import copy from typing import Optional, Any, Union, Callable import torch from torch import Tensor from .. import functional as F from .module import Module from .activation import MultiheadAttention from .container import ModuleList from ..init import xavier_uniform_ from .dropout import Dropout from .linear import Linear from .normalization import LayerNorm class Transformer(Module): r"""A transformer model. User is able to modify the attributes as needed. The architecture 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 can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. Args: d_model: the number of expected features in the encoder/decoder inputs (default=512). nhead: the number of heads in the multiheadattention models (default=8). num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6). num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of encoder/decoder intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu custom_encoder: custom encoder (default=None). custom_decoder: custom decoder (default=None). layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, encoder and decoder layers will perform LayerNorms before other attention and feedforward operations, otherwise after. Default: ``False`` (after). Examples:: >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12) >>> src = torch.rand((10, 32, 512)) >>> tgt = torch.rand((20, 32, 512)) >>> out = transformer_model(src, tgt) Note: A full example to apply nn.Transformer module for the word language model is available in https://github.com/pytorch/examples/tree/master/word_language_model """ def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6, num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, custom_encoder: Optional[Any] = None, custom_decoder: Optional[Any] = None, layer_norm_eps: float = 1e-5, batch_first: bool = False, norm_first: bool = False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(Transformer, self).__init__() if custom_encoder is not None: self.encoder = custom_encoder else: encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, layer_norm_eps, batch_first, norm_first, **factory_kwargs) encoder_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) if custom_decoder is not None: self.decoder = custom_decoder else: decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, layer_norm_eps, batch_first, norm_first, **factory_kwargs) decoder_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm) self._reset_parameters() self.d_model = d_model self.nhead = nhead self.batch_first = batch_first def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor: r"""Take in and process masked source/target sequences. Args: src: the sequence to the encoder (required). tgt: the sequence to the decoder (required). src_mask: the additive mask for the src sequence (optional). tgt_mask: the additive mask for the tgt sequence (optional). memory_mask: the additive mask for the encoder output (optional). src_key_padding_mask: the ByteTensor mask for src keys per batch (optional). tgt_key_padding_mask: the ByteTensor mask for tgt keys per batch (optional). memory_key_padding_mask: the ByteTensor mask for memory keys per batch (optional). Shape: - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or `(N, S, E)` if `batch_first=True`. - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or `(N, T, E)` if `batch_first=True`. - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. - memory_mask: :math:`(T, S)`. - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight. [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by the attention. If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions will be unchanged. If a BoolTensor is provided, the positions with the value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or `(N, T, E)` if `batch_first=True`. Note: Due to the multi-head attention architecture in the transformer model, the output sequence length of a transformer is same as the input sequence (i.e. target) length of the decode. where S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number Examples: >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask) """ is_batched = src.dim() == 3 if not self.batch_first and src.size(1) != tgt.size(1) and is_batched: raise RuntimeError("the batch number of src and tgt must be equal") elif self.batch_first and src.size(0) != tgt.size(0) and is_batched: raise RuntimeError("the batch number of src and tgt must be equal") if src.size(-1) != self.d_model or tgt.size(-1) != self.d_model: raise RuntimeError("the feature number of src and tgt must be equal to d_model") memory = self.encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask) output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) return output @staticmethod def generate_square_subsequent_mask(sz: int) -> Tensor: r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ return torch.triu(torch.full((sz, sz), float('-inf')), diagonal=1) def _reset_parameters(self): r"""Initiate parameters in the transformer model.""" for p in self.parameters(): if p.dim() > 1: xavier_uniform_(p) class TransformerEncoder(Module): r"""TransformerEncoder is a stack of N encoder layers Args: 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: ``False`` (disabled). 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) """ __constants__ = ['norm'] def __init__(self, encoder_layer, num_layers, norm=None, enable_nested_tensor=False): super(TransformerEncoder, self).