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#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from parlai.core.agents import Agent
from parlai.core.dict import DictionaryAgent
from parlai.core.utils import PaddingUtils, round_sigfigs
from parlai.core.thread_utils import SharedTable
from .modules import RNNModel
import torch
from torch.autograd import Variable
import torch.nn as nn
import os
import math
import json
class LanguageModelAgent(Agent):
""" Agent which trains an RNN on a language modeling task.
It is adapted from the language model featured in Pytorch's examples repo
here: <>.
def dictionary_class():
return DictionaryAgent
def add_cmdline_args(argparser):
"""Add command-line arguments specifically for this agent."""
agent = argparser.add_argument_group('Language Model Arguments')
agent.add_argument('--init-model', type=str, default=None,
help='load dict/features/weights/opts from this file')
agent.add_argument('-hs', '--hiddensize', type=int, default=200,
help='size of the hidden layers')
agent.add_argument('-esz', '--embeddingsize', type=int, default=200,
help='size of the token embeddings')
agent.add_argument('-nl', '--numlayers', type=int, default=2,
help='number of hidden layers')
agent.add_argument('-dr', '--dropout', type=float, default=0.2,
help='dropout rate')
agent.add_argument('-clip', '--gradient-clip', type=float, default=0.25,
help='gradient clipping')
agent.add_argument('--no-cuda', action='store_true', default=False,
help='disable GPUs even if available')
agent.add_argument('-rnn', '--rnn-class', default='LSTM',
help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)')
agent.add_argument('-sl', '--seq-len', type=int, default=35,
help='sequence length')
agent.add_argument('-tied', '--emb-tied', action='store_true',
help='tie the word embedding and softmax weights')
agent.add_argument('-seed', '--random-seed', type=int, default=1111,
help='random seed')
agent.add_argument('--gpu', type=int, default=-1,
help='which GPU device to use')
agent.add_argument('-tr', '--truncate-pred', type=int, default=50,
help='truncate predictions')
agent.add_argument('-rf', '--report-freq', type=float, default=0.1,
help='report frequency of prediction during eval')
agent.add_argument('-pt', '--person-tokens', type='bool', default=True,
help='append person1 and person2 tokens to text')
# learning rate parameters
agent.add_argument('-lr', '--learningrate', type=float, default=20,
help='initial learning rate')
agent.add_argument('-lrf', '--lr-factor', type=float, default=1.0,
help='mutliply learning rate by this factor when the \
validation loss does not decrease')
agent.add_argument('-lrp', '--lr-patience', type=int, default=10,
help='wait before decreasing learning rate')
agent.add_argument('-lrm', '--lr-minimum', type=float, default=0.1,
help='minimum learning rate')
agent.add_argument('-sm', '--sampling-mode', type='bool', default=False,
help='sample when generating tokens instead of taking \
the max and do not produce UNK token (when bs=1)')
return agent
def __init__(self, opt, shared=None):
"""Set up model if shared params not set, otherwise no work to do."""
super().__init__(opt, shared)
opt = self.opt # there is a deepcopy in the init
self.metrics = {
'loss': 0,
'num_tokens': 0,
'lmloss': 0,
'lm_num_tokens': 0
self.states = {}
# check for cuda
self.use_cuda = not opt.get('no_cuda') and torch.cuda.is_available()
self.batchsize = opt.get('batchsize', 1)
self.use_person_tokens = opt.get('person_tokens', True)
self.sampling_mode = opt.get('sampling_mode', False)
if shared:
# set up shared properties
self.opt = shared['opt']
opt = self.opt
self.dict = shared['dict']
self.model = shared['model']
self.metrics = shared['metrics']
# get NULL token and END token
self.NULL_IDX = self.dict[self.dict.null_token]
self.END_IDX = self.dict[self.dict.end_token]
if 'states' in shared:
self.states = shared['states']
if self.use_person_tokens:
# add person1 and person2 tokens
# this is not a shared instance of this class, so do full init
if self.use_cuda:
print('[ Using CUDA ]')
init_model = None
# check first for 'init_model' for loading model from file
if opt.get('init_model') and os.path.isfile(opt['init_model']):
init_model = opt['init_model']
# next check for 'model_file', this would override init_model
if opt.get('model_file') and os.path.isfile(opt['model_file']):
