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# TransformerEncoder

class torch.nn.TransformerEncoder(encoder_layer, num_layers, norm=None, enable_nested_tensor=True, mask_check=True)

  TransformerEncoder is a stack of N encoder layers. Users can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.

  Parameters:

    - encoder_layer – an instance of the TransformerEncoderLayer() class (required).

    - num_layers – the number of sub-encoder-layers in the encoder (required).

    - norm – the layer normalization component (optional).

    - enable_nested_tensor – if True, input will automatically convert to nested tensor (and convert back on output). This will improve the overall performance of TransformerEncoder when padding rate is high. Default: `True` (enabled).

  Examples::

        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
        >>> src = torch.rand(10, 32, 512)
        >>> out = transformer_encoder(src)

  forward(src, mask=None, src_key_padding_mask=None, is_causal=None)

    Pass the input through the encoder layers in turn.

    Parameters:

      - src (Tensor) – the sequence to the encoder (required).

      - mask (Optional[Tensor]) – the mask for the src sequence (optional).

      - src_key_padding_mask (Optional[Tensor]) – the mask for the src keys per batch (optional).

      - is_causal (Optional[bool]) – If specified, applies a causal mask as `mask`. Default: `None`; try to detect a causal mask. Warning: `is_causal` provides a hint that `mask` is the causal mask. Providing incorrect hints can result in incorrect execution, including forward and backward compatibility.

    Return type:

      Tensor

    Shape:

      see the docs in Transformer class.

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