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Commit 90d6e3b9 authored by Lukasz Kaiser's avatar Lukasz Kaiser
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Cleaned-up version of the Neural GPU code (runs outside of google3).

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neural_gpu/README.md
View file @ 90d6e3b9
......@@ -2,4 +2,10 @@
Code for the Neural GPU model as described
in [[http://arxiv.org/abs/1511.08228]].
Requirements:
* TensorFlow (see tensorflow.org for how to install)
* Matplotlib for Python (sudo apt-get install python-matplotlib)
Run: python neural_gpu_trainer.py --task=rev
Maintained by Lukasz Kaiser (lukaszkaiser)
neural_gpu/data_utils.py
View file @ 90d6e3b9
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==============================================================================
"""Convolutional Gated Recurrent Networks for Algorithm Learning."""
import math
......@@ -21,12 +5,10 @@ import random
import sys
import time
import google3
import numpy as np
import tensorflow as tf
from google3.third_party.tensorflow.python.platform import gfile
from tensorflow.python.platform import gfile
FLAGS = tf.app.flags.FLAGS
......@@ -162,6 +144,21 @@ def init_data(task, length, nbr_cases, nclass):
test_set[task][l].append([inp, target])
def to_symbol(i):
"""Covert ids to text."""
if i == 0: return ""
if i == 11: return "+"
if i == 12: return "*"
return str(i-1)
def to_id(s):
"""Covert text to ids."""
if s == "+": return 11
if s == "*": return 12
return int(s) + 1
def get_batch(max_length, batch_size, do_train, task, offset=None, preset=None):
"""Get a batch of data, training or testing."""
inputs = []
......
neural_gpu/neural_gpu.py
View file @ 90d6e3b9
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==============================================================================
"""The Neural GPU Model."""
import time
import google3
import tensorflow as tf
from google3.experimental.users.lukaszkaiser.neural_gpu import data_utils
import data_utils
def conv_linear(args, kw, kh, nin, nout, do_bias, bias_start, prefix):
......
neural_gpu/neural_gpu_trainer.py
View file @ 90d6e3b9
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==============================================================================
"""Neural GPU for Learning Algorithms."""
import math
......@@ -22,16 +6,15 @@ import random
import sys
import time
import google3
import matplotlib.animation as anim
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from google3.third_party.tensorflow.python.platform import gfile
import google3.experimental.users.lukaszkaiser.neural_gpu.data_utils as data
import google3.experimental.users.lukaszkaiser.neural_gpu.neural_gpu as ngpu
from tensorflow.python.platform import gfile
import data_utils as data
import neural_gpu
tf.app.flags.DEFINE_float("lr", 0.1, "Learning rate.")
tf.app.flags.DEFINE_float("init_weight", 1.0, "Initial weights deviation.")
......@@ -39,7 +22,7 @@ tf.app.flags.DEFINE_float("max_grad_norm", 0.05, "Clip gradients to this norm.")
tf.app.flags.DEFINE_float("cutoff", 1.2, "Cutoff at the gates.")
tf.app.flags.DEFINE_float("pull", 0.0005, "Starting pull of the relaxations.")
tf.app.flags.DEFINE_float("pull_incr", 1.2, "Increase pull by that much.")
tf.app.flags.DEFINE_float("dropout", 0.2, "Dropout that much.")
tf.app.flags.DEFINE_float("dropout", 0.15, "Dropout that much.")
tf.app.flags.DEFINE_float("grad_noise_scale", 1.0, "Gradient noise scale.")
tf.app.flags.DEFINE_integer("batch_size", 64, "Batch size.")
tf.app.flags.DEFINE_integer("low_batch_size", 16, "Low batch size.")
......@@ -63,6 +46,7 @@ tf.app.flags.DEFINE_string("task", "rev", "Which task are we learning?")
tf.app.flags.DEFINE_string("train_dir", "/tmp/", "Directory to store models.")
