##############################
-class Residual(nn.Module):
+class WithResidual(nn.Module):
def __init__(self, *f):
super().__init__()
self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
##############################
-class PositionalEncoding(nn.Module):
+class AddPositionalEncoding(nn.Module):
def __init__(self, len_max):
super().__init__()
self.len_max = len_max
- # From Vaswani et al 2018
- # PE_{t,2i} = sin(t/(L^{2i/D}))
- # PE_{t,2i+1} = cos(t/(L^{2i/D}))
+ # [Vaswani et al 2018] PE_{t,2i} = sin(t/(L^{2i/D})), PE_{t,2i+1} = cos(t/(L^{2i/D}))
def forward(self, x):
t = torch.arange(x.size(1), dtype = x.dtype, device = x.device)[:, None]
j = torch.arange(x.size(2), dtype = x.dtype, device = x.device)[None, :]
k = j%2
pe = torch.sin(t / (self.len_max ** ((j - k) / x.size(2))) + math.pi/2 * k)
- return x + pe # Let broadcasting to its job
+ return x + pe
##############################
class QKVAttention(nn.Module):
- def __init__(
- self,
- dim_in, dim_qk, dim_v,
- nb_heads = 1, causal = False, attention_dropout = 0.0
- ):
+ def __init__(self,
+ dim_in, dim_qk, dim_v,
+ nb_heads = 1, causal = False, attention_dropout = 0.0):
super().__init__()
def randw(*d):
def __init__(self,
vocabulary_size,
dim_model, dim_keys, dim_hidden,
- nb_heads, nb_blocks, dropout = 0.):
+ nb_heads, nb_blocks,
+ dropout = 0.0, len_max = 1e5):
super().__init__()
self.embedding = nn.Sequential(
nn.Embedding(vocabulary_size, dim_model),
nn.Dropout(dropout),
- PositionalEncoding(len_max = 1e5),
+ AddPositionalEncoding(len_max),
)
trunk_blocks = [ ]
for _ in range(nb_blocks):
trunk_blocks += [
- Residual(
- nn.LayerNorm(dim_model),
+ WithResidual(
+ nn.LayerNorm((dim_model,)),
QKVAttention(
dim_in = dim_model,
dim_qk = dim_keys,
causal = True, attention_dropout = dropout
),
),
- Residual(
- nn.LayerNorm(dim_model),
+ WithResidual(
+ nn.LayerNorm((dim_model,)),
nn.Linear(in_features = dim_model, out_features = dim_hidden),
nn.ReLU(),
nn.Linear(in_features = dim_hidden, out_features = dim_model),
x = self.embedding(x)
x = self.trunk(x)
x = self.readout(x)
- return x[:, :-1]
+ x = F.pad(x, (0, 0, 0, -1))
+ return x
######################################################################