def test_mnist_train(self):
"""Generic test on default arguments in training"""
py_args = self.generic_arguments.copy()
out = test_util.run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [98.41]
test_util.parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_train_32_100_10(self):
"""Generic functional test"""
py_args = self.generic_arguments.copy()
out = run_test_helper(run_pytorch_mnist, **py_args)
expected_accuracy = [
87.65, 88.06, 88.4, 88.43, 88.68, 88.71, 88.69, 88.89, 88.85, 88.61
]
parse_results_for_accuracy(out, expected_accuracy, 2.5)
def test_mnist_train(self):
"""Generic test on default arguments in training"""
py_args = self.generic_arguments.copy()
out = run_test_helper(run_mnist_training, **py_args)
expected_accuracy = [
50.32, 58.67, 68.09, 72.81, 75.30, 77.14, 78.53, 79.75, 80.62,
81.37
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_mnist_train(self):
"""Generic test on default arguments in training"""
py_args = self.generic_arguments.copy()
out = run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
88.88, 89.63, 89.83, 90.01, 90.12, 90.22, 90.40, 90.59, 90.65,
90.70
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_mnist_train_sharded(self):
"""Generic test on default arguments in training over 2 IPUs"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 2
out = run_test_helper(
run_popart_mnist_training,
**py_args
)
expected_accuracy = [90.70]
parse_results_for_accuracy(
out, expected_accuracy, self.accuracy_tolerances
)
def test_mnist_train_sharded_pipelined(self):
"""Generic test on default arguments in training over 2 IPUs
and pipelined"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 2
py_args["--pipeline"] = ""
out = run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
88.11, 88.69, 88.91, 88.94, 88.92, 88.98, 89.05, 89.14, 89.18,
89.25
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_mnist_train_replicated(self):
"""Generic test on default arguments in training over 2 IPUs
with replication"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 2
py_args["--replication-factor"] = 2
out = run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
88.88, 89.63, 89.83, 90.01, 90.12, 90.22, 90.40, 90.59, 90.65,
90.70
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_mnist_complete(self):
"""Tests that the model trains and a pvti file is created"""
py_args = self.generic_arguments.copy()
out = run_test_helper(
run_popart_mnist_complete,
**py_args
)
expected_accuracy = [88.88]
parse_results_for_accuracy(
out, expected_accuracy, self.accuracy_tolerances
)
# Verify that the pvti file is created
if not any(fname.endswith(".pvti") for fname in os.listdir(os.path.dirname(__file__))):
raise AssertionError("No pvti file found")
def test_mnist_train_replicated_pipelined(self):
"""Generic test on default arguments in training over 2 IPUs
and pipelined"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 4
py_args["--replication-factor"] = 2
py_args["--pipeline"] = ""
out = run_test_helper(
run_popart_mnist_training,
**py_args
)
expected_accuracy = [89.25]
parse_results_for_accuracy(
out, expected_accuracy, self.accuracy_tolerances
)
def test_resnet8_bs4_4ipus(self):
with TemporaryDirectory() as datadir:
out = run_popart_resnet_training(
**{
'--size': '8',
'--batch-size': 4,
'--norm-type': 'GROUP',
'--num-ipus': 4,
'--epochs': 5,
'--no-prng': '',
'--data-dir': datadir,
'--num-workers': 0
})
expected_accuracy = [48.5, 53.6, 61.6, 64.6, 65.1]
parse_results_for_accuracy(out, expected_accuracy, 4)
def test_resnet8_bs4_4ipus_pipeline(self):
with TemporaryDirectory() as datadir:
out = run_popart_resnet_training(
**{
'--size': '8',
'--batch-size': 4,
'--norm-type': 'GROUP',
'--num-ipus': 4,
'--epochs': 5,
'--pipeline': '',
'--no-prng': '',
'--data-dir': datadir,
'--num-workers': 0,
'--ckpt': os.path.join(datadir, "ckpt.onnx"),
})
expected_accuracy = [42.6, 48.8, 63.7, 62.5, 61.0]
parse_results_for_accuracy(out, expected_accuracy, 6)
def test_resnet8_bs4_4ipus_pipeline_replication(self):
with TemporaryDirectory() as datadir:
out = run_popart_resnet_training(
**{
'--size': '8',
'--batch-size': 8,
'--norm-type': 'GROUP',
'--num-ipus': 8,
'--epochs': 5,
'--pipeline': '',
'--replication-factor': 2,
'--no-prng': '',
'--data-dir': datadir,
'--num-workers': 0
})
expected_accuracy = [42.25, 48.55, 59.4, 62.35, 61.0]
parse_results_for_accuracy(out, expected_accuracy, 4)
def run_cifar10(file_name):
cwd = os.path.dirname(os.path.abspath(__file__))
cmd = ["python3", 'cifar10_{0}.py'.format(file_name)]
out = subprocess.check_output(cmd, cwd=cwd).decode("utf-8")
parse_results_for_accuracy(out, [81.0], 6.0)
return out