FLAGS = tf.app.flags.FLAGS
EXTRA_EVAL = 12
def initialize(sess):
......@@ -83,7 +67,7 @@ def initialize(sess):
min_length = 3
max_length = min(FLAGS.max_length, data.bins[-1])
assert max_length + 1 > min_length
while len(data.bins) > 1 and data.bins[-2] > max_length + 12:
while len(data.bins) > 1 and data.bins[-2] > max_length + EXTRA_EVAL:
data.bins = data.bins[:-1]
assert data.bins[0] > FLAGS.rx_step
nclass = min(FLAGS.niclass, FLAGS.noclass)
......@@ -92,7 +76,7 @@ def initialize(sess):
# Initialize data for each task.
tasks = FLAGS.task.split("-")
for t in tasks:
for l in xrange(max_length + 11):
for l in xrange(max_length + EXTRA_EVAL - 1):
data.init_data(t, l, data_size, nclass)
data.init_data(t, data.bins[-2], data_size, nclass)
data.init_data(t, data.bins[-1], data_size, nclass)
......@@ -101,14 +85,14 @@ def initialize(sess):
# Print out parameters.
curriculum = 0.12
fin = ("cv %d kw %d h %d kh %d rxr %d bs %d ns %.2f t %s"
% (FLAGS.nconvs, FLAGS.kw, FLAGS.height, FLAGS.kh, FLAGS.rx_step,
FLAGS.batch_size, FLAGS.grad_noise_scale, FLAGS.task))
fin = "data %d %s" % (FLAGS.train_data_size, fin)
tag = ("df %.2f p %.3f lr %.2f iw %.2f cr %.2f nm %d d%.4f gn %.2f %s" %
(FLAGS.cutoff, FLAGS.pull_incr, FLAGS.lr, FLAGS.init_weight,
curriculum, FLAGS.nmaps, FLAGS.dropout, FLAGS.max_grad_norm, fin))
data.print_out(tag)
msg1 = ("layers %d kw %d h %d kh %d relax %d batch %d noise %.2f task %s"
% (FLAGS.nconvs, FLAGS.kw, FLAGS.height, FLAGS.kh, FLAGS.rx_step,
FLAGS.batch_size, FLAGS.grad_noise_scale, FLAGS.task))
msg2 = "data %d %s" % (FLAGS.train_data_size, msg1)
msg3 = ("cut %.2f pull %.3f lr %.2f iw %.2f cr %.2f nm %d d%.4f gn %.2f %s" %
(FLAGS.cutoff, FLAGS.pull_incr, FLAGS.lr, FLAGS.init_weight,
curriculum, FLAGS.nmaps, FLAGS.dropout, FLAGS.max_grad_norm, msg2))
data.print_out(msg3)
# Create checkpoint directory if it does not exist.
checkpoint_dir = os.path.join(FLAGS.train_dir, "neural_gpu%s"
......@@ -120,7 +104,7 @@ def initialize(sess):
# Create model and initialize it.
tf.get_variable_scope().set_initializer(
tf.uniform_unit_scaling_initializer(factor=1.8 * FLAGS.init_weight))
model = ngpu.NeuralGPU(
model = neural_gpu.NeuralGPU(
FLAGS.nmaps, FLAGS.nmaps, FLAGS.niclass, FLAGS.noclass, FLAGS.dropout,
FLAGS.rx_step, FLAGS.max_grad_norm, FLAGS.cutoff, FLAGS.nconvs,
FLAGS.kw, FLAGS.kh, FLAGS.height, FLAGS.mode, FLAGS.lr,
......@@ -145,131 +129,148 @@ def single_test(l, model, sess, task, nprint, batch_size, print_out=True,
"""Test model on test data of length l using the given session."""
inpt, target = data.get_batch(l, batch_size, False, task, offset)
_, res, _, steps = model.step(sess, inpt, target, False)
errors, total, seq = data.accuracy(inpt, res, target, batch_size, nprint)
seq = float(seq) / batch_size
errors, total, seq_err = data.accuracy(inpt, res, target, batch_size, nprint)
seq_err = float(seq_err) / batch_size
if total > 0:
errors = float(errors) / total
if print_out:
data.print_out(" %s len %d errors %.2f sequence-errors %.2f"
% (task, l, 100*errors, 100*seq))
return errors, seq, (steps, inpt, [np.argmax(o, axis=1) for o in res])
% (task, l, 100*errors, 100*seq_err))
return errors, seq_err, (steps, inpt, [np.argmax(o, axis=1) for o in res])
def multi_test(l, model, sess, task, nprint, batch_size, offset=None):
"""Run multiple tests at lower batch size to save memory."""
