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optimizer.py
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import numpy as np
import util
import time
import constants
import torch
from torch.optim import Adam
import os
from negative_sampling import Dynamic_Sampler, Policy_Sampler
from torch import nn
from torch.autograd import Variable
import operator
import copy
class SGD(object):
def __init__(self,train,dev,model,negative_sampler,evaluator,results_dir,config,state=None):
self.train = train
self.dev = dev
self.model = model
self.evaluator = evaluator
self.ns = negative_sampler
self.results_dir = results_dir
self.model_name = config['model']
#SGD Params
lr = config.get('lr',0.001)
l2 = config.get('l2',0.0)
#self.batch_size = config.get('batch_size',constants.batch_size)
self.batch_size = config.get('batch_size',constants.batch_size)
print("lr: {:.4f}, l2: {:.5f}, batch_size: {}".format(lr,l2,self.batch_size))
self.optim = Adam(model.parameters(),lr=lr,weight_decay=l2)
if state is not None:
self.optim.load_state_dict(state)
#Report and Early Stopping Params
self.prev_score = evaluator.init_score
self.early_stop_counter = constants.early_stop_counter
self.patience = constants.patience
self.num_epochs = config['num_epochs']
self.report_steps = constants.report_steps
self.test_batch_size = config.get('test_batch_size',constants.test_batch_size)
self.halt = False
self.dump = True # save without checking
self.prev_steps = 0
self.prev_time = time.time()
#Loss
self.mm = nn.MarginRankingLoss(margin=1)
self.bce = torch.nn.BCEWithLogitsLoss()
def minimize(self):
print("Training...")
for epoch in range(self.num_epochs):
start = time.time()
train_cp = list(self.train)
np.random.shuffle(train_cp)
batches = util.chunk(train_cp, self.batch_size)
for step,batch in enumerate(batches):
self.optim.zero_grad()
loss = self.fprop(batch)
loss.backward()
g_norm = torch.nn.utils.clip_grad_norm(self.model.parameters(), 100)
self.optim.step()
if step % self.report_steps == 0:
self.report(step,g_norm)
self.prev_steps=0
self.prev_time=time.time()
end = time.time()
mins = int(end - start)/60
secs = int(end - start)%60
print("Epoch {} took {} minutes {} seconds".format(epoch+1,mins,secs))
# Refresh
self.save(self.dump)
# Only one epoch for Dynamic Samplers
if isinstance(self.ns,Dynamic_Sampler) or isinstance(self.ns,Policy_Sampler):
self.dump = False
if epoch>=4:
self.halt = True
if self.halt:
return
def forward(self,batch,volatile,is_target):
negs = self.ns.batch_sample(batch, is_target)
batch = util.get_triples(batch, negs, is_target, volatile=volatile)
score = self.model(*batch)
#return self.logistic(score)
return self.max_margin(score)
def fprop(self,batch,volatile=False):
return self.forward(batch,volatile,True) + self.forward(batch, volatile, False)
def max_margin(self,scores):
y = util.to_var(np.ones(scores.size()[0],dtype='float32'), requires_grad=False)
loss = self.mm(scores[:,0],scores[:,1],y)
for i in range(2,scores.size()[1]):
loss += self.mm(scores[:,0],scores[:,i],y)
return loss/(scores.size()[1]-1.)
