Merge cf96b31 into 6a55459

torch_struct/deptree.py

@@ -51,43 +51,48 @@ def _dp(self, arc_scores_in, lengths=None, force_grad=False):
         arc_scores = _convert(arc_scores_in)
         arc_scores, batch, N, lengths = self._check_potentials(arc_scores, lengths)
         arc_scores.requires_grad_(True)
-        DIRS = 2
         alpha = [
-            [Chart((batch, DIRS, N, N), arc_scores, semiring) for _ in range(2)]
+            [
+                [Chart((batch, N, N), arc_scores, semiring) for _ in range(2)]
+                for _ in range(2)
+            ]
             for _ in range(2)
         ]
-        semiring.one_(alpha[A][C].data[:, :, :, :, 0].data)
-        semiring.one_(alpha[B][C].data[:, :, :, :, -1].data)
-
-        def stack(a, b=None):
-            if b is None:
-                return torch.stack([a, a], dim=2)
-            else:
-                return torch.stack([a, b], dim=2)
+        semiring.one_(alpha[A][C][L].data[:, :, :, 0].data)
+        semiring.one_(alpha[A][C][R].data[:, :, :, 0].data)
+        semiring.one_(alpha[B][C][L].data[:, :, :, -1].data)
+        semiring.one_(alpha[B][C][R].data[:, :, :, -1].data)
 
         for k in range(1, N):
             f = torch.arange(N - k), torch.arange(k, N)
-            AC = alpha[A][C][:, : N - k, :k]
-            ACL, ACR = AC.unbind(2)
-
-            BC = alpha[B][C][:, k:, N - k :]
-            BCL, BCR = BC.unbind(2)
-            arcs = semiring.dot(
-                semiring.times(stack(ACR), stack(BCL)),
-                stack(
-                    arc_scores[:, :, f[1], f[0]], arc_scores[:, :, f[0], f[1]]
-                ).unsqueeze(-1),
-            )
-            alpha[A][I][:, : N - k, k] = arcs
-            alpha[B][I][:, k:N, N - k - 1] = arcs
+            ACL = alpha[A][C][L][: N - k, :k]
+            ACR = alpha[A][C][R][: N - k, :k]
+
+            BCL = alpha[B][C][L][k:, N - k :]
+            BCR = alpha[B][C][R][k:, N - k :]
+            x = semiring.dot(ACR, BCL)
+
+            arcs_l = semiring.times(x, arc_scores[:, :, f[1], f[0]])
+
+            alpha[A][I][L][: N - k, k] = arcs_l
+            alpha[B][I][L][k:N, N - k - 1] = arcs_l
+
+            arcs_r = semiring.times(x, arc_scores[:, :, f[0], f[1]])
+            alpha[A][I][R][: N - k, k] = arcs_r
+            alpha[B][I][R][k:N, N - k - 1] = arcs_r
+
+            AIR = alpha[A][I][R][: N - k, 1 : k + 1]
+            BIL = alpha[B][I][L][k:, N - k - 1 : N - 1]
+
+            new = semiring.dot(ACL, BIL)
+            alpha[A][C][L][: N - k, k] = new
+            alpha[B][C][L][k:N, N - k - 1] = new
 
-            AIR = alpha[A][I][R, : N - k, 1 : k + 1]
-            BIL = alpha[B][I][L, k:, N - k - 1 : N - 1]
-            new = semiring.dot(stack(ACL, AIR), stack(BIL, BCR))
-            alpha[A][C][:, : N - k, k] = new
-            alpha[B][C][:, k:N, N - k - 1] = new
+            new = semiring.dot(AIR, BCR)
+            alpha[A][C][R][: N - k, k] = new
+            alpha[B][C][R][k:N, N - k - 1] = new
 
-        final = alpha[A][C][R, 0]
+        final = alpha[A][C][R][(0,)]
         v = torch.stack([final[:, i, l] for i, l in enumerate(lengths)], dim=1)
         return v, [arc_scores], alpha
 

torch_struct/linearchain.py

@@ -36,9 +36,10 @@ def _check_potentials(self, edge, lengths=None):
 
         if lengths is None:
             lengths = torch.LongTensor([N] * batch)
-
-        assert max(lengths) <= N, "Length longer than edge scores"
-        assert max(lengths) == N, "One length must be at least N"
+            # pass
+        else:
+            assert max(lengths) <= N, "Length longer than edge scores"
+            assert max(lengths) == N, "One length must be at least N"
         assert C == C2, "Transition shape doesn't match"
         return edge, batch, N, C, lengths
 
