class torch.nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=<function relu>, layer_norm_eps=1e-05, batch_first=False, norm_first=False, device=None, dtype=None)[source]

TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application.

  • d_model (int) – the number of expected features in the input (required).

  • nhead (int) – the number of heads in the multiheadattention models (required).

  • dim_feedforward (int) – the dimension of the feedforward network model (default=2048).

  • dropout (float) – the dropout value (default=0.1).

  • activation (Union[str, Callable[[Tensor], Tensor]]) – the activation function of the intermediate layer, can be a string (“relu” or “gelu”) or a unary callable. Default: relu

  • layer_norm_eps (float) – the eps value in layer normalization components (default=1e-5).

  • batch_first (bool) – If True, then the input and output tensors are provided as (batch, seq, feature). Default: False (seq, batch, feature).

  • norm_first (bool) – if True, layer norm is done prior to attention and feedforward operations, respectively. Otherwise it’s done after. Default: False (after).

>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> out = encoder_layer(src)
Alternatively, when batch_first is True:
>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True)
>>> src = torch.rand(32, 10, 512)
>>> out = encoder_layer(src)
Fast path:

forward() will use a special optimized implementation described in FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness if all of the following conditions are met:

  • Either autograd is disabled (using torch.inference_mode or torch.no_grad) or no tensor argument requires_grad

  • training is disabled (using .eval())

  • batch_first is True and the input is batched (i.e., src.dim() == 3)

  • activation is one of: "relu", "gelu", torch.functional.relu, or torch.functional.gelu

  • at most one of src_mask and src_key_padding_mask is passed

  • if src is a NestedTensor, neither src_mask nor src_key_padding_mask is passed

  • the two LayerNorm instances have a consistent eps value (this will naturally be the case unless the caller has manually modified one without modifying the other)

If the optimized implementation is in use, a NestedTensor can be passed for src to represent padding more efficiently than using a padding mask. In this case, a NestedTensor will be returned, and an additional speedup proportional to the fraction of the input that is padding can be expected.

forward(src, src_mask=None, src_key_padding_mask=None, is_causal=False)[source]

Pass the input through the encoder layer.

  • src (Tensor) – the sequence to the encoder layer (required).

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

  • is_causal (bool) – If specified, applies a causal mask as src_mask. Default: False.

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

Return type:



see the docs in Transformer class.


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