.

README.md

@@ -117,6 +117,8 @@ show(dist.marginals.detach()[0].sum(-1))
 
 ## Library
 
+Full docs: http://nlp.seas.harvard.edu/pytorch-struct/
+
 Current distributions implemented:
 
 * LinearChainCRF 

examples/tree.py

@@ -1,21 +1,13 @@
 # -*- coding: utf-8 -*-
 # wandb login 7cd7ade39e2d850ec1cf4e914d9a148586a20900
-from torch_struct import (
-    CKY_CRF,
-    CKY,
-    LogSemiring,
-    MaxSemiring,
-    SampledSemiring,
-    EntropySemiring,
-    SelfCritical,
-)
+from torch_struct import TreeCRF, SelfCritical
 import torchtext.data as data
 from torch_struct.data import ListOpsDataset, TokenBucket
-from torch_struct.networks import NeuralCFG, TreeLSTM, SpanLSTM
+from torch_struct.networks import TreeLSTM, SpanLSTM
 import torch
 import torch.nn as nn
 import wandb
-from torch_struct import MultiSampledSemiring
+
 
 config = {
     "method": "reinforce",
@@ -59,7 +51,7 @@ def expand_spans(spans, words, K, V):
 def valid_sup(valid_iter, model, tree_lstm, V):
     total = 0
     correct = 0
-    struct = CKY_CRF
+    Dist = TreeCRF
     for i, ex in enumerate(valid_iter):
         words, lengths = ex.word
         trees = ex.tree
@@ -74,8 +66,9 @@ def tree_reward(spans):
 
         words = words.cuda()
         phi = model(words, lengths)
-        argmax = struct(MaxSemiring).marginals(phi, lengths=lengths)
-        argmax_tree = struct().from_parts(argmax.detach())[0]
+        dist = TreeCRF(phi, lengths)
+        argmax = dist.argmax
+        argmax_tree = dist.struct.from_parts(argmax.detach())[0]
         score, tota = tree_reward(argmax_tree)
         total += int(tota)
         correct += score
@@ -95,8 +88,7 @@ def run_train(train_iter, valid_iter, model, tree_lstm, V):
     model.train()
     tree_lstm.train()
     losses = []
-    struct = CKY_CRF
-    entropy_fn = struct(EntropySemiring)
+    Dist = TreeCRF
     step = 0
     trees = None
     for epoch in range(100):
@@ -119,11 +111,10 @@ def tree_reward(spans, K):
                 ret = ret.view(K, batch, -1)
                 return -ret[:, torch.arange(batch), label].view(K, batch)
 
-            sc = SelfCritical(CKY_CRF, tree_reward)
+            sc = SelfCritical(tree_reward)
             phi = model(words, lengths)
-            structs, rewards, score, max_score = sc.forward(
-                phi, lengths, K=config["RL_K"]
-            )
+            dist = Dist(phi)
+            structs, rewards, score, max_score = sc.forward(dist, K=config["RL_K"])
 
             if config["train_model"]:
                 opt_params.zero_grad()
@@ -134,12 +125,8 @@ def tree_reward(spans, K):
 
             if config["method"] == "reinforce":
                 opt_struct.zero_grad()
-                log_partition, entropy = entropy_fn.sum(
-                    phi, lengths=lengths, _raw=True
-                ).unbind()
-                r = struct().score(
-                    phi.unsqueeze(0), structs, batch_dims=[0, 1]
-                ) - log_partition.unsqueeze(0)
+                entropy = dist.entropy
+                r = dist.log_prob(structs)
                 obj = rewards.mul(r).mean(-1).mean(-1)
                 policy = (
                     obj - config["entropy"] * entropy.div(lengths.float().cuda()).mean()
@@ -184,16 +171,17 @@ def tree_reward(spans, K):
 
 
 def valid_show(valid_iter, model):
-    struct = CKY_CRF
     table = wandb.Table(columns=["Sent", "Predicted Tree", "True Tree"])
+    Dist = TreeCRF
     for i, ex in enumerate(valid_iter):
         words, lengths = ex.word
         label = ex.label
         batch = label.shape[0]
         words = words.cuda()
         phi = model(words, lengths)
-        argmax = struct(MaxSemiring).marginals(phi, lengths=lengths)
-        argmax_tree = struct().from_parts(argmax.detach())[0].cpu()
+        dist = Dist(phi)
+        argmax = dist.argmax
+        argmax_tree = dist.struct.from_parts(argmax.detach())[0].cpu()
         for b in range(words.shape[0]):
             out = [WORD.vocab.itos[w.item()] for w in words[b]]
             sent = " ".join(out)
@@ -272,7 +260,6 @@ def main():
         for p in model.parameters():
             if p.dim() > 1:
                 torch.nn.init.xavier_uniform_(p)
-        struct = CKY_CRF
 
         wandb.watch((model, tree_lstm))
         print(wandb.config)

torch_struct/rl.py

@@ -1,27 +1,19 @@
 import torch
-from .semirings import MultiSampledSemiring, MaxSemiring
 
 
 class SelfCritical:
-    def __init__(self, struct, reward_fn):
-        self.struct = struct
+    def __init__(self, reward_fn):
         self.reward_fn = reward_fn
-        self.max_fn = self.struct(MaxSemiring)
-        self.sample_fn = self.struct(MultiSampledSemiring)
 
-    def forward(self, phi, lengths, K=5):
-        sample = self.sample_fn.marginals(phi, lengths=lengths)
-        sample = sample.detach()
+    def forward(self, dist, K=5):
+        samples = dist.sample((K,))
         trees = []
-        samples = []
         for k in range(K):
-            tmp_sample = MultiSampledSemiring.to_discrete(sample, k + 1)
-            samples.append(tmp_sample)
-            sampled_tree = self.max_fn.from_parts(tmp_sample)[0].cpu()
+            sampled_tree = dist.struct.from_parts(samples[k])[0].cpu()
             trees.append(sampled_tree)
-        structs = torch.stack(samples)
-        argmax = self.max_fn.marginals(phi, lengths=lengths)
-        argmax_tree = self.max_fn.from_parts(argmax.detach())[0].cpu()
+        structs = torch.stack(trees)
+        argmax = dist.argmax
+        argmax_tree = dist.struct.from_parts(argmax.detach())[0].cpu()
         trees.append(argmax_tree)
         sample_score = self.reward_fn(torch.cat(trees), K + 1)
         total = sample_score[:-1].mean(dim=0)