Hi everyone,
I am currently using gcp in order to test the TPU on pytorch.
My code comes from Google Colab . I change or remove some part of codes like the TPU_ADDRESS.
During the training, I got an exeption message :
2019-11-21 18:22:14.125827: I 2364 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.141768: I 2369 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.137212: I 2365 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.159541: I 2376 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.170106: I 2380 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.182444: I 2384 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
2019-11-21 18:22:14.191538: I 2392 tensorflow/compiler/xla/xla_client/mesh_service.cc:168] Waiting to connect to client mesh master (300 seconds) localhost:40359
Device : xla:1
2019-11-21 18:22:22.612051: I 2364 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
Device : xla:0
2019-11-21 18:22:23.098120: I 2380 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
Device : xla:0
2019-11-21 18:22:23.538515: I 2384 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
2019-11-21 18:22:23.689589: I 2376 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
2019-11-21 18:22:23.776546: I 2392 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
Device : xla:0
Device : xla:0
Device : xla:0
2019-11-21 18:22:24.179791: I 2369 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
2019-11-21 18:22:24.274278: I 2365 tensorflow/compiler/xla/xla_client/computation_client.cc:195] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:40359
Device : xla:0
Device : xla:0
Traceback (most recent call last):
File “test.py”, line 217, in
xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=FLAGS[‘num_cores’],start_method=‘fork’)
File “/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py”, line 173, in spawn
start_method=start_method)
File “/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/torch/multiprocessing/spawn.py”, line 149, in start_processes
while not context.join():
File “/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/torch/multiprocessing/spawn.py”, line 107, in join
(error_index, name)
Exception: process 5 terminated with signal SIGSEGV
My code is this one :
import collections
from datetime import datetime, timedelta
import os
import requests
import threading
_VersionConfig = collections.namedtuple('_VersionConfig', 'wheels,server')
VERSION = "xrt==1.15.0" #@param ["xrt==1.15.0", "torch_xla==nightly"]
CONFIG = {
'xrt==1.15.0': _VersionConfig('1.15', '1.15.0'),
'torch_xla==nightly': _VersionConfig('nightly', 'XRT-dev{}'.format(
(datetime.today() - timedelta(1)).strftime('%Y%m%d'))),
}[VERSION]
DIST_BUCKET = 'gs://tpu-pytorch/wheels'
TORCH_WHEEL = 'torch-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
TORCH_XLA_WHEEL = 'torch_xla-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
TORCHVISION_WHEEL = 'torchvision-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
# Update TPU XRT version
def update_server_xrt():
print('Updating server-side XRT to {} ...'.format(CONFIG.server))
url = 'http://{TPU_ADDRESS}:8475/requestversion/{XRT_VERSION}'.format(
TPU_ADDRESS=os.environ['TPU_IP_ADDRESS'].split(':')[0],
XRT_VERSION=CONFIG.server,
)
print('Done updating server-side XRT: {}'.format(requests.post(url)))
#update = threading.Thread(target=update_server_xrt)
#update.start()
#update.join()
# Result Visualization Helper
import math
from matplotlib import pyplot as plt
M, N = 4, 6
RESULT_IMG_PATH = '/tmp/test_result.png'
def plot_results(images, labels, preds):
images, labels, preds = images[:M*N], labels[:M*N], preds[:M*N]
inv_norm = transforms.Normalize((-0.1307/0.3081,), (1/0.3081,))
num_images = images.shape[0]
fig, axes = plt.subplots(M, N, figsize=(11, 9))
fig.suptitle('Correct / Predicted Labels (Red text for incorrect ones)')
for i, ax in enumerate(fig.axes):
ax.axis('off')
if i >= num_images:
continue
img, label, prediction = images[i], labels[i], preds[i]
img = inv_norm(img)
img = img.squeeze() # [1,Y,X] -> [Y,X]
label, prediction = label.item(), prediction.item()
if label == prediction:
ax.set_title(u'\u2713', color='blue', fontsize=22)
