def setUpClass(cls):
cls.logdir = tempfile.mkdtemp()
cmd = [
'python3',
'train.py',
'--dataset=cifar-10',
'--synthetic-data',
'--model-size=20',
'--batch-size=1',
'--iterations=100',
'--batches-per-step=1',
'--pipeline-depth=4',
'--pipeline-splits',
'b2/0/relu',
'--xla-recompute',
'--shards=2',
'--distributed',
'--no-validation',
'--no-stochastic-rounding',
'--log-dir',
cls.logdir,
]
cwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
worker_ports = cls._pick_unique_unused_ports(cls.NUM_WORKERS)
cluster_spec = {
'worker': ['localhost:%s' % port for port in worker_ports]
}
processes = cls._start_processes_with_tf_config(cmd, cwd, cluster_spec)
cls._wait_for_processes(processes, cls.WORKER_TIMEOUT_SECONDS)
cls.worker_log_dirs = cls._find_worker_log_dirs()
cls.training_logs = [
parse_csv(os.path.join(d, "training.csv"))
for d in cls.worker_log_dirs
]
def test_resnet_50_from_readme(self):
NUM_WORKERS = 2
WORKER_TIMEOUT_SECONDS = 30 * 60
with tempfile.TemporaryDirectory() as logdir:
cmd = [
'python3', 'train.py',
'--dataset=imagenet',
'--generated-data',
'--model-size=50',
'--batch-size=4',
'--batches-per-step=1',
'--shards=4',
'--pipeline',
'--gradient-accumulation-count=64',
'--pipeline-splits', 'b1/2/relu', 'b2/3/relu', 'b3/5/relu',
'--xla-recompute',
'--replicas=2', # Instead of 4 to make two processes fit on one machine.
'--distributed',
'--no-stochastic-rounding',
'--no-validation',
'--iterations=100',
'--learning-rate-schedule=1',
'--base-learning-rate=-14',
'--log-dir', logdir,
'--ckpt-all-instances', "true",
'--log-all-instances', "true"
]
extra_env = {
'POPLAR_ENGINE_OPTIONS': '{"opt.maxCopyMergeSize": 8388608}',
}
cwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
worker_ports = self._pick_unique_unused_ports(NUM_WORKERS)
cluster_spec = {
'worker': ['localhost:%s' % port for port in worker_ports]
}
processes = self._start_processes_with_tf_config(cmd, cwd, extra_env, cluster_spec)
self._wait_for_processes(processes, WORKER_TIMEOUT_SECONDS)
worker_log_dirs = self._find_worker_log_dirs(NUM_WORKERS, logdir)
training_logs = [parse_csv(os.path.join(d, "training.csv")) for d in worker_log_dirs]
# The final training accuracy should be the same on all workers.
for i in range(1, NUM_WORKERS):
self.assertEqual(
training_logs[0]['train_acc_avg'][-1],
training_logs[i]['train_acc_avg'][-1])
# The final training loss should be the same on all workers.
for i in range(1, NUM_WORKERS):
self.assertEqual(
training_logs[0]['loss_avg'][-1],
training_logs[i]['loss_avg'][-1])
# The final weights should be the same on all workers.
var_names_and_shapes = tf.train.list_variables(worker_log_dirs[0])
for var_name, _ in var_names_and_shapes:
value_worker_0 = tf.train.load_variable(worker_log_dirs[0], var_name)
for i in range(1, NUM_WORKERS):
value_worker_i = tf.train.load_variable(worker_log_dirs[i], var_name)
self.assertListEqual(value_worker_0.tolist(), value_worker_i.tolist())
def test_resnet8(self):
TIMEOUT_SECONDS = 5 * 60
NUM_TOTAL_REPLICAS = 4
NUM_INSTANCES = 2
NUM_LOCAL_REPLICAS = NUM_TOTAL_REPLICAS // NUM_INSTANCES
with tempfile.TemporaryDirectory() as logdir:
# The buildbot runs as root, so let's allow that.
cmd = [
'poprun',
'--mpi-global-args=--tag-output --allow-run-as-root',
'--num-replicas=' + str(NUM_TOTAL_REPLICAS),
'--num-instances=' + str(NUM_INSTANCES),
sys.executable,
'train.py',
'--dataset=cifar-10',
'--generated-data',
'--model-size=8',
'--batch-size=1',
'--batches-per-step=10',
'--gradient-accumulation-count=10',
'--no-validation',
'--no-stochastic-rounding',
'--iterations=100',
'--log-dir', logdir,
'--name-suffix', 'popdist_instance',
'--ckpt-all-instances', "true",
'--log-all-instances', "true"
]
# Add some debug logging.
extra_env = {
'POPRUN_LOG_LEVEL': 'TRACE',
'TF_CPP_VMODULE': 'poplar_compiler=1,poplar_executor=1',
}
cwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
env = os.environ.copy()
env.update(extra_env)
subprocess.check_call(cmd, cwd=cwd, env=env, timeout=TIMEOUT_SECONDS)
instance_logdirs = glob.glob(f"{logdir}/*_popdist_instance_*")
self.assertEqual(len(instance_logdirs), NUM_INSTANCES)
training_logs = []
for instance_logdir in instance_logdirs:
# Check that each instance got the correct number of replicas from popdist.
with open(os.path.join(instance_logdir, 'arguments.json'), 'r') as f:
argument_log = json.load(f)
self.assertEqual(argument_log['replicas'], NUM_LOCAL_REPLICAS)
# Check that the final accuracy is decent.
training_log = parse_csv(os.path.join(instance_logdir, 'training.csv'))
self.assertGreater(training_log['train_acc_avg'][-1], 95)
training_logs.append(training_log)
# The final training accuracy should be the same on all instances.
for i in range(1, NUM_INSTANCES):
self.assertEqual(
training_logs[0]['train_acc_avg'][-1],
training_logs[i]['train_acc_avg'][-1])
# The final training loss should be the same on all instances.
for i in range(1, NUM_INSTANCES):
self.assertEqual(
training_logs[0]['loss_avg'][-1],
training_logs[i]['loss_avg'][-1])
# The final weights should be the same on all instances.
var_names_and_shapes = tf.train.list_variables(instance_logdirs[0])
for var_name, _ in var_names_and_shapes:
value_instance_0 = tf.train.load_variable(instance_logdirs[0], var_name)
for i in range(1, NUM_INSTANCES):
value_instance_i = tf.train.load_variable(instance_logdirs[i], var_name)
self.assertListEqual(value_instance_0.tolist(), value_instance_i.tolist())
def test_resnet8(self):
TIMEOUT_SECONDS = 5 * 60
NUM_TOTAL_REPLICAS = 4
NUM_INSTANCES = 2
NUM_LOCAL_REPLICAS = NUM_TOTAL_REPLICAS // NUM_INSTANCES
with tempfile.TemporaryDirectory() as logdir:
# The buildbot runs as root, so let's allow that.
cmd = [
'poprun',
'--mpi-global-args=--tag-output --allow-run-as-root',
'--num-replicas=' + str(NUM_TOTAL_REPLICAS),
'--num-instances=' + str(NUM_INSTANCES),
sys.executable,
'train.py',
'--dataset=cifar-10',
'--synthetic-data',
'--model-size=8',
'--batch-size=1',
'--batches-per-step=10',
'--gradient-accumulation-count=10',
'--no-validation',
'--no-stochastic-rounding',
'--iterations=100',
'--log-dir',
logdir,
'--name-suffix',
'popdist_instance',
]
# Add the MPI library dirs on the LD_LIBRARY_PATH, as these are not
# always searched by default (e.g. on CentOS). A user would normally
# do "module load mpi/openmpi-x86_64" on CentOS to achieve this instead.
libdirs = subprocess.check_output(["mpic++", "--showme:libdirs"])
libdirs = libdirs.decode().strip().replace(" ", ":")
ld_library_path = "{}:{}".format(os.environ["LD_LIBRARY_PATH"],
libdirs)
# Add some debug logging, and use executable cache which is shared between instances.
extra_env = {
'POPRUN_LOG_LEVEL': 'TRACE',
'TF_CPP_VMODULE': 'poplar_compiler=1,poplar_executor=1',
'TF_POPLAR_FLAGS':
f"--executable_cache_path={logdir}/exec_cache",
'LD_LIBRARY_PATH': ld_library_path,
}
cwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
env = os.environ.copy()
env.update(extra_env)
subprocess.check_call(cmd,
cwd=cwd,
env=env,
timeout=TIMEOUT_SECONDS)
instance_logdirs = glob.glob(f"{logdir}/*_popdist_instance_*")
self.assertEqual(len(instance_logdirs), NUM_INSTANCES)
training_logs = []
for instance_logdir in instance_logdirs:
# Check that each instance got the correct number of replicas from popdist.
with open(os.path.join(instance_logdir, 'arguments.json'),
'r') as f:
argument_log = json.load(f)
self.assertEqual(argument_log['replicas'], NUM_LOCAL_REPLICAS)
# Check that the final accuracy is decent.
training_log = parse_csv(
os.path.join(instance_logdir, 'training.csv'))
self.assertGreater(training_log['train_acc_avg'][-1], 95)
training_logs.append(training_log)
# The final training accuracy should be the same on all instances.
for i in range(1, NUM_INSTANCES):
self.assertEqual(training_logs[0]['train_acc_avg'][-1],
training_logs[i]['train_acc_avg'][-1])
# The final training loss should be the same on all instances.
for i in range(1, NUM_INSTANCES):
self.assertEqual(training_logs[0]['loss_avg'][-1],
training_logs[i]['loss_avg'][-1])
# The final weights should be the same on all instances.
var_names_and_shapes = tf.train.list_variables(instance_logdirs[0])
for var_name, _ in var_names_and_shapes:
value_instance_0 = tf.train.load_variable(
instance_logdirs[0], var_name)
for i in range(1, NUM_INSTANCES):
value_instance_i = tf.train.load_variable(
instance_logdirs[i], var_name)
self.assertListEqual(value_instance_0.tolist(),
value_instance_i.tolist())
