def test_it(self):
x = torch.randn(3, 4, 12)
x.requires_grad = True
m = TransformerDecoder(12, numheads=4, numlayers=2, noctx=True)
mc = q.deep_copy(m)
mc.set_cell_mode(True)
y = m(x)
y.norm(1).backward()
xgrad = x.grad
print(y.norm(1, 2))
print(xgrad.norm(1, 2))
# x = torch.randn(3, 4, 12)
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys = []
for i in range(x.size(1)):
ys.append(mc(x[:, i].unsqueeze(1)))
ys = torch.cat(ys, 1)
print(ys.norm(1, 2))
ys.norm(1).backward()
xsgrad = x.grad
print(xsgrad.norm(1, 2))
self.assertTrue(
np.allclose(y.detach().numpy(), ys.detach().numpy(), atol=1e-5))
self.assertTrue(
np.allclose(xgrad.detach().numpy(),
xsgrad.detach().numpy(),
atol=1e-5))
def test_it_relpos_out_of_horizon(self):
seqlen = 7 #10
horizon = 7
x = torch.randn(3, seqlen, 12)
x.requires_grad = True
m = TransformerDecoder(12,
numheads=4,
numlayers=2,
noctx=True,
relpos=False)
mc = q.deep_copy(m)
mc.set_cell_mode(True)
ys = []
allys = []
for i in range(horizon, seqlen + 1):
y = m(x[:, i - horizon:i])
allys.append(y)
ys.append(y[:, -1].unsqueeze(1))
ys_ref = torch.cat(ys, 1)
print(ys_ref.size())
ys_ref.norm(1).backward()
allys = allys[0]
xgrad = x.grad
print(ys_ref.norm(1, 2))
print(xgrad.norm(1, 2))
# x = torch.randn(3, 4, 12)
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys = []
for i in range(seqlen):
ys.append(mc(x[:, i].unsqueeze(1)))
ys = torch.cat(ys, 1)
print(ys.size())
_allys = ys[:, :horizon]
ys = ys[:, -(seqlen - horizon + 1):]
print(ys.norm(1, 2))
ys.norm(1).backward()
xsgrad = x.grad
print(xsgrad.norm(1, 2))
print("ALL YS")
print(allys.norm(1, 2))
print("___")
print(_allys.norm(1, 2))
self.assertTrue(
np.allclose(ys_ref.detach().numpy(),
ys.detach().numpy(),
atol=1e-5))
self.assertTrue(
np.allclose(xgrad.detach().numpy(),
xsgrad.detach().numpy(),
atol=1e-5))
def test_it(self):
x = torch.randn(4, 5, 12)
y = torch.randn(4, 5, 12)
x.requires_grad = True
y.requires_grad = True
numheads = 6
m = TS2S_arg(dim=12, numlayers=2, numheads=numheads)
z = m(x, y)
print(z.size())
z[:, -1].norm(1).backward()
xgrad = x.grad
ygrad = y.grad
print(xgrad.norm(1, 2))
print(ygrad.norm(1, 2))
zref = z
mc = q.deep_copy(m)
mc.set_cell_mode(True)
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
y = torch.tensor(y.detach().numpy() + 0.)