init_model = opt['model_file']
# for backwards compatibility: will only be called for older models
# for which .opt file does not exist
if (init_model is not None and
not os.path.isfile(init_model + '.opt')):
new_opt = self.load_opt(init_model)
# load model parameters if available
print('[ Setting opt from {} ]'.format(
# since .opt file does not exist, save one for future use
print("Saving opt file at:", init_model + ".opt")
with open(init_model + '.opt', 'w') as handle:
json.dump(new_opt, handle)
opt = self.override_opt(new_opt)
if ((init_model is not None and
os.path.isfile(init_model + '.dict')) or
opt['dict_file'] is None):
opt['dict_file'] = init_model + '.dict'
# load dictionary and basic tokens & vectors
self.dict = DictionaryAgent(opt) = 'LanguageModel'
# get NULL token and END token
self.NULL_IDX = self.dict[self.dict.null_token]
self.END_IDX = self.dict[self.dict.end_token]
if self.use_person_tokens:
# add person1 and person2 tokens
# set model
self.model = RNNModel(opt, len(self.dict))
if init_model is not None:
if self.use_cuda:
self.next_observe = []
self.next_batch = []
self.is_training = True
self.clip = opt.get('gradient_clip', 0.25)
# set up criteria
self.criterion = nn.CrossEntropyLoss(ignore_index=self.NULL_IDX,
if self.use_cuda:
# push to cuda
# init hidden state
self.hidden = self.model.init_hidden(self.batchsize)
# init tensor of end tokens
self.ends = torch.LongTensor([self.END_IDX for _ in range(self.batchsize)])
if self.use_cuda:
self.ends = self.ends.cuda()
# set up model and learning rate scheduler parameters = opt['learningrate']
self.optimizer = torch.optim.SGD(self.model.parameters(),
self.best_val_loss = self.states.get('best_val_loss', None)
self.lr_factor = opt['lr_factor']
if self.lr_factor < 1.0:
self.lr_patience = opt['lr_patience']
self.lr_min = opt['lr_minimum']
self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, factor=self.lr_factor, verbose=True,
patience=self.lr_patience, min_lr=self.lr_min)
# initial step for scheduler if self.best_val_loss is initialized
if self.best_val_loss is not None:
self.scheduler = None
def override_opt(self, new_opt):
"""Set overridable opts from loaded opt file.
Print out each added key and each overriden key.
Only override args specific to the model.
model_args = {'hiddensize', 'embeddingsize', 'numlayers', 'dropout',
'seq_len', 'emb_tied', 'truncate_pred', 'report_freq',
'person_tokens', 'learningrate'}
for k, v in new_opt.items():
if k not in model_args:
# skip non-model args
if k not in self.opt:
print('Adding new option [ {k}: {v} ]'.format(k=k, v=v))
elif self.opt[k] != v:
print('Overriding option [ {k}: {old} => {v}]'.format(
k=k, old=self.opt[k], v=v))
self.opt[k] = v
return self.opt
def parse(self, text):
"""Convert string to token indices."""
return self.dict.txt2vec(text)
def zero_grad(self):
"""Zero out optimizer."""
def update_params(self):
"""Do one optimization step."""
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
def reset(self):
"""Reset observation and episode_done."""
self.observation = None
def reset_metrics(self):
self.metrics['loss'] = 0
self.metrics['lmloss'] = 0
self.metrics['num_tokens'] = 0
self.metrics['lm_num_tokens'] = 0
def report(self):
m = {}
if self.metrics['num_tokens'] > 0:
m['loss'] = self.metrics['loss'] / self.metrics['num_tokens']
m['ppl'] = math.exp(m['loss'])
if self.metrics['lm_num_tokens'] > 0:
m['lmloss'] = self.metrics['lmloss'] / self.metrics['lm_num_tokens']
m['lmppl'] = math.exp(m['lmloss'])
for k, v in m.items():
# clean up: rounds to sigfigs and converts tensors to floats
m[k] = round_sigfigs(v, 4)
return m
def share(self):
"""Share internal states between parent and child instances."""
shared = super().share()
shared['opt'] = self.opt
shared['dict'] = self.dict
shared['NULL_IDX'] = self.NULL_IDX
shared['END_IDX'] = self.END_IDX
shared['model'] = self.model
if self.opt.get('numthreads', 1) > 1:
if type(self.metrics) == dict:
# move metrics and model to shared memory
self.metrics = SharedTable(self.metrics)
shared['states'] = { # only need to pass optimizer states
'optimizer': self.optimizer.state_dict(),
shared['metrics'] = self.metrics
return shared
def observe(self, observation):
"""Save observation for act.
If multiple observations are from the same episode, concatenate them.
