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Merge 068848a into 6f06de9
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@srush
srush committed Sep 8, 2019
2 parents 6f06de9 + 068848a commit 9da7602
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Showing 5 changed files with 132 additions and 118 deletions.
4 changes: 2 additions & 2 deletions torch_struct/cky.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ def backward(ctx, grad_v):


class CKY(_Struct):
def sum(self, scores, lengths=None, force_grad=False, _autograd=False):
def sum(self, scores, lengths=None, force_grad=False, _autograd=True):
"""
Compute the inside pass of a CFG using CKY.
Expand Down Expand Up @@ -162,7 +162,7 @@ def _dp_backward(self, scores, lengths, alpha_in, v, force_grad=False):

return (term_marginals, edge_marginals, root_marginals)

def marginals(self, scores, lengths=None, _autograd=False):
def marginals(self, scores, lengths=None, _autograd=True):
"""
Compute the marginals of a CFG using CKY.
Expand Down
182 changes: 98 additions & 84 deletions torch_struct/deptree.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import torch
import itertools
from .helpers import _Struct, roll
from .helpers import _Struct, roll2


def _convert(logits):
Expand Down Expand Up @@ -48,16 +48,35 @@ def _dp(self, arc_scores, lengths=None, force_grad=False):

DIRS = 2

alpha = [
self._make_chart(2, (DIRS, batch, N, N), arc_scores, force_grad)
for _ in range(2)
]
# Want to fix this slicing function.
# class MySlice(Function):
# @staticmethod
# def forward(ctx, alpha, beta, s1, s2, e, a, b, c, d):
# indices = torch.tensor([s1, s2, e, a, b, c, d])
# ctx.save_for_backward(indices)
# return alpha[e, :, a:b, c:d]

# @staticmethod
# def backward(ctx, grad_v):
# a, = ctx.saved_tensors
# itertools(a[2], :, a[3]:a[4], a[5]:a[6])
# beta[a[0]][a[1]][a[2], :, a[3]:a[4], a[5]:a[6]] += grad_v
# return None, None, None, None, None, None, None, None, None

# s = MySlice.apply
def s(input, e, a, b, c, d):
return input[e, :, a:b, c:d]

def stack(a, b):
return torch.stack([a, b])

def sstack(a):
return torch.stack([a, a])

alpha = [
self._make_chart(2, (DIRS, batch, N, N), arc_scores, force_grad)
for _ in range(2)
]
arcs = self._make_chart(N, (DIRS, batch, N), arc_scores, force_grad)

# Inside step. assumes first token is root symbol
Expand All @@ -66,28 +85,32 @@ def sstack(a):

for k in range(1, N):
f = torch.arange(N - k), torch.arange(k, N)
arcs[k] = semiring.dot(
sstack(alpha[A][C][R, :, : N - k, :k]),
sstack(alpha[B][C][L, :, k:, N - k :]),
stack(arc_scores[:, f[1], f[0]], arc_scores[:, f[0], f[1]]).unsqueeze(
-1
arcs[k] = semiring.times(
sstack(
semiring.sum(
semiring.times(
s(alpha[A][C], R, 0, N - k, 0, k),
s(alpha[B][C], L, k, N, N - k, N),
)
)
),
stack(arc_scores[:, f[1], f[0]], arc_scores[:, f[0], f[1]]),
)
alpha[A][I][:, :, : N - k, k] = arcs[k]
alpha[B][I][:, :, k:N, N - k - 1] = alpha[A][I][:, :, : N - k, k]
alpha[A][C][:, :, : N - k, k] = semiring.dot(
stack(
alpha[A][C][L, :, : N - k, :k],
alpha[A][I][R, :, : N - k, 1 : k + 1],
s(alpha[A][C], L, 0, N - k, 0, k),
s(alpha[A][I], R, 0, N - k, 1, k + 1),
),
stack(
alpha[B][I][L, :, k:, N - k - 1 : N - 1],
alpha[B][C][R, :, k:, N - k :],
s(alpha[B][I], L, k, N, N - k - 1, N - 1),
s(alpha[B][C], R, k, N, N - k, N),
),
)
alpha[B][C][:, :, k:N, N - k - 1] = alpha[A][C][:, :, : N - k, k]

