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Add GPU loop unrolling for SimCube computation

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Ralph Tang
2018-02-04 23:20:22 -05:00
parent 28f62623be
commit a110b03164
2 changed files with 27 additions and 22 deletions
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vdpwi/__main__.py
+4 -4
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@@ -61,11 +61,11 @@ def evaluate(model, data_loader):
true_labels = []
for sent1, sent2, label_pmf in data_loader:
scores = model(sent1, sent2)
scores = F.softmax(scores).cpu().data.numpy()
scores = F.softmax(scores).cpu().data.numpy()[0]
prediction = np.dot(np.arange(1, len(scores) + 1), scores)
truth = np.dot(np.arange(1, len(scores) + 1), label_pmf.cpu().data.numpy())
truth = np.dot(np.arange(1, len(scores) + 1), label_pmf.cpu().data.numpy()[0])
predictions.append(prediction); true_labels.append(truth)
return EvaluateResult(stats.pearsonr(predictions, truth)[0], stats.spearmanr(predictions, truth)[0])
return EvaluateResult(stats.pearsonr(predictions, true_labels)[0], stats.spearmanr(predictions, true_labels)[0])
def train(config):
context = create_context(config)
@@ -75,7 +75,7 @@ def train(config):
for i, (sent1, sent2, label_pmf) in loader_wrapper:
context.model.train()
context.optimizer.zero_grad()
scores = context.model(sent1, sent2)
scores = F.log_softmax(context.model(sent1, sent2))
loss = context.criterion(scores, label_pmf)
loss.backward()
vdpwi/model.py
+23 -18
View File
@@ -58,28 +58,33 @@ class VDPWIModel(SerializableModule):
self.classifier_net = VDPWIConvNet(config.n_labels) if classifier_net is None else classifier_net
def compute_sim_cube(self, seq1, seq2):
def compute_sim(h1, h2):
h1_len = torch.sqrt(torch.sum(h1**2))
h2_len = torch.sqrt(torch.sum(h2**2))
def compute_sim(prism1, prism2):
prism1_len = torch.sqrt(torch.sum(prism1**2, 2))
prism2_len = torch.sqrt(torch.sum(prism2**2, 2))
dot_prod = torch.dot(h1, h2)
cos_dist = dot_prod / (h1_len * h2_len + 1E-8)
l2_dist = torch.sqrt(torch.sum((h1 - h2)**2))
return torch.cat([dot_prod, cos_dist, l2_dist])
dot_prod = torch.matmul(prism1.unsqueeze(2), prism2.unsqueeze(3))
dot_prod = dot_prod.squeeze(2).squeeze(2)
cos_dist = dot_prod / (prism1_len * prism2_len + 1E-8)
l2_dist = torch.sqrt(torch.sum((prism1 - prism2)**2, 2))
return torch.stack([dot_prod, cos_dist, l2_dist], 0)
def compute_prism(seq1, seq2):
prism1 = seq1.repeat(seq2.size(0), 1, 1)
prism2 = seq2.repeat(seq1.size(0), 1, 1)
prism1 = prism1.permute(1, 0, 2).contiguous()
prism2 = prism2.permute(0, 1, 2).contiguous()
return compute_sim(prism1, prism2)
sim_cube = Variable(torch.Tensor(13, seq1.size(0), seq2.size(0)))
if self.use_cuda:
sim_cube = sim_cube.cuda()
seq1_f = seq1[:, 0]
seq1_b = seq1[:, 1]
seq2_f = seq2[:, 0]
seq2_b = seq2[:, 1]
for t, (h1f, h1b) in enumerate(zip(seq1_f, seq1_b)):
for s, (h2f, h2b) in enumerate(zip(seq2_f, seq2_b)):
sim_cube[0:3, t, s] = compute_sim(torch.cat([h1f, h1b]), torch.cat([h2f, h2b]))
sim_cube[3:6, t, s] = compute_sim(h1f, h2f)
sim_cube[6:9, t, s] = compute_sim(h1b, h2b)
sim_cube[9:12, t, s] = compute_sim(h1f + h1b, h2f + h2b)
seq1_f = seq1[:, :300]
seq1_b = seq1[:, 300:]
seq2_f = seq2[:, :300]
seq2_b = seq2[:, 300:]
sim_cube[0:3] = compute_prism(seq1, seq2)
sim_cube[3:6] = compute_prism(seq1_f, seq2_f)
sim_cube[6:9] = compute_prism(seq1_b, seq2_b)
sim_cube[9:12] = compute_prism(seq1_f + seq1_b, seq2_f + seq2_b)
return sim_cube
def compute_focus_cube(self, sim_cube):
@@ -113,4 +118,4 @@ class VDPWIModel(SerializableModule):
sim_cube = self.compute_sim_cube(seq1, seq2)
focus_cube = self.compute_focus_cube(sim_cube)
logits = self.classifier_net(focus_cube.unsqueeze(0))
return F.log_softmax(logits)
return logits