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Tensorflow Rotating GPU Buffer
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# Implementation of a rotating buffer on the GPU of size 2. | |
import threading | |
import tensorflow as tf | |
from tensorflow.python.client import timeline | |
import numpy as np | |
import time | |
params = { | |
'batch_size': 128, | |
'seg_len': 4000, | |
} | |
kernel_shape = [64, 8, 100] | |
graph = tf.Graph() | |
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False), graph=graph) | |
verbose = False | |
target_dev = '/gpu:0' | |
with graph.as_default(), tf.device('/cpu:0'): | |
# Variables | |
with tf.device(target_dev): | |
var_buffer_out = tf.Variable([[0]], validate_shape=False, dtype=tf.float32, trainable=False) | |
var_buffer_in = tf.Variable([0], validate_shape=False, dtype=tf.float32, trainable=False) | |
data_in = tf.placeholder(dtype=tf.float32, shape=[None, params['seg_len'], kernel_shape[1]]) | |
queue = tf.FIFOQueue(shapes=[[params['seg_len'], kernel_shape[1]]], | |
dtypes=[tf.float32], | |
capacity=2*params['batch_size'], | |
) | |
enqueue_op = queue.enqueue_many([data_in]) | |
dequeued_data_in = queue.dequeue_many(params['batch_size']) | |
with tf.device(target_dev): | |
move_buffer = tf.assign(var_buffer_out, var_buffer_in, validate_shape=False) | |
put_in_buffer = tf.assign(var_buffer_in, dequeued_data_in, validate_shape=False) | |
get_from_buffer = tf.identity(var_buffer_out) | |
with tf.variable_scope("conv1"): | |
Wz = tf.get_variable("Wz", initializer=np.zeros(kernel_shape, dtype=np.float32)+0.00001) | |
Z = tf.nn.conv1d(get_from_buffer, Wz, 1, 'SAME') | |
test_out = (tf.reduce_mean(Z) * 0) + tf.reduce_mean(get_from_buffer) | |
if verbose: | |
with tf.device('/cpu:0'): | |
test_out = tf.Print(test_out, [put_in_buffer, move_buffer, test_out], '[BUF_IN][BUF_OUT][RESULT]:') | |
with tf.control_dependencies([move_buffer]): | |
pull = tf.group(put_in_buffer, test_out) | |
def runThread(sess, coord): | |
i=0 | |
while not coord.should_stop(): | |
sess.run(enqueue_op, feed_dict={ | |
data_in: np.zeros([params['batch_size'], params['seg_len'], kernel_shape[1]], dtype=np.float32)+i | |
}) | |
i += 1 | |
sess.run(tf.global_variables_initializer()) | |
coord = tf.train.Coordinator() | |
threads = tf.train.start_queue_runners(sess=sess, coord=coord) | |
threads = [] | |
for n in range(1): | |
t = threading.Thread(target=runThread, args=(sess, coord)) | |
t.daemon = True | |
t.start() | |
threads.append(t) | |
# Put something in the input-buffer on the GPU. | |
sess.run([put_in_buffer]) | |
# Move to the output-side of the buffer on the GPU. | |
sess.run([move_buffer]) | |
# Now pull th | |
for i in range(20): | |
sess.run([pull]) | |
print('Running a trace..') | |
time.sleep(5) # Give the queue fetcher some time | |
run_metadata = tf.RunMetadata() | |
sess.run([pull], | |
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), | |
run_metadata=run_metadata | |
) | |
trace = timeline.Timeline(step_stats=run_metadata.step_stats) | |
trace_file = open('timeline.ctf.json', 'w') | |
trace_file.write(trace.generate_chrome_trace_format()) | |
exit() # Trace seems clog thing up? | |
# Wrap up | |
coord.request_stop() | |
coord.join(threads) | |
sess.close() |
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