__init__() self.layers = _get_clones(encoder_layer, num_layers) self.num_layers = num_layers self.norm = norm self.enable_nested_tensor = enable_nested_tensor def forward(self, src: Tensor, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor: r"""Pass the input through the encoder layers in turn. Args: src: the sequence to the encoder (required). mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). Shape: see the docs in Transformer class. """ output = src convert_to_nested = False first_layer = self.layers[0] if isinstance(first_layer, torch.nn.TransformerEncoderLayer): if (not first_layer.norm_first and not first_layer.training and first_layer.self_attn.batch_first and first_layer.self_attn._qkv_same_embed_dim and first_layer.activation_relu_or_gelu and first_layer.norm1.eps == first_layer.norm2.eps and src.dim() == 3 and self.enable_nested_tensor) : if src_key_padding_mask is not None and not output.is_nested and mask is None: tensor_args = ( src, first_layer.self_attn.in_proj_weight, first_layer.self_attn.in_proj_bias, first_layer.self_attn.out_proj.weight, first_layer.self_attn.out_proj.bias, first_layer.norm1.weight, first_layer.norm1.bias, first_layer.norm2.weight, first_layer.norm2.bias, first_layer.linear1.weight, first_layer.linear1.bias, first_layer.linear2.weight, first_layer.linear2.bias, ) if not torch.overrides.has_torch_function(tensor_args): if not torch.is_grad_enabled() or all([not x.requires_grad for x in tensor_args]): if output.is_cuda or 'cpu' in str(output.device): convert_to_nested = True output = torch._nested_tensor_from_mask(output, src_key_padding_mask.logical_not()) for mod in self.layers: if convert_to_nested: output = mod(output, src_mask=mask) else: output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask) if convert_to_nested: output = output.to_padded_tensor(0.) if self.norm is not None: output = self.norm(output) return output class TransformerDecoder(Module): r"""TransformerDecoder is a stack of N decoder layers Args: decoder_layer: an instance of the TransformerDecoderLayer() class (required). num_layers: the number of sub-decoder-layers in the decoder (required). norm: the layer normalization component (optional). Examples:: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = transformer_decoder(tgt, memory) """ __constants__ = ['norm'] def __init__(self, decoder_layer, num_layers, norm=None): super(TransformerDecoder, self).__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.norm = norm def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor: r"""Pass the inputs (and mask) through the decoder layer in turn. Args: tgt: the sequence to the decoder (required). memory: the sequence from the last layer of the encoder (required). tgt_mask: the mask for the tgt sequence (optional). memory_mask: the mask for the memory sequence (optional). tgt_key_padding_mask: the mask for the tgt keys per batch (optional). memory_key_padding_mask: the mask for the memory keys per batch (optional). Shape: see the docs in Transformer class. """ output = tgt for mod in self.layers: output = mod(output, memory, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) if self.norm is not None: output = self.norm(output) return output class TransformerEncoderLayer(Module): r"""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. Args: d_model: the number of expected features in the input (required). nhead: the number of heads in the multiheadattention models (required). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of the intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, layer norm is done prior to attention and feedforward operations, respectivaly. Otherwise it's done after. Default: ``False`` (after). Examples:: >>> 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 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``) - norm_first is ``False`` (this restriction may be loosened in the future) - 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. """ __constants__ = ['batch_first', 'norm_first'] def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1, activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, layer_norm_eps: float = 1e-5, batch_first: bool = False, norm_first: bool = False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(TransformerEncoderLayer, self).__init__() self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs) # Implementation of Feedforward model self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs) self.dropout = Dropout(dropout) self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs) self.norm_first = norm_first self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) # Legacy string support for activation function. if isinstance(activation, str): activation = _get_activation_fn(activation) # We can't test self.activation in forward() in TorchScript, # so stash some information about it instead. if activation is F.relu: self.activation_relu_or_gelu = 1 elif activation is F.gelu: self.activation_relu_or_gelu = 2 else: self.activation_relu_or_gelu = 0 self.activation = activation def __setstate__(self, state): super(TransformerEncoderLayer, self).