Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements; and to You under the Apache License, Version 2.0.
In [1]:
from __future__ import division
from __future__ import print_function
from future import standard_library
standard_library.install_aliases()
from builtins import zip
from builtins import range
from builtins import object
from past.utils import old_div
import pickle as pickle
import numpy as np
import argparse
import sys
from tqdm import tnrange, tqdm_notebook
# sys.path.append(os.path.join(os.path.dirname(__file__), '../../build/python'))
from singa import layer
from singa import loss
from singa import device
from singa import tensor
from singa import optimizer
from singa import initializer
from singa.proto import model_pb2
from singa import utils
In [2]:
class Data(object):
def __init__(self, fpath, batch_size=32, seq_length=100, train_ratio=0.8):
'''Data object for loading a plain text file.
Args:
fpath, path to the text file.
train_ratio, split the text file into train and test sets, where
train_ratio of the characters are in the train set.
'''
self.raw_data = open(fpath, 'r').read() # read text file
chars = list(set(self.raw_data))
self.vocab_size = len(chars)
self.char_to_idx = {ch: i for i, ch in enumerate(chars)}
self.idx_to_char = {i: ch for i, ch in enumerate(chars)}
data = [self.char_to_idx[c] for c in self.raw_data]
# seq_length + 1 for the data + label
nsamples = old_div(len(data), (1 + seq_length))
data = data[0:nsamples * (1 + seq_length)]
data = np.asarray(data, dtype=np.int32)
data = np.reshape(data, (-1, seq_length + 1))
# shuffle all sequences
np.random.shuffle(data)
self.train_dat = data[0:int(data.shape[0]*train_ratio)]
self.num_train_batch = old_div(self.train_dat.shape[0], batch_size)
self.val_dat = data[self.train_dat.shape[0]:]
self.num_test_batch = old_div(self.val_dat.shape[0], batch_size)
self.batch_size = batch_size
self.seq_length = seq_length
print('train dat', self.train_dat.shape)
print('val dat', self.val_dat.shape)
def numpy2tensors(npx, npy, dev):
'''batch, seq, dim -- > seq, batch, dim'''
tmpx = np.swapaxes(npx, 0, 1)
tmpy = np.swapaxes(npy, 0, 1)
inputs = []
labels = []
for t in range(tmpx.shape[0]):
x = tensor.from_numpy(tmpx[t])
y = tensor.from_numpy(tmpy[t])
x.to_device(dev)
y.to_device(dev)
inputs.append(x)
labels.append(y)
return inputs, labels
def convert(batch, batch_size, seq_length, vocab_size, dev):
'''convert a batch of data into a sequence of input tensors'''
y = batch[:, 1:]
x1 = batch[:, :seq_length]
x = np.zeros((batch_size, seq_length, vocab_size), dtype=np.float32)
for b in range(batch_size):
for t in range(seq_length):
c = x1[b, t]
x[b, t, c] = 1
return numpy2tensors(x, y, dev)
Prepare the dataset. Download all works of Shakespeare concatenated. Other plain text files can also be used.
In [3]:
def get_lr(epoch):
return old_div(0.001, float(1 << (old_div(epoch, 50))))
hidden_size=32
num_stacks=1
dropout=0.5
data = Data('static/shakespeare_input.txt')
# SGD with L2 gradient normalization
opt = optimizer.RMSProp(constraint=optimizer.L2Constraint(5))
cuda = device.create_cuda_gpu()
rnn = layer.LSTM(name='lstm', hidden_size=hidden_size, num_stacks=num_stacks, dropout=dropout, input_sample_shape=(data.vocab_size,))
rnn.to_device(cuda)
rnn_w = rnn.param_values()[0]
rnn_w.uniform(-0.08, 0.08)
dense = layer.Dense('dense', data.vocab_size, input_sample_shape=(32,))
dense.to_device(cuda)
dense_w = dense.param_values()[0]
dense_b = dense.param_values()[1]
print('dense w ', dense_w.shape)
print('dense b ', dense_b.shape)
initializer.uniform(dense_w, dense_w.shape[0], 0)
print('dense weight l1 = %f' % (dense_w.l1()))
dense_b.set_value(0)
print('dense b l1 = %f' % (dense_b.l1()))
g_dense_w = tensor.Tensor(dense_w.shape, cuda)