@@ -49,17 +50,36 @@ def _dp_scan(self, log_potentials, lengths=None, force_grad=False):
         "Compute forward pass by linear scan"
         # Setup
         semiring = self.semiring
+        ssize = semiring.size()
         log_potentials, batch, N, C, lengths = self._check_potentials(
             log_potentials, lengths
         )
         log_N, bin_N = self._bin_length(N - 1)
         chart = self._chart((batch, bin_N, C, C), log_potentials, force_grad)
 
         # Init
-        for b in range(lengths.shape[0]):
-            end = lengths[b] - 1
-            semiring.one_(chart[:, b, end:].diagonal(0, 2, 3))
-            chart[:, b, :end] = log_potentials[:, b, :end]
+        semiring.one_(chart[:, :, :].diagonal(0, 3, 4))
+
+        # Length mask
+        big = torch.zeros(
+            ssize,
+            batch,
+            bin_N,
+            C,
+            C,
+            dtype=log_potentials.dtype,
+            device=log_potentials.device,
+        )
+        big[:, :, : N - 1] = log_potentials
+        c = chart[:, :, :].view(ssize, batch * bin_N, C, C)
+        lp = big[:, :, :].view(ssize, batch * bin_N, C, C)
+        mask = torch.arange(bin_N).view(1, bin_N).expand(batch, bin_N)
+        mask = mask >= (lengths - 1).view(batch, 1)
+        mask = mask.view(batch * bin_N, 1, 1).to(lp.device)
+        semiring.zero_mask_(lp.data, mask)
+        semiring.zero_mask_(c.data,  (~mask))
+
+        c[:] = semiring.sum(torch.stack([c.data, lp], dim=-1))
 
         # Scan
         for n in range(1, log_N + 1):

torch_struct/semimarkov.py

@@ -23,6 +23,7 @@ def _dp(self, log_potentials, lengths=None, force_grad=False):
 
         # Setup
         semiring = self.semiring
+        ssize = semiring.size()
         log_potentials.requires_grad_(True)
         log_potentials, batch, N, K, C, lengths = self._check_potentials(
             log_potentials, lengths
@@ -33,12 +34,36 @@ def _dp(self, log_potentials, lengths=None, force_grad=False):
         )
 
         # Init.
-        for b in range(lengths.shape[0]):
-            end = lengths[b] - 1
-            semiring.one_(init[:, b, end:, 0, 0].diagonal(0, 2, 3))
-            init[:, b, :end, : (K - 1), 0] = log_potentials[:, b, :end, 1:K]
-            for k in range(1, K - 1):
-                semiring.one_(init[:, b, : end - (k - 1), k - 1, k].diagonal(0, 2, 3))
+        semiring.one_(init.data[:, :, :, 0, 0].diagonal(0, -2, -1))
+
+        # Length mask
+        # big = torch.zeros(
+        #     ssize,
+        #     batch,
+        #     bin_N,
+        #     K,
+        #     C,
+        #     C,
+        #     dtype=log_potentials.dtype,
+        #     device=log_potentials.device,
+        # )
+        # big[:, :, : N - 1] = log_potentials
+        # c = init[:, :, :].view(ssize, batch * bin_N, K - 1, K - 1, C, C)
+        # lp = big[:, :, :].view(ssize, batch * bin_N, K, C, C)
+        # mask = torch.arange(bin_N) \
+        #             .view(1, bin_N).expand(batch, bin_N)
+        # mask = mask >= (lengths - 1).view(batch, 1)
+        # mask = mask.view(batch * bin_N, 1, 1, 1).to(lp.device)
+        # semiring.zero_mask_(lp.data, mask)
+        # semiring.zero_mask_(c.data[:, :, :, 0], (~mask))
+        # c[:, :, : K - 1, 0] = semiring.sum(
+        #     torch.stack([c.data[:, :, : K - 1, 0],
+        #                  lp[:, :, 1:K]], dim=-1)
+        # )
+        # end = torch.min(lengths) - 1
+        # for k in range(1, K - 1):
+        #     semiring.one_(init.data[:, :, : end - (k - 1), k - 1, k].diagonal(0, -2, -1))
+
         K_1 = K - 1
 
