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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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from torch.nn import MultiheadAttention |
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from typing import Optional |
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import copy |
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def _get_activation_fn(activation): |
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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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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"): |
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super().__init__() |
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self.self_attn = 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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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(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 _get_clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
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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 Transformer(nn.Module): |
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def __init__(self, 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" |
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): |
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super().__init__() |
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self.num_encoder_layers = num_encoder_layers |
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if num_decoder_layers > 0: |
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encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, |
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dropout, activation) |
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encoder_norm = nn.LayerNorm(d_model) |
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self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) |
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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, pos_embed=None, mask=None): |
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src = src.permute(2, 0, 1) |
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if mask is not None: |
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mask = mask.flatten(1) |
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memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) |
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return memory.transpose(0,1) |
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