# shallow copy observation (deep copy can be expensive)
obs = observation.copy()
seq_len = self.opt['seq_len']
is_training = True
if 'labels' not in obs:
is_training = False
if is_training:
if 'text' in obs:
if self.use_person_tokens:
obs['text'] = 'PERSON1 ' + obs['text']
vec = self.parse(obs['text'])
self.next_observe += vec
if 'labels' in obs:
if self.use_person_tokens:
labels = [
'PERSON2 ' + label
for label in obs['labels']
if label != ''
obs['labels'] = tuple(labels)
vec = self.parse(obs['labels'][0])
self.next_observe += vec
if len(self.next_observe) < (seq_len + 1):
# not enough to return to make a batch
# we handle this case in vectorize
# labels indicates that we are training
self.observation = {'labels': ''}
return self.observation
vecs_to_return = []
total = len(self.next_observe) // (seq_len + 1)
for _ in range(total):
observe = self.next_observe[:(seq_len + 1)]
self.next_observe = self.next_observe[(seq_len + 1):]
dict_to_return = {'text': '', 'labels': '', 'text2vec': vecs_to_return}
self.observation = dict_to_return
return dict_to_return
if 'text' in obs:
if self.use_person_tokens:
obs['text'] = 'PERSON1 ' + obs['text']
if 'eval_labels' in obs:
if self.use_person_tokens:
eval_labels = [
'PERSON2 ' + label
for label in obs['eval_labels']
if label != ''
obs['eval_labels'] = tuple(eval_labels)
self.observation = obs
return obs
def repackage_hidden(self, h):
"""Wraps hidden states in new Variables, to detach them from their history."""
if isinstance(h, Variable):
return Variable(
return tuple(self.repackage_hidden(v) for v in h)
def get_target_loss(self, data, hidden, targets):
"""Calculates the loss with respect to the targets, token by token,
where each output token is conditioned on either the input or the
previous target token.
loss = 0.0
bsz = data.size(0)
# during interactive mode, when no targets exist, we return 0
if targets is None:
return loss
# feed in inputs without end token
output, hidden = self.model(data.transpose(0, 1), hidden)
self.hidden = self.repackage_hidden(hidden)
# feed in end tokens
output, hidden = self.model(Variable(self.ends[:bsz].view(1, bsz)), self.hidden)
self.hidden = self.repackage_hidden(hidden)
output_flat = output.view(-1, len(self.dict))
loss += self.criterion(output_flat,, 0).view(-1)).data
for i in range(1, targets.size(1)):
output, hidden = self.model(, i - 1).view(1, bsz),
self.hidden = self.repackage_hidden(hidden)
output_flat = output.view(-1, len(self.dict))
loss += self.criterion(output_flat,, i).view(-1)).data
return loss
def get_predictions(self, data):
"""Generates predictions word by word until we either reach the end token
or some max length (opt['truncate_pred']).
token_list = []
bsz = data.size(0)
done = [False for _ in range(bsz)]
total_done = 0
hidden = self.model.init_hidden(bsz)
i = 0
word_idx = None
while total_done < bsz and i <= self.opt['truncate_pred']:
if i == 0:
# feed in input without end tokens
output, hidden = self.model(data.transpose(0, 1), hidden)
hidden = self.repackage_hidden(hidden)
# feed in end tokens
output, hidden = self.model(
Variable(self.ends[:bsz].view(1, bsz)), hidden
output, hidden = self.model(
Variable(word_idx.view(1, bsz)), hidden, no_pack=True
hidden = self.repackage_hidden(hidden)
word_weights = output.squeeze().data.exp()
if bsz > 1:
_, word_idx = torch.max(word_weights, 1)
if self.sampling_mode:
unk_idx = self.dict[self.dict.unk_token]
# make word_weights have smaller norm so that calculated
# norm does not blow up
word_weights = word_weights.div(1e10)
# make word_weights have L2 norm 1
ww_norm = torch.norm(word_weights, p=2)
word_weights = word_weights.div(ww_norm)
# square distribution
word_weights = torch.mul(word_weights, word_weights)
# sample distribution
word_idx = torch.multinomial(word_weights, 1)
# do not produce UNK token
while word_idx == unk_idx:
word_idx = torch.multinomial(word_weights, 1)
_, word_idx = torch.max(word_weights, 0)
# mark end indices for items in batch
word_idx = word_idx.view(-1)
for k in range(word_idx.size(0)):
if not done[k]:
if int(word_idx[k]) == self.END_IDX:
done[k] = True
total_done += 1
token_list.append(word_idx.view(bsz, 1))
i += 1
return, 1)
def predict(self, data, hidden, targets=None, is_training=True, y_lens=None):
"""Produce a prediction from our model."""
output = None
predictions = None
if is_training:
output, hidden = self.model(data, hidden)
loss = self.criterion(output.view(-1, len(self.dict)), targets.view(-1))
# save loss to metrics
target_tokens =
self.metrics['lmloss'] += loss.double().item()
self.metrics['lm_num_tokens'] += target_tokens
# average loss per token
loss /= target_tokens
predictions = self.get_predictions(data)
bsz = data.size(0)
if bsz != self.batchsize:
self.hidden = self.model.init_hidden(bsz)
if targets is not None:
loss = self.get_target_loss(data, self.hidden, targets)
self.metrics['loss'] += loss
self.metrics['num_tokens'] += sum(y_lens)
return output, hidden, predictions
def vectorize(self, observations, seq_len, is_training):
"""Convert a list of observations into input & target tensors."""
