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import copy |
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from typing import Optional, List |
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import torch |
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import torch.nn.functional as F |
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from torch import nn, Tensor |
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class Transformer(nn.Module): |
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def __init__(self, config, d_model=512, nhead=8, num_encoder_layers=6, |
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num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False, |
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return_intermediate_dec=False): |
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super().__init__() |
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encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, |
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dropout, activation, normalize_before) |
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encoder_norm = nn.LayerNorm(d_model) if normalize_before else None |
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self.encoder = TransformerEncoder( |
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encoder_layer, num_encoder_layers, encoder_norm) |
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self.embeddings = DecoderEmbeddings(config) |
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decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, |
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dropout, activation, normalize_before) |
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decoder_norm = nn.LayerNorm(d_model) |
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self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, |
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return_intermediate=return_intermediate_dec) |
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self._reset_parameters() |
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self.d_model = d_model |
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self.nhead = nhead |
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def _reset_parameters(self): |
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for p in self.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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def forward(self, src, mask, pos_embed, tgt, tgt_mask): |
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bs, c, h, w = src.shape |
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src = src.flatten(2).permute(2, 0, 1) |
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pos_embed = pos_embed.flatten(2).permute(2, 0, 1) |
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mask = mask.flatten(1) |
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tgt = self.embeddings(tgt).permute(1, 0, 2) |
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query_embed = self.embeddings.position_embeddings.weight.unsqueeze(1) |
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query_embed = query_embed.repeat(1, bs, 1) |
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memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) |
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hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, tgt_key_padding_mask=tgt_mask, |
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pos=pos_embed, query_pos=query_embed, |
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tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) |
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return hs |
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class TransformerEncoder(nn.Module): |
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def __init__(self, encoder_layer, num_layers, norm=None): |
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super().__init__() |
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self.layers = _get_clones(encoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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def forward(self, src, |
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mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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output = src |
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for layer in self.layers: |
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output = layer(output, src_mask=mask, |
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src_key_padding_mask=src_key_padding_mask, pos=pos) |
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if self.norm is not None: |
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output = self.norm(output) |
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return output |
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class TransformerDecoder(nn.Module): |
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def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): |
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super().__init__() |
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self.layers = _get_clones(decoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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self.return_intermediate = return_intermediate |
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def forward(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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output = tgt |
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intermediate = [] |
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for layer in self.layers: |
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output = layer(output, memory, tgt_mask=tgt_mask, |
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memory_mask=memory_mask, |
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tgt_key_padding_mask=tgt_key_padding_mask, |
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memory_key_padding_mask=memory_key_padding_mask, |
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pos=pos, query_pos=query_pos) |
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if self.return_intermediate: |
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intermediate.append(self.norm(output)) |
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if self.norm is not None: |
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output = self.norm(output) |
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if self.return_intermediate: |
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intermediate.pop() |
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intermediate.append(output) |
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if self.return_intermediate: |
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return torch.stack(intermediate) |
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return output |
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class TransformerEncoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, |
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src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(src, pos) |
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src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, |
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key_padding_mask=src_key_padding_mask)[0] |
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src = src + self.dropout1(src2) |
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src = self.norm1(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) |
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src = src + self.dropout2(src2) |
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src = self.norm2(src) |
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return src |
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def forward_pre(self, src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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src2 = self.norm1(src) |
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q = k = self.with_pos_embed(src2, pos) |
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src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, |
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key_padding_mask=src_key_padding_mask)[0] |
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src = src + self.dropout1(src2) |
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src2 = self.norm2(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) |
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src = src + self.dropout2(src2) |
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return src |
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def forward(self, src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(src, src_mask, src_key_padding_mask, pos) |
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return self.forward_post(src, src_mask, src_key_padding_mask, pos) |
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class TransformerDecoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.multihead_attn = nn.MultiheadAttention( |
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d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.norm3 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.dropout3 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(tgt, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout1(tgt2) |
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tgt = self.norm1(tgt) |
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask)[0] |
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tgt = tgt + self.dropout2(tgt2) |
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tgt = self.norm2(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) |
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tgt = tgt + self.dropout3(tgt2) |
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tgt = self.norm3(tgt) |
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return tgt |
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def forward_pre(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2 = self.norm1(tgt) |
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q = k = self.with_pos_embed(tgt2, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout1(tgt2) |
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tgt2 = self.norm2(tgt) |
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask)[0] |
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tgt = tgt + self.dropout2(tgt2) |
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tgt2 = self.norm3(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
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tgt = tgt + self.dropout3(tgt2) |
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return tgt |
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def forward(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(tgt, memory, tgt_mask, memory_mask, |
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) |
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return self.forward_post(tgt, memory, tgt_mask, memory_mask, |
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) |
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class DecoderEmbeddings(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.word_embeddings = nn.Embedding( |
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config.vocab_size, config.hidden_dim, padding_idx=config.pad_token_id) |
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self.position_embeddings = nn.Embedding( |
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config.max_position_embeddings, config.hidden_dim |
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) |
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self.LayerNorm = torch.nn.LayerNorm( |
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config.hidden_dim, eps=config.layer_norm_eps) |
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self.dropout = nn.Dropout(config.dropout) |
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def forward(self, x): |
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input_shape = x.size() |
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seq_length = input_shape[1] |
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device = x.device |
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position_ids = torch.arange( |
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seq_length, dtype=torch.long, device=device) |
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position_ids = position_ids.unsqueeze(0).expand(input_shape) |
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input_embeds = self.word_embeddings(x) |
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position_embeds = self.position_embeddings(position_ids) |
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embeddings = input_embeds + position_embeds |
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embeddings = self.LayerNorm(embeddings) |
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embeddings = self.dropout(embeddings) |
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return embeddings |
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def _get_clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
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def _get_activation_fn(activation): |
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"""Return an activation function given a string""" |
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if activation == "relu": |
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return F.relu |
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if activation == "gelu": |
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return F.gelu |
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if activation == "glu": |
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return F.glu |
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |
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def generate_square_subsequent_mask(sz): |
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r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). |
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Unmasked positions are filled with float(0.0). |
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""" |
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mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) |
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mask = mask.float().masked_fill(mask == 0, float( |
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'-inf')).masked_fill(mask == 1, float(0.0)) |
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return mask |
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def build_transformer(config): |
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return Transformer( |
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config, |
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d_model=config.hidden_dim, |
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dropout=config.dropout, |
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nhead=config.nheads, |
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dim_feedforward=config.dim_feedforward, |
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num_encoder_layers=config.enc_layers, |
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num_decoder_layers=config.dec_layers, |
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normalize_before=config.pre_norm, |
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return_intermediate_dec=False, |
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) |
