emmi.modules.blocks.perceiver_transformer_blockpair
===================================================

.. py:module:: emmi.modules.blocks.perceiver_transformer_blockpair


Classes
-------

.. autoapisummary::

   emmi.modules.blocks.perceiver_transformer_blockpair.PerceiverTransformerBlock


Module Contents
---------------

.. py:class:: PerceiverTransformerBlock(hidden_dim, num_heads, transformer_attn_ctor = DotProductAttention, init_weights = 'truncnormal002', mlp_hidden_dim = None, drop_path = 0.0)

   Bases: :py:obj:`torch.nn.Module`


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing to nest them in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self):
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will have their
   parameters converted too when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   Instantiates a block which contains a perciever and a transformer block.
   :param hidden_dim: hidden Dimension of the transformer block.
   :param num_heads: Number of attention heads.
   :param mlp_hidden_dim: Hidden dim of the feed forward MLP after the self-attention. Defaults to None.
   :param init_weights: Initialization method for the weight matrixes of the network. Defaults to "truncnormal002".


   .. py:attribute:: perceiver


   .. py:attribute:: transformer


   .. py:method:: forward(q, kv, transformer_attn_kwargs = None)

      Forward pass of the transformer block.
      :param q: Input tensor with shape (batch_size, num_query_tokens, hidden_dim).
      :param kv: Input tensor with shape (batch_size, num_kv_tokens, hidden_dim).
      :param transformer_attn_kwargs: Dict with arguments for the attention of the transformer block (such as the
                                      attention mask). Defaults to None.

      :returns: Result with shape (batch_size, num_query_tokens, hidden_dim).