labels = None
valid_inds = None
y_lens = None
if is_training:
for obs in observations:
if obs:
if 'text2vec' in obs:
self.next_batch += obs['text2vec']
if len(self.next_batch) <= self.batchsize:
return None, None, None, None, None
data_list = []
targets_list = []
# total is the number of batches
total = len(self.next_batch) // self.batchsize
for _ in range(total):
batch = self.next_batch[:self.batchsize]
self.next_batch = self.next_batch[self.batchsize:]
source = torch.LongTensor(batch).t().contiguous()
data = Variable(source[:seq_len])
targets = Variable(source[1:])
if self.use_cuda:
data = data.cuda()
targets = targets.cuda()
# here we get valid examples and pad them with zeros
xs, ys, labels, valid_inds, _, y_lens = PaddingUtils.pad_text(
observations, self.dict, end_idx=self.END_IDX,
if self.use_cuda:
if xs is not None:
xs = Variable(torch.LongTensor(xs)).cuda()
if ys is not None:
ys = Variable(torch.LongTensor(ys)).cuda()
if xs is not None:
xs = Variable(torch.LongTensor(xs))
if ys is not None:
ys = Variable(torch.LongTensor(ys))
data_list = [xs]
targets_list = [ys]
return data_list, targets_list, labels, valid_inds, y_lens
def batch_act(self, observations):
batch_reply = [{'id': self.getID()} for _ in range(len(observations))]
if any(['labels' in obs for obs in observations]):
# if we are starting a new training epoch, reinitialize hidden
if not self.is_training:
self.hidden = self.model.init_hidden(self.batchsize)
self.is_training = True
data_list, targets_list, _c, _v, y_lens = self.vectorize(
observations, self.opt['seq_len'], self.is_training
# if we just finished training, reinitialize hidden
if self.is_training:
self.hidden = self.model.init_hidden(self.batchsize)
self.is_training = False
data_list, targets_list, labels, valid_inds, y_lens = self.vectorize(
observations, self.opt['seq_len'], self.is_training
if data_list is None:
# not enough data to batch act yet, return empty responses
return batch_reply
batch_reply = []
# during evaluation, len(data_list) is always 1
# during training, len(dat_list) >= 0: vectorize returns a list
# containing all batches available at the time it is called
for i in range(len(data_list)):
temp_dicts = [{'id': self.getID()} for _ in range(len(observations))]
# ignore case when we do not return any valid indices
if data_list[i] is not None:
output, hidden, predictions = self.predict(
data_list[i], self.hidden, targets_list[i],
self.is_training, y_lens
self.hidden = self.repackage_hidden(hidden)
if predictions is not None:
# map predictions back to the right order
predictions.cpu(), valid_inds, temp_dicts, observations,
self.dict, self.END_IDX, report_freq=self.opt['report_freq'])
batch_reply += temp_dicts
# for prediction metrics computations, we get rid of PERSON1 and PERSON2 tokens
if not self.is_training:
for reply in batch_reply:
if 'text' in reply:
reply['text'] = reply['text'].replace('PERSON1 ', '')
reply['text'] = reply['text'].replace('PERSON2 ', '')
return batch_reply
def act(self):
# call batch_act with this batch of one
return self.batch_act([self.observation])[0]
def save(self, path=None):
"""Save model parameters if model_file is set."""
path = self.opt.get('model_file', None) if path is None else path
if path and hasattr(self, 'model'):
model = {}
model['model'] = self.model.state_dict()
model['opt'] = self.opt
model['best_val_loss'] = self.best_val_loss
with open(path, 'wb') as write:, write)
# save opt file
with open(path + '.opt', 'w') as handle:
json.dump(self.opt, handle)
def shutdown(self):
"""Save the state of the model when shutdown."""
path = self.opt.get('model_file', None)
if path is not None: + '.shutdown_state')
def receive_metrics(self, metrics_dict):
if 'loss' in metrics_dict and self.scheduler is not None:
def load_opt(self, path):
"""Return opt, states."""
states = torch.load(path, map_location=lambda cpu, _: cpu)
return states['opt']
def load(self, path):
"""Load model states."""
if os.path.isfile(path):
# load model parameters if available
print('[ Loading existing model params from {} ]'.format(path))
self.states = torch.load(path, map_location=lambda cpu, _: cpu)