else:
ax.set_title(
'X {}/{}'.format(label, prediction), color='red')
ax.imshow(img)
plt.savefig(RESULT_IMG_PATH, transparent=True)
# Define Parameters
FLAGS = {}
FLAGS['datadir'] = "/tmp/mnist"
FLAGS['batch_size'] = 128
FLAGS['num_workers'] = 4
FLAGS['learning_rate'] = 0.01
FLAGS['momentum'] = 0.5
FLAGS['num_epochs'] = 10
FLAGS['num_cores'] = 8
FLAGS['log_steps'] = 20
FLAGS['metrics_debug'] = False
import numpy as np
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
import torch_xla.distributed.parallel_loader as pl
import torch_xla.distributed.xla_multiprocessing as xmp
import torch_xla.utils.utils as xu
from torchvision import datasets, transforms
class MNIST(nn.Module):
def __init__(self):
super(MNIST, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.bn1 = nn.BatchNorm2d(10)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.bn2 = nn.BatchNorm2d(20)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = self.bn1(x)
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = self.bn2(x)
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def train_mnist():
torch.manual_seed(1)
# Get and shard dataset into dataloaders
norm = transforms.Normalize((0.1307,), (0.3081,))
train_dataset = datasets.MNIST(
os.path.join(FLAGS['datadir'], str(xm.get_ordinal())),
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(), norm]))
test_dataset = datasets.MNIST(
os.path.join(FLAGS['datadir'], str(xm.get_ordinal())),
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(), norm]))
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS['batch_size'],
sampler=train_sampler,
num_workers=FLAGS['num_workers'],
drop_last=True)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS['batch_size'],
shuffle=False,
num_workers=FLAGS['num_workers'],
drop_last=True)
# Scale learning rate to world size
lr = FLAGS['learning_rate'] * xm.xrt_world_size()
# Get loss function, optimizer, and model
device = xm.xla_device()
print("Device : ", device)
model = MNIST().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=FLAGS['momentum'])
loss_fn = nn.NLLLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS['batch_size'])
#if x % FLAGS['log_steps'] == 0:
# print('[xla:{}]({}) Loss={:.5f} Rate={:.2f} GlobalRate={:.2f} Time={}'.format(
# xm.get_ordinal(), x, loss.item(), tracker.rate(),
# tracker.global_rate(), time.asctime()), flush=True)
def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
data, pred, target = None, None, None
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
#print('[xla:{}] Accuracy={:.2f}%'.format(
# xm.get_ordinal(), accuracy), flush=True)
return accuracy, data, pred, target
# Train and eval loops
accuracy = 0.0
data, pred, target = None, None, None
for epoch in range(1, FLAGS['num_epochs'] + 1):
begin = time.time()
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
xm.master_print("Finished training epoch {0} in {1} sec".format(epoch, time.time()-begin))
#para_loader = pl.ParallelLoader(test_loader, [device])
#accuracy, data, pred, target = test_loop_fn(para_loader.per_device_loader(device))
#if FLAGS['metrics_debug']:
# xm.master_print(met.metrics_report(), flush=True)
return accuracy, data, pred, target
# Start training processes
def _mp_fn(rank, flags):
global FLAGS
FLAGS = flags
torch.set_default_tensor_type('torch.FloatTensor')
accuracy, data, pred, target = train_mnist()
#if rank == 0:
# Retrieve tensors that are on TPU core 0 and plot.
#plot_results(data.cpu(), pred.cpu(), target.cpu())
xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=FLAGS['num_cores'],start_method='fork')
#train_mnist()
Moreover if I use only train_mnist function instead of xmp.spawn, the training works but take around 13 seconds which is slower than in Colab (around 3 seconds) so I suspect than something is wrong.
The only difference I have with colab is that :
- update = threading.Thread(target=update_server_xrt) is commented
- put the good TPU_ADDRESS instead of Colab TPU IP
I used the custom image with pytorch XLA already installed on GCP. Should I installed something else ? I think there should be no issue with the code as it works on colab,
I am using torch XLA nightly.
I thank you for your advices 