v = torch.stack([alpha[A][C][R, i, 0, l] for i, l in enumerate(lengths)])
print(v)
return (v, arcs[1:], alpha)

def _check_potentials(self, arc_scores, lengths=None):
Expand Down Expand Up @@ -116,99 +139,90 @@ def _dp_backward(self, arc_scores, lengths, alpha_in, v=None, force_grad=False):
for _ in range(2)
]

def stack(a, b):
return torch.stack([a, b], dim=-1)
def stack(a, b, dim=-1):
return torch.stack([a, b], dim=dim)

def sstack(a):
return torch.stack([a, a], dim=-1)

for k in range(N - 1, -1, -1):
# Initialize
for b, l in enumerate(lengths):
alpha[A][C][R, b, 0, l] = semiring.one()
alpha[B][C][R, b, l, N - l - 1] = semiring.one()

# R completes
# I -> C* C
# I -> C* C
# C -> I C*
a = semiring.dot(
*roll(
stack(alpha[A][I][R], alpha[A][I][L]),
sstack(alpha_in[A][C][L]),
N,
k,
1,
if N - k - 1 > 0:
# R completes
# I -> C* C
# I -> C* C
# C -> I C*
a = semiring.sum(
semiring.times(
*roll2(
stack(
stack(alpha[A][I][R], alpha[A][I][L]),
sstack(alpha_in[B][C][R]),
dim=0,
),
stack(
sstack(alpha_in[A][C][L]),
stack(alpha[B][I][L], alpha[B][I][R]),
dim=0,
),
N,
k,
1,
)
).view(2, batch, N - k - 1, -1),
dim=-1,
)
)

c = semiring.dot(*roll(alpha_in[B][I][R], alpha[B][C][R], N, k, 0))

alpha[A][C][R, :, : N - k - 1, k] = semiring.plus(
semiring.sum(a), alpha[A][C][R, :, : N - k - 1, k]
)
alpha[A][C][L, :, 1 : N - k, k] = a[1]
alpha[A][C][R, :, : N - k - 1, k] = a[0]

alpha[A][C][R][:, : N - k, k] = semiring.plus(
alpha[A][C][R][:, : N - k, k], c
)

# L completes
# I -> C* C
# I -> C* C
# C -> I C*
a = semiring.dot(
*roll(
sstack(alpha_in[B][C][R]),
stack(alpha[B][I][L], alpha[B][I][R]),
N,
k,
1,
for b, l in enumerate(lengths):
if l == k:
alpha[A][C][R, b, 0, l] = semiring.one()
alpha[B][C][R, b, l, N - l - 1] = semiring.one()

c = semiring.sum(
semiring.times(
*roll2(
stack(alpha[A][C][L], alpha_in[B][I][R], dim=0),
stack(alpha_in[A][I][L], alpha[B][C][R], dim=0),
N,
k,
0,
)
)
)

c = semiring.dot(*roll(alpha[A][C][L], alpha_in[A][I][L], N, k, 0))

alpha[A][C][L, :, 1 : N - k, k] = semiring.plus(
semiring.sum(a), alpha[A][C][L, :, 1 : N - k, k]
)
alpha[A][C][L][:, : N - k, k] = semiring.plus(
c, alpha[A][C][L][:, : N - k, k]
alpha[A][C][:, :, : N - k, k] = semiring.plus(
alpha[A][C][:, :, : N - k, k], c
)

# Compute reverses.
alpha[B][C][:, :, k:N, N - k - 1] = alpha[A][C][:, :, : N - k, k]

if k > 0:
f = torch.arange(N - k), torch.arange(k, N)

# Incomplete
alpha[A][I][R][:, : N - k, k] = semiring.dot(
arc_scores[:, f[0], f[1]].unsqueeze(-1),
*roll(alpha[A][C][R], alpha_in[A][C][R], N, k)
alpha[A][I][:, :, : N - k, k] = semiring.dot(
stack(
arc_scores[:, f[1], f[0]], arc_scores[:, f[0], f[1]], dim=0
).unsqueeze(-1),
*roll2(
stack(alpha_in[B][C][L], alpha[A][C][R], dim=0),
stack(alpha[B][C][L], alpha_in[A][C][R], dim=0),
N,
k,
)
)

