|
- import hetu as ht
-
- import os
- import time
- import argparse
- import numpy as np
- import socket
-
-
- def fc(x, shape, name, with_relu=True):
- weight = ht.init.random_normal(shape, stddev=0.04, name=name+'_weight')
- bias = ht.init.random_normal(shape[-1:], stddev=0.04, name=name+'_bias')
- x = ht.matmul_op(x, weight)
- x = x + ht.broadcastto_op(bias, x)
- if with_relu:
- x = ht.relu_op(x)
- return x
-
-
- if __name__ == "__main__":
- # argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--warmup', type=int, default=1,
- help='warm up steps excluded from timing')
- parser.add_argument('--batch-size', type=int,
- default=10000, help='batch size')
- parser.add_argument('--learning-rate', type=float,
- default=0.01, help='learning rate')
- args = parser.parse_args()
-
- datasets = ht.data.mnist()
- train_set_x, train_set_y = datasets[0]
- valid_set_x, valid_set_y = datasets[1]
- test_set_x, test_set_y = datasets[2]
-
- with ht.context([ht.rgpu('daim117', 0), ht.rgpu('daim117', 1)]):
- x = ht.Variable(name="dataloader_x", trainable=False)
- activation = fc(x, (784, 1024), 'mlp_fc0', with_relu=True)
-
- with ht.context([ht.rgpu('daim117', 2), ht.rgpu('daim117', 3)]):
- activation = fc(activation, (1024, 1024), 'mlp_fc1', with_relu=True)
- activation = fc(activation, (1024, 1024), 'mlp_fc11', with_relu=True)
-
- with ht.context([ht.rgpu('daim118', 0), ht.rgpu('daim118', 1)]):
- activation = fc(activation, (1024, 1024), 'mlp_fc2', with_relu=True)
- activation = fc(activation, (1024, 1024), 'mlp_fc22', with_relu=True)
-
- with ht.context([ht.rgpu('daim118', 2), ht.rgpu('daim118', 3)]):
- y_pred = fc(activation, (1024, 10), 'mlp_fc3', with_relu=True)
- y_ = ht.Variable(name="dataloader_y", trainable=False)
- loss = ht.softmaxcrossentropy_op(y_pred, y_)
- loss = ht.reduce_mean_op(loss, [0])
- opt = ht.optim.SGDOptimizer(learning_rate=args.learning_rate)
- train_op = opt.minimize(loss)
- executor = ht.Executor([loss, train_op])
-
- print_ranks = [2, 3]
- hostname = socket.gethostname()
-
- # training
- steps = train_set_x.shape[0] // args.batch_size
- for step in range(steps):
- start = step * args.batch_size
- end = start + args.batch_size
- loss_val, _ = executor.run(feed_dict={
- x: train_set_x[start:end], y_: train_set_y[start:end]}, convert_to_numpy_ret_vals=True)
- if executor.local_rank in print_ranks and hostname == 'daim118':
- print('[step {}]: loss: {}'.format(step, loss_val[0]))
|
