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81 lines
2.7 KiB
81 lines
2.7 KiB
3 years ago
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"""
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Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention"
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Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py
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"""
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import torch
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from torch.nn import Module, Dropout
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def elu_feature_map(x):
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return torch.nn.functional.elu(x) + 1
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class LinearAttention(Module):
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def __init__(self, eps=1e-6):
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super().__init__()
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self.feature_map = elu_feature_map
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self.eps = eps
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def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
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""" Multi-Head linear attention proposed in "Transformers are RNNs"
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Args:
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queries: [N, L, H, D]
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keys: [N, S, H, D]
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values: [N, S, H, D]
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q_mask: [N, L]
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kv_mask: [N, S]
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Returns:
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queried_values: (N, L, H, D)
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"""
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Q = self.feature_map(queries)
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K = self.feature_map(keys)
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# set padded position to zero
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if q_mask is not None:
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Q = Q * q_mask[:, :, None, None]
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if kv_mask is not None:
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K = K * kv_mask[:, :, None, None]
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values = values * kv_mask[:, :, None, None]
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v_length = values.size(1)
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values = values / v_length # prevent fp16 overflow
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KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V
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Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps)
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queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length
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return queried_values.contiguous()
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class FullAttention(Module):
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def __init__(self, use_dropout=False, attention_dropout=0.1):
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super().__init__()
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self.use_dropout = use_dropout
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self.dropout = Dropout(attention_dropout)
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def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
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""" Multi-head scaled dot-product attention, a.k.a full attention.
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Args:
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queries: [N, L, H, D]
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keys: [N, S, H, D]
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values: [N, S, H, D]
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q_mask: [N, L]
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kv_mask: [N, S]
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Returns:
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queried_values: (N, L, H, D)
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"""
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# Compute the unnormalized attention and apply the masks
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QK = torch.einsum("nlhd,nshd->nlsh", queries, keys)
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if kv_mask is not None:
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QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float('-inf'))
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# Compute the attention and the weighted average
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softmax_temp = 1. / queries.size(3)**.5 # sqrt(D)
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A = torch.softmax(softmax_temp * QK, dim=2)
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if self.use_dropout:
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A = self.dropout(A)
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queried_values = torch.einsum("nlsh,nshd->nlhd", A, values)
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return queried_values.contiguous()
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