__setstate__(state) if not hasattr(self, 'activation'): self.activation = F.relu def forward(self, src: Tensor, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor: r"""Pass the input through the encoder layer. Args: src: the sequence to the encoder layer (required). src_mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). Shape: see the docs in Transformer class. """ # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf if (src.dim() == 3 and not self.norm_first and not self.training and self.self_attn.batch_first and self.self_attn._qkv_same_embed_dim and self.activation_relu_or_gelu and self.norm1.eps == self.norm2.eps and src_mask is None and not (src.is_nested and src_key_padding_mask is not None)): tensor_args = ( src, self.self_attn.in_proj_weight, self.self_attn.in_proj_bias, self.self_attn.out_proj.weight, self.self_attn.out_proj.bias, self.norm1.weight, self.norm1.bias, self.norm2.weight, self.norm2.bias, self.linear1.weight, self.linear1.bias, self.linear2.weight, self.linear2.bias, ) if (not torch.overrides.has_torch_function(tensor_args) and # We have to use a list comprehension here because TorchScript # doesn't support generator expressions. all([(x.is_cuda or 'cpu' in str(x.device)) for x in tensor_args]) and (not torch.is_grad_enabled() or all([not x.requires_grad for x in tensor_args]))): return torch._transformer_encoder_layer_fwd( src, self.self_attn.embed_dim, self.self_attn.num_heads, self.self_attn.in_proj_weight, self.self_attn.in_proj_bias, self.self_attn.out_proj.weight, self.self_attn.out_proj.bias, self.activation_relu_or_gelu == 2, False, # norm_first, currently not supported self.norm1.eps, self.norm1.weight, self.norm1.bias, self.norm2.weight, self.norm2.bias, self.linear1.weight, self.linear1.bias, self.linear2.weight, self.linear2.bias, src_mask if src_mask is not None else src_key_padding_mask, # TODO: split into two args ) x = src if self.norm_first: x = x + self._sa_block(self.norm1(x), src_mask, src_key_padding_mask) x = x + self._ff_block(self.norm2(x)) else: x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask)) x = self.norm2(x + self._ff_block(x)) return x # self-attention block def _sa_block(self, x: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor: x = self.self_attn(x, x, x, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)[0] return self.dropout1(x) # feed forward block def _ff_block(self, x: Tensor) -> Tensor: x = self.linear2(self.dropout(self.activation(self.linear1(x)))) return self.dropout2(x) class TransformerDecoderLayer(Module): r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder 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. Args: d_model: the number of expected features in the input (required). nhead: the number of heads in the multiheadattention models (required). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of the intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, layer norm is done prior to self attention, multihead attention and feedforward operations, respectivaly. Otherwise it's done after. Default: ``False`` (after). Examples:: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = decoder_layer(tgt, memory) Alternatively, when ``batch_first`` is ``True``: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=True) >>> memory = torch.rand(32, 10, 512) >>> tgt = torch.rand(32, 20, 512) >>> out = decoder_layer(tgt, memory) """ __constants__ = ['batch_first', 'norm_first'] def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1, activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, layer_norm_eps: float = 1e-5, batch_first: bool = False, norm_first: bool = False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(TransformerDecoderLayer, self).__init__() self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs) self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs) # Implementation of Feedforward model self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs) self.dropout = Dropout(dropout) self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs) self.norm_first = norm_first self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.norm3 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) self.dropout3 = Dropout(dropout) # Legacy string support for activation function. if isinstance(activation, str): self.activation = _get_activation_fn(activation) else: self.activation = activation def __setstate__(self, state): if 'activation' not in state: state['activation'] = F.relu super(TransformerDecoderLayer, self).__setstate__(state) def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor: r"""Pass the inputs (and mask) through the decoder layer. Args: tgt: the sequence to the decoder layer (required). memory: the sequence from the last layer of the encoder (required). tgt_mask: the mask for the tgt sequence (optional). memory_mask: the mask for the memory sequence (optional). tgt_key_padding_mask: the mask for the tgt keys per batch (optional). memory_key_padding_mask: the mask for the memory keys per batch (optional). Shape: see the docs in Transformer class. """ # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf x = tgt if self.norm_first: x = x + self._sa_block(self.norm1(x), tgt_mask, tgt_key_padding_mask) x = x + self._mha_block(self.norm2(x), memory, memory_mask, memory_key_padding_mask) x = x + self._ff_block(self.norm3(x)) else: x = self.norm1(x + self._sa_block(x, tgt_mask, tgt_key_padding_mask)) x = self.norm2(x + self._mha_block(x, memory, memory_mask, memory_key_padding_mask)) x = self.norm3(x + self._ff_block(x)) return x # self-attention block def _sa_block(self, x: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor: x = self.self_attn(x, x, x, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)[0] return self.dropout1(x) # multihead attention block def _mha_block(self, x: Tensor, mem: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) -> Tensor: x = self.multihead_attn(x, mem, mem, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)[0] return self.dropout2(x) # feed forward block def _ff_block(self, x: Tensor) -> Tensor: x = self.linear2(self.dropout(self.activation(self.linear1(x)))) return self.dropout3(x) def _get_clones(module, N): return ModuleList([copy.deepcopy(module) for i in range(N)]) def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]: if activation == "relu": return F.relu elif activation == "gelu": return F.gelu raise RuntimeError("activation should be relu/gelu, not {}".format(activation))