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| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| import copy, math | |
| from models.position_encoding import build_position_encoding | |
| class TransformerEncoder(nn.Module): | |
| def __init__(self, enc_layer, num_layers, use_dense_pos=False): | |
| super().__init__() | |
| self.layers = nn.ModuleList([copy.deepcopy(enc_layer) for i in range(num_layers)]) | |
| self.num_layers = num_layers | |
| self.use_dense_pos = use_dense_pos | |
| def forward(self, src, pos, padding_mask=None): | |
| if self.use_dense_pos: | |
| ## pos encoding at each MH-Attention block (q,k) | |
| output, pos_enc = src, pos | |
| for layer in self.layers: | |
| output, att_map = layer(output, pos_enc, padding_mask) | |
| else: | |
| ## pos encoding at input only (q,k,v) | |
| output, pos_enc = src + pos, None | |
| for layer in self.layers: | |
| output, att_map = layer(output, pos_enc, padding_mask) | |
| return output, att_map | |
| class EncoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", | |
| use_dense_pos=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) | |
| def with_pos_embed(self, tensor, pos): | |
| return tensor if pos is None else tensor + pos | |
| def forward(self, src, pos, padding_mask): | |
| q = k = self.with_pos_embed(src, pos) | |
| src2, attn = self.self_attn(q, k, value=src, key_padding_mask=padding_mask) | |
| 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, attn | |
| class TransformerDecoder(nn.Module): | |
| def __init__(self, dec_layer, num_layers, use_dense_pos=False, return_intermediate=False): | |
| super().__init__() | |
| self.layers = nn.ModuleList([copy.deepcopy(dec_layer) for i in range(num_layers)]) | |
| self.num_layers = num_layers | |
| self.use_dense_pos = use_dense_pos | |
| self.return_intermediate = return_intermediate | |
| def forward(self, tgt, tgt_pos, memory, memory_pos, | |
| tgt_padding_mask, src_padding_mask, tgt_attn_mask=None): | |
| intermediate = [] | |
| if self.use_dense_pos: | |
| ## pos encoding at each MH-Attention block (q,k) | |
| output = tgt | |
| tgt_pos_enc, memory_pos_enc = tgt_pos, memory_pos | |
| for layer in self.layers: | |
| output, att_map = layer(output, tgt_pos_enc, memory, memory_pos_enc, | |
| tgt_padding_mask, src_padding_mask, tgt_attn_mask) | |
| if self.return_intermediate: | |
| intermediate.append(output) | |
| else: | |
| ## pos encoding at input only (q,k,v) | |
| output = tgt + tgt_pos | |
| tgt_pos_enc, memory_pos_enc = None, None | |
| for layer in self.layers: | |
| output, att_map = layer(output, tgt_pos_enc, memory, memory_pos_enc, | |
| tgt_padding_mask, src_padding_mask, tgt_attn_mask) | |
| if self.return_intermediate: | |
| intermediate.append(output) | |
| if self.return_intermediate: | |
| return torch.stack(intermediate) | |
| return output, att_map | |
| class DecoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", | |
| use_dense_pos=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| self.corr_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) | |
| def with_pos_embed(self, tensor, pos): | |
| return tensor if pos is None else tensor + pos | |
| def forward(self, tgt, tgt_pos, memory, memory_pos, | |
| tgt_padding_mask, memory_padding_mask, tgt_attn_mask): | |
| q = k = self.with_pos_embed(tgt, tgt_pos) | |
| tgt2, attn = self.self_attn(q, k, value=tgt, key_padding_mask=tgt_padding_mask, | |
| attn_mask=tgt_attn_mask) | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt = self.norm1(tgt) | |
| tgt2, attn = self.corr_attn(query=self.with_pos_embed(tgt, tgt_pos), | |
| key=self.with_pos_embed(memory, memory_pos), | |
| value=memory, key_padding_mask=memory_padding_mask) | |
| 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, attn | |
| 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}.") | |
| #----------------------------------------------------------------------------------- | |
| ''' | |
| copy from the implementatoin of "attention-is-all-you-need-pytorch-master" by Yu-Hsiang Huang | |
| ''' | |
| class MultiHeadAttention(nn.Module): | |
| ''' Multi-Head Attention module ''' | |
| def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): | |
| super().__init__() | |
| self.n_head = n_head | |
| self.d_k = d_k | |
| self.d_v = d_v | |
| self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) | |
| self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) | |
| self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) | |
| self.fc = nn.Linear(n_head * d_v, d_model, bias=False) | |
| self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5) | |
| self.dropout = nn.Dropout(dropout) | |
| self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) | |
| def forward(self, q, k, v, mask=None): | |
| d_k, d_v, n_head = self.d_k, self.d_v, self.n_head | |
| sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) | |
| residual = q | |
| # Pass through the pre-attention projection: b x lq x (n*dv) | |
| # Separate different heads: b x lq x n x dv | |
| q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) | |
| k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) | |
| v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) | |
| # Transpose for attention dot product: b x n x lq x dv | |
| q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) | |
| if mask is not None: | |
| mask = mask.unsqueeze(1) # For head axis broadcasting. | |
| q, attn = self.attention(q, k, v, mask=mask) | |
| # Transpose to move the head dimension back: b x lq x n x dv | |
| # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) | |
| q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) | |
| q = self.dropout(self.fc(q)) | |
| q += residual | |
| q = self.layer_norm(q) | |
| return q, attn | |
| class ScaledDotProductAttention(nn.Module): | |
| ''' Scaled Dot-Product Attention ''' | |
| def __init__(self, temperature, attn_dropout=0.1): | |
| super().__init__() | |
| self.temperature = temperature | |
| self.dropout = nn.Dropout(attn_dropout) | |
| def forward(self, q, k, v, mask=None): | |
| attn = torch.matmul(q / self.temperature, k.transpose(2, 3)) | |
| if mask is not None: | |
| attn = attn.masked_fill(mask == 0, -1e9) | |
| attn = self.dropout(F.softmax(attn, dim=-1)) | |
| output = torch.matmul(attn, v) | |
| return output, attn |