# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # #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. #!/usr/bin/env python from six.moves import xrange # pylint: disable=redefined-builtin import re import math import time import numpy as np from datetime import datetime import reader import tensorflow as tf from tensorflow.python.ops import rnn FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""") tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""") tf.app.flags.DEFINE_integer('num_layers', 1, """Number of batches to run.""") tf.app.flags.DEFINE_integer('max_len', 100, """Number of batches to run.""") tf.app.flags.DEFINE_integer('hidden_size', 128, """Number of batches to run.""") tf.app.flags.DEFINE_integer('emb_size', 64, """Number of batches to run.""") tf.app.flags.DEFINE_boolean('log_device_placement', False, """Whether to log device placement.""") tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""") VOCAB_SIZE = 30000 NUM_CLASS = 2 NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 NUM_EPOCHS_PER_DECAY = 50 INITIAL_LEARNING_RATE = 0.1 LEARNING_RATE_DECAY_FACTOR = 0.1 TOWER_NAME = 'tower' train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE) def get_incoming_shape(incoming): """ Returns the incoming data shape """ if isinstance(incoming, tf.Tensor): return incoming.get_shape().as_list() elif type(incoming) in [np.array, list, tuple]: return np.shape(incoming) else: raise Exception("Invalid incoming layer.") # Note input * W is done in LSTMCell, # which is different from PaddlePaddle def single_lstm(name, incoming, n_units, use_peepholes=True, return_seq=False, return_state=False): with tf.name_scope(name) as scope: cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes) output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32) out = output if return_seq else output[-1] return (out, _cell_state) if return_state else out def lstm(name, incoming, n_units, use_peepholes=True, return_seq=False, return_state=False, num_layers=1): with tf.name_scope(name) as scope: lstm_cell = tf.nn.rnn_cell.LSTMCell( n_units, use_peepholes=use_peepholes) cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers) initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32) if not isinstance(incoming, list): # if the input is embeding, the Tensor shape : [None, time_step, emb_size] incoming = [ tf.squeeze(input_, [1]) for input_ in tf.split(1, FLAGS.max_len, incoming) ] outputs, state = tf.nn.rnn(cell, incoming, initial_state=initial_state, dtype=tf.float32) out = outputs if return_seq else outputs[-1] return (out, _cell_state) if return_state else out def embedding(name, incoming, vocab_size, emb_size): with tf.name_scope(name) as scope: #with tf.device("/cpu:0"): embedding = tf.get_variable( name + '_emb', [vocab_size, emb_size], dtype=tf.float32) out = tf.nn.embedding_lookup(embedding, incoming) return out def fc(name, inpOp, nIn, nOut, act=True): with tf.name_scope(name) as scope: kernel = tf.get_variable( name + '_w', [nIn, nOut], initializer=tf.truncated_normal_initializer( stddev=0.01, dtype=tf.float32), dtype=tf.float32) biases = tf.get_variable( name + '_b', [nOut], initializer=tf.constant_initializer( value=0.0, dtype=tf.float32), dtype=tf.float32, trainable=True) net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ tf.matmul(inpOp, kernel) + biases return net def inference(seq): net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size) print "emb:", get_incoming_shape(net) net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers) print "lstm:", get_incoming_shape(net) net = fc('fc1', net, FLAGS.hidden_size, 2) return net def loss(logits, labels): # one label index for one sample #labels = tf.cast(labels, tf.int64) # cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( # logits, labels, name='cross_entropy_per_example') labels = tf.cast(labels, tf.float32) cross_entropy = tf.nn.softmax_cross_entropy_with_logits( logits, labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) return tf.add_n(tf.get_collection('losses'), name='total_loss') def tower_loss(scope): """Calculate the total loss on a single tower running the model. Args: scope: unique prefix string identifying the tower, e.g. 'tower_0' Returns: Tensor of shape [] containing the total loss for a batch of data """ data, label = train_dataset.next_batch(FLAGS.batch_size) # Build a Graph that computes the logits predictions from the # inference model. last_layer = inference(data) # Build the portion of the Graph calculating the losses. Note that we will # assemble the total_loss using a custom function below. #_ = loss(last_layer, label) _ = loss(last_layer, label) # Assemble all of the losses for the current tower only. losses = tf.get_collection('losses', scope) # Calculate the total loss for the current tower. total_loss = tf.add_n(losses, name='total_loss') # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name) # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.scalar_summary(loss_name + ' (raw)', l) #tf.scalar_summary(loss_name, loss_averages.average(l)) with tf.control_dependencies([loss_averages_op]): total_loss = tf.identity(total_loss) return total_loss def average_gradients(tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(0, grads) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def time_tensorflow_run(session, target): num_steps_burn_in = 80 total_duration = 0.0 total_duration_squared = 0.0 for i in xrange(FLAGS.num_batches + num_steps_burn_in): start_time = time.time() _ = session.run(target, feed_dict={x_input: data, y_input: label}) _, loss_value = session.run(target) duration = time.time() - start_time if i > num_steps_burn_in: if not i % 10: num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus examples_per_sec = num_examples_per_step / duration # sec_per_batch = duration / FLAGS.num_gpus sec_per_batch = duration format_str = ( '%s: step %d, loss= %.2f (%.1f examples/sec; %.3f ' 'sec/batch batch_size= %d)') print(format_str % (datetime.now(), i - num_steps_burn_in, loss_value, duration, sec_per_batch, num_examples_per_step)) total_duration += duration total_duration_squared += duration * duration mn = total_duration / FLAGS.num_batches vr = total_duration_squared / FLAGS.num_batches - mn * mn sd = math.sqrt(vr) print('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now(), FLAGS.num_batches, mn, sd)) def run_benchmark(): with tf.Graph().as_default(), tf.device('/cpu:0'): # Create a variable to count the number of train() calls. This equals the # number of batches processed * FLAGS.num_gpus. global_step = tf.get_variable( 'global_step', [], initializer=tf.constant_initializer(0), trainable=False) # Calculate the learning rate schedule. num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size) decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) # Create an optimizer that performs gradient descent. opt = tf.train.AdamOptimizer(0.001) #train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE) # Calculate the gradients for each model tower. tower_grads = [] for i in xrange(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: # Calculate the loss for one tower of the model. This function # constructs the entire model but shares the variables across # all towers. loss = tower_loss(scope) # Reuse variables for the next tower. tf.get_variable_scope().reuse_variables() # Retain the summaries from the final tower. # summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope) # Calculate the gradients for the batch of data on this tower. grads = opt.compute_gradients(loss) # Keep track of the gradients across all towers. tower_grads.append(grads) # We must calculate the mean of each gradient. Note that this is the # synchronization point across all towers. grads = average_gradients(tower_grads) # Apply the gradients to adjust the shared variables. apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) # Group all updates to into a single train op. train_op = tf.group(apply_gradient_op) # Build an initialization operation. init = tf.initialize_all_variables() # Start running operations on the Graph. allow_soft_placement must be set to # True to build towers on GPU, as some of the ops do not have GPU # implementations. sess = tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=FLAGS.log_device_placement)) sess.run(init) time_tensorflow_run(sess, [train_op, loss]) def main(_): run_benchmark() if __name__ == '__main__': tf.app.run()