         # Order n, n-1

torch_struct/semirings/semirings.py

@@ -1,5 +1,11 @@
 import torch
-import genbmm
+
+has_genbmm = False
+try:
+    import genbmm
+    has_genbmm = True
+except ImportError:
+    pass
 
 
 def matmul(cls, a, b):
@@ -63,6 +69,17 @@ def zero_(xs):
         "Fill *ssize x ...* tensor with additive identity."
         raise NotImplementedError()
 
+    @classmethod
+    def zero_mask_(cls, xs, mask):
+        "Fill *ssize x ...* tensor with additive identity."
+        # xs.masked_fill_(mask[0], cls.zero)
+        # print(mask
+        # xs.masked_fill_(mask[0], cls.zero)
+        # xs[0, mask] = cls.zero
+        xs[0].masked_fill_(mask, cls.zero)
+        # print(mask.shape, xs.shape)
+        # assert False
+
     @staticmethod
     def one_(xs):
         "Fill *ssize x ...* tensor with multiplicative identity."
@@ -143,7 +160,7 @@ def matmul(cls, a, b, dims=1):
         (Faster than calling sum and times.)
         """
 
-        if isinstance(a, genbmm.BandedMatrix):
+        if has_genbmm and isinstance(a, genbmm.BandedMatrix):
             return b.multiply(a.transpose())
         else:
             return torch.matmul(a, b)
@@ -158,7 +175,7 @@ class LogSemiring(_BaseLog):
 
     @classmethod
     def matmul(cls, a, b):
-        if isinstance(a, genbmm.BandedMatrix):
+        if has_genbmm and isinstance(a, genbmm.BandedMatrix):
             return b.multiply_log(a.transpose())
         else:
             return _BaseLog.matmul(a, b)
@@ -173,7 +190,7 @@ class MaxSemiring(_BaseLog):
 
     @classmethod
     def matmul(cls, a, b):
-        if isinstance(a, genbmm.BandedMatrix):
+        if has_genbmm and isinstance(a, genbmm.BandedMatrix):
             return b.multiply_max(a.transpose())
         else:
             return matmul(cls, a, b)
@@ -269,6 +286,8 @@ class EntropySemiring(Semiring):
     * First-and second-order expectation semirings with applications to minimum-risk training on translation forests :cite:`li2009first`
     """
 
+    zero = 0
+
     @staticmethod
     def size():
         return 2
@@ -301,6 +320,12 @@ def mul(a, b):
     def prod(cls, xs, dim=-1):
         return xs.sum(dim)
 
+    @classmethod
+    def zero_mask_(cls, xs, mask):
+        "Fill *ssize x ...* tensor with additive identity."
+        xs[0].masked_fill_(mask, -1e5)
+        xs[1].masked_fill_(mask, 0)
+
     @staticmethod
     def zero_(xs):
         xs[0].fill_(-1e5)

torch_struct/test_algorithms.py

@@ -142,14 +142,20 @@ def test_cky(data):
 @settings(max_examples=50, deadline=None)
 def test_generic_a(data):
     model = data.draw(
-        sampled_from([Alignment])  # , LinearChain, SemiMarkov, CKY, CKY_CRF, DepTree])
+        sampled_from(
+            [SemiMarkov])#, Alignment , LinearChain, SemiMarkov, CKY, CKY_CRF, DepTree])
     )
+
     semiring = data.draw(sampled_from([LogSemiring, MaxSemiring]))
     struct = model(semiring)
     vals, (batch, N) = model._rand()
-    alpha = struct.sum(vals)
-    count = struct.enumerate(vals)[0]
 
+    with torch.autograd.profiler.profile(use_cuda=False) as prof:
+
+        alpha = struct.sum(vals)
+        count = struct.enumerate(vals)[0]
+    print(prof.key_averages().table(sort_by="cuda_time_total"))
+    # assert(False)
     assert alpha.shape[0] == batch
     assert count.shape[0] == batch
     assert alpha.shape == count.shape