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| import torch | |
| import torch.nn.functional as F | |
| from torch import nn, Tensor | |
| import copy | |
| from typing import Optional, List | |
| 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}.") | |
| class TransformerEncoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): | |
| super(TransformerEncoderLayer, self).__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 forward(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None): | |
| src2 = self.self_attn(src, src, 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 | |