y.requires_grad = True
print("y size: ", y.size())
zs = []
for i in range(y.size(1)):
z = mc(x, y[:, i].unsqueeze(1))
print(z.size())
zs.append(z)
z = torch.cat(zs, 1)
z[:, -1].norm(1).backward()
print(x.grad.norm(1, 2))
print(y.grad.norm(1, 2))
print(z.norm(1, 2), zref.norm(1, 2))
print((z - zref).norm())
self.assertTrue(
np.allclose(z.detach().numpy(), zref.detach().numpy(), atol=1e-6))
print((x.grad - xgrad).norm(1))
self.assertTrue(
np.allclose(ygrad.detach().numpy(),
y.grad.detach().numpy(),
atol=1e-6))
self.assertTrue(
np.allclose(xgrad.detach().numpy(),
x.grad.detach().numpy(),
atol=1e-5))
def test_it_relpos_out_of_horizon(self):
seqlen = 7
horizon = 7
x = torch.randn(3, seqlen, 12)
x.requires_grad = True
m = TransformerDecoderBlock(12,
numheads=4,
bidir=False,
noctx=True,
relpos=False)
mc = q.deep_copy(m)
mc.set_cell_mode(True)
ys = []
allys = []
for i in range(horizon, seqlen + 1):
y = m(x[:, i - horizon:i])
allys.append(y)
ys.append(y[:, -1].unsqueeze(1))
ys_ref = torch.cat(ys, 1)
print(ys_ref.size())
ys_ref.norm(1).backward()
allys = allys[0]
xgrad = x.grad
print(ys_ref.norm(1, 2))
# print(xgrad.norm(1, 2))
# x = torch.randn(3, 4, 12)
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys = []
for i in range(seqlen):
ys.append(mc(x[:, i].unsqueeze(1)))
ys = torch.cat(ys, 1)
print(ys.size())
_allys = ys[:, :horizon]
ys = ys[:, -(seqlen - horizon + 1):]
print(ys.norm(1, 2))
ys.norm(1).backward()
xsgrad = x.grad
def test_it_relpos(self):
x = torch.randn(4, 5, 12)
x.requires_grad = True
numheads = 6
m = MultiHeadAttention(12, numheads=numheads, bidir=False, relpos=True)
mc = q.deep_copy(m)
mc.set_cell_mode(True)
ys = []
for i in range(x.size(1)):
y = mc(x[:, i].unsqueeze(1))
print(y.size())
ys.append(y)
ys = torch.cat(ys, 1)
l = ys.sum()
l.backward()
xgrad = x.grad
print(xgrad.norm(1, 2))
m.zero_grad()
mc.zero_grad()
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys_ref = m(x)
l = ys_ref.sum()
l.backward()
print(x.grad.norm(1, 2))
print("norm of out diff")
print((ys - ys_ref).norm())
print("norm of grad diff")
print((xgrad - x.grad).norm())
self.assertTrue(
np.allclose(ys.detach().numpy(),
ys_ref.detach().numpy(),
atol=1e-6))
self.assertTrue(
np.allclose(xgrad.detach().numpy(),
x.grad.detach().numpy(),
atol=1e-6))
def test_it_window(self):
x = torch.randn(4, 5, 12)
x.requires_grad = True
numheads = 6
m = MultiHeadAttention(12, numheads=numheads, bidir=False)
mc = q.deep_copy(m)
mc.set_cell_mode(True)
ys = []
for i in range(x.size(1)):
y = mc(x[:, i].unsqueeze(1))
print(y.size())
ys.append(y)
l = y.sum()
l.backward(retain_graph=True)
# TODO: check that outside window, grad on x is zero
x.grad = None
ys = torch.cat(ys, 1)
l = ys[:, 2].sum()
l.backward()
xgrad = x.grad
print(xgrad.norm(1, 2))
m.zero_grad()
mc.zero_grad()
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys_ref = m(x)
l = ys_ref[:, 2].sum()
l.backward()
print(x.grad.norm(1, 2))
self.assertTrue(
np.allclose(xgrad.detach().numpy(),
x.grad.detach().numpy()))
def tst_it_relpos_out_of_horizon_debug(self):
seqlen = 4
horizon = 3
x = torch.randn(3, seqlen, 12)
x.requires_grad = True
m = MultiHeadAttention(12, numheads=4, bidir=False)
mc = q.deep_copy(m)
mc.set_cell_mode(True, horizon=horizon)
y, k = m(x[:, 1:])
y.norm(1).backward()
xgrad = x.grad
# print(ys_ref.norm(1, 2))
# print(xgrad.norm(1, 2))
x = torch.tensor(x.detach().numpy() + 0.)
x.requires_grad = True
ys = []
ks = []
for i in range(x.size(1)):
ys_, k_ = mc(x[:, i].unsqueeze(1))
ys.append(ys_)
ks.append(k_)
ys = torch.cat(ys, 1)
# print(ys.norm(1, 2))
print(y.norm(1, 2))
print(ys.norm(1, 2))
print(k.size())
print(k.norm(1, 2))
print(ks[-1].size())
print(ks[-1].norm(1, 2))