Documentation

load_iteration_per_loop_var

Description

This API is used in conjunction with create_iteration_per_loop_var to set the number of iterations per training loop every sess.run() call on the device side.

Prototype

def load_iteration_per_loop_var(self, sess, iterations_per_loop=1)

Options

Option

Input/Output

Description

sess

Input

Created TensorFlow session

iterations_per_loop

Input

Number of iterations per training loop per sess.run() call on the device side. Defaults to 1. The total number of iterations per training loop must be an integer multiple of iterations_per_loop.

Returns

None

Restrictions

In mixed compute mode (mix_compile_mode is set to True), iterations_per_loop must be set to 1.

Example

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from npu_bridge.npu_init import *

config = tf.ConfigProto(allow_soft_placement=True)
custom_op =  config.graph_options.rewrite_options.custom_optimizers.add()
custom_op.name =  "NpuOptimizer"
custom_op.parameter_map["enable_data_pre_proc"].b = True # If the GetNext operator exists on the network, offload it. GetNext operator offload is a prerequisite for iteration offload.
custom_op.parameter_map["iterations_per_loop"].i = 10  # Used for functional validation. Must be equal to iterations_per_loop set in load_iteration_per_loop_var.
config = npu_config_proto(config_proto=config)

# Train a model.
with tf.Session(config=config) as sess:
    sess.run(init)
    # Set the number of iterations per loop to 10 in sess.run mode.
    iteration = util.IterationPerLoop() 
    train_op = iteration.create_iteration_per_loop_var(optimizer) # Modify the graph.
    tf.train.Supervisor(logdir="/home/xxxx",init_op=init)  # Freeze the graph.
    iteration.load_iteration_per_loop_var(sess, 10)  # Set the number of iterations per loop.

    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)
 
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([train_op, cost], feed_dict={x: batch_xs, y: batch_ys})
 
            avg_cost += c / total_batch