": {"fullname": "ezgatr.utils.debug.time_cuda_exec", "modulename": "ezgatr.utils.debug", "qualname": "time_cuda_exec", "kind": "function", "doc": "

    Time the execution of a function involving CUDA.

    \n", "signature": "(n_exec: int = 1000, report_avg: bool = True):", "funcdef": "def"}, "ezgatr.utils.logger": {"fullname": "ezgatr.utils.logger", "modulename": "ezgatr.utils.logger", "kind": "module", "doc": "

    \n"}}, "docInfo": {"ezgatr": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 778}, "ezgatr.interfaces": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 75}, "ezgatr.interfaces.plane.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 114}, "ezgatr.interfaces.point": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.point.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 61}, 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