# C -> C I
alpha[A][I][L][:, : N - k, k] = semiring.dot(
arc_scores[:, f[1], f[0]].unsqueeze(-1),
*roll(alpha_in[B][C][L], alpha[B][C][L], N, k)
)

# Compute reverses
alpha[B][I][:, :, k:N, N - k - 1] = alpha[A][I][:, :, : N - k, k]

v = alpha[A][C][R, :, 0, 0]
left = semiring.times(alpha[A][I][L, :, :, :], alpha_in[A][I][L, :, :, :])
right = semiring.times(alpha[A][I][R, :, :, :], alpha_in[A][I][R, :, :, :])

ret = torch.zeros(batch, N, N).type_as(left)
for k in range(N):
for d in range(N - k):
ret[:, k + d, k] = semiring.div_exp(
left[:, k, d] - arc_scores[:, k + d, k], v.view(batch)
)
ret[:, k, k + d] = semiring.div_exp(
right[:, k, d] - arc_scores[:, k, k + d], v.view(batch)
)
for k in torch.arange(N):
f = torch.arange(N - k), torch.arange(k, N)
ret[:, f[1], k] = left[:, k, f[0]]
ret[:, k, f[1]] = right[:, k, f[0]]

ret = semiring.div_exp(ret - arc_scores, v.view(batch, 1, 1))
return _unconvert(ret)

def _arrange_marginals(self, grads):
Expand Down
46 changes: 20 additions & 26 deletions torch_struct/helpers.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -7,29 +7,10 @@ def roll(a, b, N, k, gap=0):
return (a[:, : N - (k + gap), (k + gap) :], b[:, k + gap :, : N - (k + gap)])


class DPManual(Function):
@staticmethod
def forward(ctx, obj, input, lengths):
with torch.no_grad():
v, _, alpha = obj._dp(input, lengths, False)
ctx.obj = obj
ctx.lengths = lengths
ctx.alpha = alpha

if isinstance(input, tuple):
ctx.save_for_backward(*input)
else:
ctx.save_for_backward(input)
return v
def roll2(a, b, N, k, gap=0):
return (a[:, :, : N - (k + gap), (k + gap) :], b[:, :, k + gap :, : N - (k + gap)])


@staticmethod
def backward(ctx, grad_v):
input = ctx.saved_tensors
if len(input) == 1:
input = input[0]
with torch.no_grad():
marginals = ctx.obj._dp_backward(input, ctx.lengths, ctx.alpha)
return None, marginals, None


class _Struct:
Expand Down Expand Up @@ -61,17 +42,30 @@ def sum(self, edge, lengths=None, _autograd=True):
Returns:
v: b tensor of total sum
"""


if (
_autograd
or self.semiring is not LogSemiring
or not hasattr(self, "_dp_backward")
):
return self._dp(edge, lengths)[0]
else:
return DPManual.apply(self, edge, lengths)
v, _, alpha = self._dp(edge, lengths, False)

class DPManual(Function):
@staticmethod
def forward(ctx, input):
return v

@staticmethod
def backward(ctx, grad_v):
marginals = self._dp_backward(edge, lengths, alpha)
return marginals.mul(
grad_v.view((grad_v.shape[0],) + tuple([1] * marginals.dim())))

return DPManual.apply(edge)
def marginals(self, edge, lengths=None, _autograd=True):
"""
Compute the marginals of a structured model.
Expand All @@ -83,15 +77,15 @@ def marginals(self, edge, lengths=None, _autograd=True):
marginals: b x (N-1) x C x C table
"""
v, edge, alpha = self._dp(edge, lengths=lengths, force_grad=True)
v, edges, alpha = self._dp(edge, lengths=lengths, force_grad=True)
if (
_autograd
or self.semiring is not LogSemiring
or not hasattr(self, "_dp_backward")
):
marg = torch.autograd.grad(
v.sum(dim=0),
edge,
edges,
create_graph=True,
only_inputs=True,
allow_unused=False,
Expand Down
2 changes: 1 addition & 1 deletion torch_struct/semirings.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def one():

@staticmethod
def div_exp(a, b):
return a.exp().div(b.exp())
return (a - b).exp()


class LogSemiring(_BaseLog):
Expand Down
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