##############################
-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)
def forward(self, x):
return x + self.f(x)
+
##############################
-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
- return x + torch.sin(t / (self.len_max ** ((j - k) / x.size(2))) + math.pi/2 * k)[None, :, :]
+ 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
+
##############################
+
class QKVAttention(nn.Module):
def __init__(
- self,
- dim_in, dim_qk, dim_v,
- nb_heads = 1, causal = False, attention_dropout = 0.0
+ self, dim_in, dim_qk, dim_v, nb_heads=1, causal=False, attention_dropout=0.0
):
super().__init__()
self.w_q = randw(nb_heads, dim_qk, dim_in)
self.w_k = randw(nb_heads, dim_qk, dim_in)
self.w_v = randw(nb_heads, dim_v, dim_in)
- self.w_o = randw(dim_in, dim_v * nb_heads)
+ self.w_o = randw(dim_v * nb_heads, dim_in)
- def forward(self, x_q, x_kv = None):
- if x_kv is None: x_kv = x_q
+ def forward(self, x_q, x_kv=None):
+ if x_kv is None:
+ x_kv = x_q
- q = torch.einsum('ntc,hdc->nhtd', x_q, self.w_q)
- k = torch.einsum('ntc,hdc->nhtd', x_kv, self.w_k)
- v = torch.einsum('ntc,hdc->nhtd', x_kv, self.w_v)
+ q = torch.einsum("ntc,hdc->nhtd", x_q, self.w_q)
+ k = torch.einsum("ntc,hdc->nhtd", x_kv, self.w_k)
+ v = torch.einsum("ntc,hdc->nhtd", x_kv, self.w_v)
- a = torch.einsum('nhtd,nhsd->nhts', q, k) / math.sqrt(q.size(3))
+ a = torch.einsum("nhtd,nhsd->nhts", q, k) / math.sqrt(q.size(3))
if self.causal:
- mask = torch.arange(a.size(2), device = q.device)[None, None, :, None] \
- < torch.arange(a.size(3), device = q.device)[None, None, None, :]
- a = a.masked_fill(mask, float('-inf'))
+ forbidden_attention = (
+ torch.arange(a.size(2), device=q.device)[None, None, :, None]
+ < torch.arange(a.size(3), device=q.device)[None, None, None, :]
+ )
+ a = a.masked_fill(forbidden_attention, float("-inf"))
- a = a.softmax(dim = 3)
+ a = a.softmax(dim=3)
a = F.dropout(a, self.attention_dropout, self.training)
- y = torch.einsum('nhts,nhsd->nthd', a, v).flatten(2)
+ y = torch.einsum("nhts,nhsd->nthd", a, v).flatten(2)
y = y @ self.w_o
return y
+
##############################
+
class MyGPT(nn.Module):
- def __init__(self,
- vocabulary_size,
- dim_model, dim_keys, dim_hidden,
- nb_heads, nb_blocks, dropout = 0.):
+ def __init__(
+ self,
+ vocabulary_size,
+ dim_model,
+ dim_keys,
+ dim_hidden,
+ 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 = [ ]
+ 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, dim_v = dim_model // nb_heads,
- nb_heads = nb_heads,
- causal = True, attention_dropout = dropout
+ dim_in=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ causal=True,
+ attention_dropout=dropout,
),
- nn.Linear(in_features = dim_model, out_features = dim_model),
),
- Residual(
- nn.LayerNorm(dim_model),
- nn.Linear(in_features = dim_model, out_features = dim_hidden),
+ 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),
+ nn.Linear(in_features=dim_hidden, out_features=dim_model),
nn.Dropout(dropout),
),
]
self.trunk = nn.Sequential(*trunk_blocks)
- self.readout = nn.Linear(in_features = dim_model, out_features = vocabulary_size)
+ self.readout = nn.Linear(in_features=dim_model, out_features=vocabulary_size)
+
+ with torch.no_grad():
+ for m in self.modules():
+ if isinstance(m, nn.Embedding):
+ m.weight.normal_(mean=0, std=2e-2)
+ elif isinstance(m, nn.LayerNorm):
+ m.bias.zero_()
+ m.weight.fill_(1.0)
def forward(self, x):
+ x = F.pad(x, (1, -1))
x = self.embedding(x)
x = self.trunk(x)
x = self.readout(x)
return x
+
######################################################################
-if __name__ == '__main__':
- print('Basic check.')
+if __name__ == "__main__":
+ print("Basic check.")
vocabulary_size = 10
x = torch.randint(vocabulary_size, (25, 100))
model = MyGPT(
- vocabulary_size = vocabulary_size,
- dim_model = 16, dim_keys = 50, dim_hidden = 100,
- nb_heads = 2, nb_blocks = 3,
- dropout = 0.1
+ vocabulary_size=vocabulary_size,
+ dim_model=18,
+ dim_keys=50,
+ dim_hidden=100,
+ nb_heads=2,
+ nb_blocks=3,
+ dropout=0.1,
)
y = model(x)
projects / mygpt.git / blobdiff