errors = 0.0
seq = 0.0
errors, seq_err = 0.0, 0.0
to_print = nprint
low_batch = FLAGS.low_batch_size
low_batch = min(low_batch, batch_size)
for mstep in xrange(batch_size / low_batch):
cur_offset = None if offset is None else offset + mstep * low_batch
err, sq, _ = single_test(l, model, sess, task, to_print, low_batch, False,
cur_offset)
err, sq_err, _ = single_test(l, model, sess, task, to_print, low_batch,
False, cur_offset)
to_print = max(0, to_print - low_batch)
errors += err
seq += sq
seq_err += sq_err
if FLAGS.mode > 0:
cur_errors = float(low_batch * errors) / ((mstep+1) * low_batch)
cur_seq = float(low_batch * seq) / ((mstep+1) * low_batch)
cur_seq_err = float(low_batch * seq_err) / ((mstep+1) * low_batch)
data.print_out(" %s multitest current errors %.2f sequence-errors %.2f"
% (task, 100*cur_errors, 100*cur_seq))
% (task, 100*cur_errors, 100*cur_seq_err))
errors = float(low_batch) * float(errors) / batch_size
seq = float(low_batch) * float(seq) / batch_size
seq_err = float(low_batch) * float(seq_err) / batch_size
data.print_out(" %s len %d errors %.2f sequence-errors %.2f"
% (task, l, 100*errors, 100*seq))
return errors, seq
% (task, l, 100*errors, 100*seq_err))
return errors, seq_err
def train():
"""Main training function."""
"""Train the model."""
batch_size = FLAGS.batch_size
tasks = FLAGS.task.split("-")
with tf.Session() as sess:
model, min_length, max_length, checkpoint_dir, curriculum = initialize(sess)
max_cur_length = min(min_length + 3, max_length)
prev_acc_perp = [1000000 for _ in xrange(3)]
prev_sq = 1.0
prev_seq_err = 1.0
# Main traning loop.
while True:
global_step, pull, max_cur_length, learning_rate = sess.run(
[model.global_step, model.pull, model.cur_length, model.lr])
ep = global_step / FLAGS.steps_per_checkpoint
acc_loss, acc_total, acc_errors, acc_seq = 0.0, 0, 0, 0
acc_loss, acc_total, acc_errors, acc_seq_err = 0.0, 0, 0, 0
acc_grad_norm, step_count, step_time = 0.0, 0, 0.0
for _ in xrange(FLAGS.steps_per_checkpoint):
global_step += 1
task = random.choice(tasks)
l1 = np.random.randint(max_cur_length - min_length + 1) + min_length
l = l1
if np.random.randint(10) > 3: # Prefer longer stuff 60% of time.
l = np.random.randint(max_cur_length - min_length+1) + min_length
# Select the length for curriculum learning.
l = np.random.randint(max_cur_length - min_length + 1) + min_length
# Prefer longer stuff 60% of time.
if np.random.randint(100) < 60:
l1 = np.random.randint(max_cur_length - min_length+1) + min_length
l = max(l, l1)
if np.random.randint(4) < 1: # Mixed learning: once in a while big.
