def forward(self, X):
assert X.dim() == 2
p_star = sparsemax_bisect(X, self.n_iter)
p_star /= p_star.sum(dim=1).unsqueeze(dim=1)
return torch.log(p_star)
def attn_map(self, Z):
if self.attn_func == "softmax":
return F.softmax(Z, -1)
elif self.attn_func == "esoftmax":
return esoftmax(Z, -1)
elif self.attn_func == "sparsemax":
return sparsemax(Z, -1)
elif self.attn_func == "tsallis15":
return tsallis15(Z, -1)
elif self.attn_func == "tsallis":
if self.attn_alpha == 2: # slightly faster specialized impl
return sparsemax_bisect(Z, self.bisect_iter)
else:
return tsallis_bisect(Z, self.attn_alpha, self.bisect_iter)
raise ValueError("invalid combination of arguments")
def forward(ctx, input, target, n_iter=50):
"""
input (FloatTensor): n x num_classes
target (LongTensor): n, the indices of the target classes
"""
assert_equal(input.shape[0], target.shape[0])
p_star = sparsemax_bisect(input, n_iter)
# this is onw done directly in sparsemax_bisect
# p_star /= p_star.sum(dim=1).unsqueeze(dim=1)
loss = _omega_sparsemax(p_star)
p_star.scatter_add_(1, target.unsqueeze(1),
torch.full_like(p_star, -1))
loss += torch.einsum("ij,ij->i", p_star, input)
ctx.save_for_backward(p_star)
# loss = torch.clamp(loss, min=0.0) # needed?
return loss
def test_sparsemax():
for _ in range(10):
x = 0.5 * torch.randn(10, 30000, dtype=torch.float32)
p1 = sparsemax(x, 1)
p2 = sparsemax_bisect(x)
assert torch.sum((p1 - p2)**2) < 1e-7