# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import copy from typing import Optional, List import torch import torch.nn.functional as F from torch import nn, Tensor class Transformer(nn.Module): def __init__(self, config, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False, return_intermediate_dec=False): super().__init__() encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) encoder_norm = nn.LayerNorm(d_model) if normalize_before else None self.encoder = TransformerEncoder( encoder_layer, num_encoder_layers, encoder_norm) self.embeddings = DecoderEmbeddings(config) decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) decoder_norm = nn.LayerNorm(d_model) self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, return_intermediate=return_intermediate_dec) self._reset_parameters() self.d_model = d_model self.nhead = nhead def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, src, mask, pos_embed, tgt, tgt_mask): # flatten NxCxHxW to HWxNxC bs, c, h, w = src.shape src = src.flatten(2).permute(2, 0, 1) pos_embed = pos_embed.flatten(2).permute(2, 0, 1) mask = mask.flatten(1) tgt = self.embeddings(tgt).permute(1, 0, 2) query_embed = self.embeddings.position_embeddings.weight.unsqueeze(1) query_embed = query_embed.repeat(1, bs, 1) memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, tgt_key_padding_mask=tgt_mask, pos=pos_embed, query_pos=query_embed, tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) return hs class TransformerEncoder(nn.Module): def __init__(self, encoder_layer, num_layers, norm=None): super().__init__() self.layers = _get_clones(encoder_layer, num_layers) self.num_layers = num_layers self.norm = norm def forward(self, src, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): output = src for layer in self.layers: output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) if self.norm is not None: output = self.norm(output) return output 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 class TransformerEncoderLayer(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) # 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.dropout1 = nn.Dropout(dropout) self.dropout2 = 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, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): q = k = self.with_pos_embed(src, pos) src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src = self.norm1(src) src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) src = src + self.dropout2(src2) src = self.norm2(src) return src def forward_pre(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): src2 = self.norm1(src) q = k = self.with_pos_embed(src2, pos) src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src2 = self.norm2(src) src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) src = src + self.dropout2(src2) return src def forward(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): if self.normalize_before: return self.forward_pre(src, src_mask, src_key_padding_mask, pos) return self.forward_post(src, src_mask, src_key_padding_mask, pos) 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) self.multihead_attn = nn.MultiheadAttention( d_model, nhead, dropout=dropout) # 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 DecoderEmbeddings(nn.Module): def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding( config.vocab_size, config.hidden_dim, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_dim ) self.LayerNorm = torch.nn.LayerNorm( config.hidden_dim, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.dropout) def forward(self, x): input_shape = x.size() seq_length = input_shape[1] device = x.device position_ids = torch.arange( seq_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0).expand(input_shape) input_embeds = self.word_embeddings(x) position_embeds = self.position_embeddings(position_ids) embeddings = input_embeds + position_embeds embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) 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 generate_square_subsequent_mask(sz): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float( '-inf')).masked_fill(mask == 1, float(0.0)) return mask def build_transformer(config): return Transformer( config, d_model=config.hidden_dim, dropout=config.dropout, nhead=config.nheads, dim_feedforward=config.dim_feedforward, num_encoder_layers=config.enc_layers, num_decoder_layers=config.dec_layers, normalize_before=config.pre_norm, return_intermediate_dec=False, )