l = np.random.randint(max_length - min_length + 1) + min_length
# Mixed curriculum learning: in 25% of cases go to any larger length.
if np.random.randint(100) < 25:
l1 = np.random.randint(max_length - min_length + 1) + min_length
l = max(l, l1)
# Run a step and time it.
start_time = time.time()
inp, target = data.get_batch(l, batch_size, True, task)
stepp = math.pow(global_step, -0.55)
noise_param = math.sqrt(stepp * 20 * prev_sq) * FLAGS.grad_noise_scale
noise_param = math.sqrt(math.pow(global_step, -0.55) *
(20 * prev_seq_err)) * FLAGS.grad_noise_scale
loss, res, gnorm, _ = model.step(sess, inp, target, True, noise_param)
step_time += time.time() - start_time
acc_grad_norm += float(gnorm)
# Accumulate statistics only if we did not exceed curriculum length.
if l < max_cur_length + 1:
step_count += 1
acc_loss += loss
errors, total, seq = data.accuracy(inp, res, target,
batch_size, 0)
errors, total, seq_err = data.accuracy(inp, res, target,
batch_size, 0)
acc_total += total
acc_errors += errors
acc_seq += seq
acc_seq_err += seq_err
# Normalize and print out accumulated statistics.
acc_loss /= step_count
step_time /= FLAGS.steps_per_checkpoint
acc_seq = float(acc_seq) / (step_count * batch_size)
prev_sq = acc_seq
acc_seq_err = float(acc_seq_err) / (step_count * batch_size)
prev_seq_err = acc_seq_err
acc_errors = float(acc_errors) / acc_total if acc_total > 0 else 1.0
msg1 = "ep %d st %.2f lr %.8f" % (ep, step_time, learning_rate)
msg2 = "pl %.3f cme %.3f" % (pull, curriculum)
msg = ("%s %s gn %.8f"
% (msg1, msg2, acc_grad_norm / FLAGS.steps_per_checkpoint))
data.print_out("%s len %d ppx %.8f errs %.2f sq %.2f" %
(msg, max_cur_length, data.safe_exp(acc_loss),
100*acc_errors, 100*acc_seq))
if curriculum > acc_seq:
prev_acc_perp.append(1000000)
msg1 = "step %d step-time %.2f" % (global_step, step_time)
msg2 = "lr %.8f pull %.3f" % (learning_rate, pull)
msg3 = ("%s %s grad-norm %.8f"
% (msg1, msg2, acc_grad_norm / FLAGS.steps_per_checkpoint))
data.print_out("%s len %d ppx %.8f errors %.2f sequence-errors %.2f" %
(msg3, max_cur_length, data.safe_exp(acc_loss),
100*acc_errors, 100*acc_seq_err))
# If errors are below the curriculum threshold, move curriculum forward.
if curriculum > acc_seq_err:
# Increase current length (until the next with training data).
do_incr = True
while do_incr and max_cur_length < max_length:
sess.run(model.cur_length_incr_op)
for t in tasks:
if data.train_set[t]: do_incr = False
# Forget last perplexities if we're not yet at the end.
if max_cur_length < max_length:
prev_acc_perp.append(1000000)
# Either increase pull or, if it's large, average parameters.
if pull < 1:
sess.run(model.pull_incr_op)
else:
data.print_out(" Averaging parameters.")
sess.run([model.avg_op, model.lr_decay_op])
else:
acc_perp = data.safe_exp(acc_loss)
if acc_perp > max(prev_acc_perp[-3:]):
sess.run(model.lr_decay_op)
prev_acc_perp.append(acc_perp)
# Lower learning rate if we're worse than the last 3 checkpoints.
acc_perp = data.safe_exp(acc_loss)
if acc_perp > max(prev_acc_perp[-3:]):
sess.run(model.lr_decay_op)
prev_acc_perp.append(acc_perp)
# Save checkpoint.
checkpoint_path = os.path.join(checkpoint_dir, "neural_gpu.ckpt")
model.saver.save(sess, checkpoint_path,
global_step=model.global_step)
# Run evaluation.
should_exit = True
bound = data.bins[-1] + 1
for t in tasks:
l = min_length
while l < max_length + 12 and l < bound:
_, sq, _ = single_test(l, model, sess, t, FLAGS.nprint, batch_size)
while l < max_length + EXTRA_EVAL and l < bound:
_, seq_err, _ = single_test(l, model, sess, t,
FLAGS.nprint, batch_size)
l += 1
while l < bound + 1 and not data.test_set[t][l]:
l += 1
if sq < 0.5:
_, sq = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4)
if sq > 0.001: should_exit = False
if should_exit:
if seq_err < 0.5: # Run larger test if we're good enough.