g_dense_b = tensor.Tensor(dense_b.shape, cuda)
In [4]:
lossfun = loss.SoftmaxCrossEntropy()
train_loss = 0
for epoch in range(3):
bar = tnrange(data.num_train_batch, desc='Epoch %d' % 0)
for b in bar:
batch = data.train_dat[b * data.batch_size: (b + 1) * data.batch_size]
inputs, labels = convert(batch, data.batch_size, data.seq_length, data.vocab_size, cuda)
inputs.append(tensor.Tensor())
inputs.append(tensor.Tensor())
outputs = rnn.forward(model_pb2.kTrain, inputs)[0:-2]
grads = []
batch_loss = 0
g_dense_w.set_value(0.0)
g_dense_b.set_value(0.0)
for output, label in zip(outputs, labels):
act = dense.forward(model_pb2.kTrain, output)
lvalue = lossfun.forward(model_pb2.kTrain, act, label)
batch_loss += lvalue.l1()
grad = lossfun.backward()
grad /= data.batch_size
grad, gwb = dense.backward(model_pb2.kTrain, grad)
grads.append(grad)
g_dense_w += gwb[0]
g_dense_b += gwb[1]
# print output.l1(), act.l1()
bar.set_postfix(train_loss=old_div(batch_loss, data.seq_length))
train_loss += batch_loss
grads.append(tensor.Tensor())
grads.append(tensor.Tensor())
g_rnn_w = rnn.backward(model_pb2.kTrain, grads)[1][0]
dense_w, dense_b = dense.param_values()
opt.apply_with_lr(epoch, get_lr(epoch), g_rnn_w, rnn_w, 'rnnw')
opt.apply_with_lr(epoch, get_lr(epoch), g_dense_w, dense_w, 'dense_w')
opt.apply_with_lr(epoch, get_lr(epoch), g_dense_b, dense_b, 'dense_b')
print('\nEpoch %d, train loss is %f' % (epoch, train_loss / data.num_train_batch / data.seq_length))
In [5]:
model_path= 'static/model_' + str(epoch) + '.bin'
with open(model_path, 'wb') as fd:
print('saving model to %s' % model_path)
d = {}
for name, w in zip(['rnn_w', 'dense_w', 'dense_b'],[rnn_w, dense_w, dense_b]):
d[name] = tensor.to_numpy(w)
d['idx_to_char'] = data.idx_to_char
d['char_to_idx'] = data.char_to_idx
d['hidden_size'] = hidden_size
d['num_stacks'] = num_stacks
d['dropout'] = dropout
pickle.dump(d, fd)
fd.close()
In [6]:
nsamples = 300
seed_text = "Before we proceed any further, hear me speak."
do_sample = True
with open(model_path, 'rb') as fd:
d = pickle.load(fd)
rnn_w = tensor.from_numpy(d['rnn_w'])
idx_to_char = d['idx_to_char']
char_to_idx = d['char_to_idx']
vocab_size = len(idx_to_char)
dense_w = tensor.from_numpy(d['dense_w'])
dense_b = tensor.from_numpy(d['dense_b'])
hidden_size = d['hidden_size']
num_stacks = d['num_stacks']
dropout = d['dropout']
rnn = layer.LSTM(name='lstm', hidden_size=hidden_size,
num_stacks=num_stacks, dropout=dropout,
input_sample_shape=(len(idx_to_char),))
rnn.to_device(cuda)
rnn.param_values()[0].copy_data(rnn_w)
dense = layer.Dense('dense', vocab_size, input_sample_shape=(hidden_size,))
dense.to_device(cuda)
dense.param_values()[0].copy_data(dense_w)
dense.param_values()[1].copy_data(dense_b)
hx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
cx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
hx.set_value(0.0)
cx.set_value(0.0)
if len(seed_text) > 0:
for c in seed_text:
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, char_to_idx[c]] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(model_pb2.kEval, inputs)
y = dense.forward(model_pb2.kEval, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
sys.stdout.write(seed_text)
else:
y = tensor.Tensor((1, vocab_size), cuda)
y.set_value(old_div(1.0, vocab_size))
for i in range(nsamples):
y.to_host()
prob = tensor.to_numpy(y)[0]
if do_sample:
cur = np.random.choice(vocab_size, 1, p=prob)[0]
else:
cur = np.argmax(prob)
sys.stdout.write(idx_to_char[cur])
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, cur] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(model_pb2.kEval, inputs)
y = dense.forward(model_pb2.kEval, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
print('')
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