def logistic(self,scores):
y_pos = util.to_var(np.ones(scores.size()[0],dtype='float32'),requires_grad=False)
y_neg = util.to_var(np.zeros(scores.size()[0], dtype='float32'),requires_grad=False)
loss = self.bce(scores[:, 0],y_pos)
for i in range(1,scores.size()[1]):
loss += self.bce(scores[:, i], y_neg)
return loss/scores.size()[1]
def save(self,dump=False):
curr_score = self.evaluate(self.dev,self.test_batch_size,True)
print("Current Score: {}, Previous Score: {}".format(curr_score,self.prev_score))
if self.evaluator.comparator(curr_score, self.prev_score) or dump:
print("Saving params...\n")
torch.save(self.model.state_dict(), os.path.join(
self.results_dir,'{}_params.pt'.format(self.model_name)))
#Save Optimizer Gradient History for resuming training
state_path = os.path.join(self.results_dir,"{}_optim_state.pt".format(self.model_name))
torch.save(self.optim.state_dict(),state_path)
self.prev_score = curr_score
# Reset early stop counter
self.early_stop_counter = self.patience
else:
self.early_stop_counter -= 1
print("New params worse than current, skip saving...\n")
if self.early_stop_counter <= 0:
self.halt = True
def report(self,step,g_norm):
norm_rep = "Gradient norm {:.4f}".format(g_norm)
# Profiler
secs = time.time() - self.prev_time
num_steps = step - self.prev_steps
speed = num_steps*self.batch_size / float(secs)
self.prev_steps = step
self.prev_time = time.time()
speed_rep = "Speed: {:.4f} steps/sec".format(speed)
# Objective
train_obj = self.eval_obj(self.train)
dev_obj = self.eval_obj(self.dev)
obj_rep = "Train Obj.: {:.4f}, Dev Obj: {:.4f}".format(train_obj[0], dev_obj[0])
print("{}, {}, {}".format(norm_rep, speed_rep,obj_rep))
def evaluate(self,data,num_samples,sample=True):
if sample:
batch_size = np.minimum(num_samples, self.test_batch_size)
samples = util.chunk(util.sample(data,num_samples), batch_size)
else:
samples = util.chunk(data, self.test_batch_size)
values = [self.evaluator.evaluate(s) for s in samples]
return np.nanmean(values)
def eval_obj(self,data):
samples = util.sample(data,np.minimum(1000,self.test_batch_size))
loss = self.fprop(samples, volatile=True).data.cpu().numpy()
return loss
class Reinforce(SGD):
def __init__(self, train, dev, model, negative_sampler, evaluator, results_dir, config, state=None):
assert isinstance(negative_sampler,Policy_Sampler)
super(Reinforce,self).__init__(train, dev, model, negative_sampler, evaluator, results_dir, config, state=state)
self.arms = dict()
self.softmax = nn.Softmax()
# weight decay factor
self.delta = 0.1
self.frozen_model = copy.deepcopy(model)
def fprop(self, batch, volatile=False):
s_loss = self.reinforce(batch, False, volatile)
t_loss = self.reinforce(batch, True, volatile)
return s_loss + t_loss
def reinforce(self,batch,is_target,volatile):
entities = self.ns.batch_targets(batch,self.arms,is_target)
batch_var = util.get_triples(batch, entities, is_target, volatile=volatile)
scores = self.frozen_model(*batch_var)
loss = self.sample(batch,is_target,scores,entities)
# weight decay
#self.decay()
return torch.neg(loss)
def sample(self,batch,is_target,scores,entities):
policy = self.softmax(scores[:,1:])
policy_np = policy.data.cpu().numpy()
loss = Variable(torch.from_numpy(np.asarray([0],dtype='float32')))
for count in range(policy_np.shape[0]):
# sample an action (choose a target)
positives = self.ns.pos(batch[count], is_target)
neg_map = {entities[count][i]: i for i in range(len(entities[count]))}
proj_policy = self.project_policy(batch[count], policy_np[count],neg_map, is_target)
samples = np.random.choice(entities[count],1,p=proj_policy)
samples_idx = [neg_map[s] for s in samples]
#Update arms
rewards = self.compute_reward(samples,samples_idx,scores[count,0].data.cpu().numpy(),scores[count,1:].data.cpu().numpy())
if len(rewards.keys()) >0:
for ind,s in enumerate(rewards.keys()):
assert s not in positives
loss += policy[count,neg_map[s]]*Variable(torch.from_numpy(np.asarray([rewards[s]],dtype='float32')),requires_grad=False)
return loss
def compute_reward(self,actions,actions_idx,pos_score,scores):
scores_map = {a:-1.*scores[actions_idx[i]] for i,a in enumerate(actions)}
scores_map['pos'] = -pos_score
sorted_scores = sorted(scores_map.items(), key=operator.itemgetter(1))
rewards = dict()
count = 0
for a,s in sorted_scores:
if a=='pos':
rewards = {a: rewards[a] - count for a in rewards.keys()}
for a in rewards:
self.arms[a] = self.arms.get(a,0.0) - rewards[a]
return rewards
rewards[a] = count
count += 1
def project_policy(self,ex,policy,ent_map,is_target):
positives = self.ns.pos(ex, is_target)
for p in positives:
if p in ent_map:
policy[ent_map[p]] = 0.0
return policy / np.sum(policy)
def pad(self,samples,val):
if len(samples)<self.ns.num_samples:
padding = [val] * (self.ns.num_samples - len(samples))
samples.extend(padding)
return samples
def decay(self):
for a in self.arms:
self.arms[a] *= self.delta