_, seq_err = multi_test(data.forward_max, model, sess, t,
FLAGS.nprint, batch_size * 4)
if seq_err < 0.01: # Super-large test on 1-task large-forward models.
if data.forward_max > 4000 and len(tasks) == 1:
multi_test(data.forward_max, model, sess, tasks[0], FLAGS.nprint,
batch_size * 16, 0)
......@@ -277,14 +278,17 @@ def train():
def animate(l, test_data, anim_size):
"""Create animation for the given data (hacky matplotlib use)."""
xf = 12
fps = 2
xf = 12 # Extra frames to slow down at start and end.
fps = 2 # Frames per step.
# Make the figure.
fig = plt.figure(figsize=(16, 9), facecolor="white")
ax = fig.add_axes([0, 0, 1, 1], frameon=False, zorder=2)
ax.set_xticks([i * 24-0.5 for i in xrange(4)])
ax.set_xticklabels([])
ax.set_yticks([i - 0.5 for i in xrange(l+1)])
ax.grid(which="major", axis="both", linestyle="-", color="black")
# We need text fields.
text_fields = []
text_size = 24*32/l
for y in xrange(l):
......@@ -296,11 +300,8 @@ def animate(l, test_data, anim_size):
vmax=1.0, cmap="gray", aspect="auto", origin="upper",
interpolation="none", animated=True)
im.set_zorder(1)
def to_symbol(i):
if i == 0: return ""
if i == 11: return "+"
if i == 12: return "*"
return str(i-1)
# Main animation step.
def animation_update(frame_no, test_data, xf, im, text_fields):
"""Update an animation frame."""
steps, inpt, out_raw = test_data
......@@ -319,15 +320,17 @@ def animate(l, test_data, anim_size):
if index - 2*xf < length:
t.set_text("")
else:
t.set_text(to_symbol(out[i]))
t.set_text(data.to_symbol(out[i]))
else:
for i, t in enumerate(text_fields):
t.set_text(to_symbol(inpt[i][batch]) if index < xf else "")
t.set_text(data.to_symbol(inpt[i][batch]) if index < xf else "")
if index < xf:
im.set_array(np.zeros_like(steps[0][0]))
else:
im.set_array(steps[0][batch])
return im,
# Create the animation and save to mp4.
animation = anim.FuncAnimation(
fig, animation_update, blit=True, frames=(l+4*xf)*anim_size*fps,
interval=500/fps, fargs=(test_data, xf, im, text_fields))
......@@ -343,8 +346,8 @@ def evaluate():
bound = data.bins[-1] + 1
for t in tasks:
l = min_length
while l < max_length + 12 and l < bound:
_, sq, _ = single_test(l, model, sess, t, FLAGS.nprint, batch_size)
while l < max_length + EXTRA_EVAL and l < bound:
_, seq_err, _ = single_test(l, model, sess, t, FLAGS.nprint, batch_size)
l += 1
while l < bound + 1 and not data.test_set[t][l]:
l += 1
......@@ -353,9 +356,9 @@ def evaluate():
_, _, test_data = single_test(l, model, sess, t, 0, anim_size)
animate(l, test_data, anim_size)
# More tests.
_, sq = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4)
if sq < 0.01: # More tests.
_, seq_err = multi_test(data.forward_max, model, sess, t, FLAGS.nprint,
batch_size * 4)
if seq_err < 0.01: # Super-test if we're very good and in large-test mode.
if data.forward_max > 4000 and len(tasks) == 1:
multi_test(data.forward_max, model, sess, tasks[0], FLAGS.nprint,
batch_size * 64, 0)
......@@ -365,16 +368,18 @@ def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [int(c) for c in inpt.strip()]
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
print " ".join([str(output[0]) for output in res])
res = [o for o in res[:len(ids)] if o > 0]
print " " + " ".join([data.to_symbol(output[0]) for output in res])
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
......
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