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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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import copy |
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from typing import Optional, List |
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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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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(TransformerEncoderLayer, self).__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 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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src2 = self.self_attn(src, src, 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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