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| import copy | |
| from typing import Optional, List | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn, Tensor | |
| def _get_activation_fn(activation): | |
| """Return an activation function given a string""" | |
| if activation == "relu": | |
| return F.relu | |
| if activation == "gelu": | |
| return F.gelu | |
| if activation == "glu": | |
| return F.glu | |
| raise RuntimeError(F"activation should be relu/gelu, not {activation}.") | |
| def _get_clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | |
| class TransformerDecoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=True) | |
| self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=True) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| q = k = self.with_pos_embed(tgt, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt = self.norm1(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt = self.norm2(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| tgt = self.norm3(tgt) | |
| return tgt | |
| def forward_pre(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| tgt2 = self.norm1(tgt) | |
| q = k = self.with_pos_embed(tgt2, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt2 = self.norm2(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt2 = self.norm3(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| return tgt | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| if self.normalize_before: | |
| return self.forward_pre(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| return self.forward_post(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| class TransformerDecoder(nn.Module): | |
| def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): | |
| super().__init__() | |
| self.layers = _get_clones(decoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| self.return_intermediate = return_intermediate | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| output = tgt | |
| intermediate = [] | |
| for layer in self.layers: | |
| output = layer(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, | |
| pos=pos, query_pos=query_pos) | |
| if self.return_intermediate: | |
| intermediate.append(self.norm(output)) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| if self.return_intermediate: | |
| intermediate.pop() | |
| intermediate.append(output) | |
| if self.return_intermediate: | |
| return torch.stack(intermediate) | |
| return output | |
| def build_decoder( | |
| d_model=256, | |
| nhead=8, | |
| num_decoder_layers=3, | |
| dim_feedforward=2048, | |
| dropout=0.1, | |
| activation="relu", | |
| normalize_before=False, | |
| return_intermediate_dec=False, | |
| ): | |
| decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, | |
| dropout, activation, normalize_before) | |
| decoder_norm = nn.LayerNorm(d_model) | |
| decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, | |
| return_intermediate=return_intermediate_dec) | |
| return decoder | |