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Use distributed fixture in tests #3070

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Use distributed fixture in tests #3070

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b6406ad
Use distributed fixture in tests
vfdev-5 Sep 24, 2023
daf0b67
Merge branch 'master' into tests-use-distributed-fixture
vfdev-5 Oct 2, 2023
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37 changes: 2 additions & 35 deletions tests/ignite/contrib/engines/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -642,21 +642,9 @@ def test_setup_neptune_logging(dirname):
npt_logger.close()


@pytest.mark.distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
def test_distrib_nccl_gpu(dirname, distributed_context_single_node_nccl):
local_rank = distributed_context_single_node_nccl["local_rank"]
device = idist.device()
_test_setup_common_training_handlers(dirname, device, rank=local_rank, local_rank=local_rank, distributed=True)
test_add_early_stopping_by_val_score()


@pytest.mark.distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
def test_distrib_gloo_cpu_or_gpu(dirname, distributed_context_single_node_gloo):
def test_distrib_training_handlers(distributed, dirname):
local_rank = idist.get_local_rank()
device = idist.device()
local_rank = distributed_context_single_node_gloo["local_rank"]
_test_setup_common_training_handlers(dirname, device, rank=local_rank, local_rank=local_rank, distributed=True)
_test_setup_common_training_handlers(
dirname, device, rank=local_rank, local_rank=local_rank, distributed=True, lr_scheduler="ignite|LRScheduler"
Expand All @@ -665,24 +653,3 @@ def test_distrib_gloo_cpu_or_gpu(dirname, distributed_context_single_node_gloo):
dirname, device, rank=local_rank, local_rank=local_rank, distributed=True, lr_scheduler="ignite"
)
test_add_early_stopping_by_val_score()


@pytest.mark.multinode_distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
@pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
def test_multinode_distrib_gloo_cpu_or_gpu(dirname, distributed_context_multi_node_gloo):
device = idist.device()
rank = distributed_context_multi_node_gloo["rank"]
_test_setup_common_training_handlers(dirname, device, rank=rank)
test_add_early_stopping_by_val_score()


@pytest.mark.multinode_distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
@pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
def test_multinode_distrib_nccl_gpu(dirname, distributed_context_multi_node_nccl):
local_rank = distributed_context_multi_node_nccl["local_rank"]
rank = distributed_context_multi_node_nccl["rank"]
device = idist.device()
_test_setup_common_training_handlers(dirname, device, rank=rank, local_rank=local_rank, distributed=True)
test_add_early_stopping_by_val_score()
48 changes: 3 additions & 45 deletions tests/ignite/contrib/handlers/test_clearml_logger.py
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Expand Up @@ -907,7 +907,9 @@ def forward(self, x):
return self.net(x)


def _test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=False):
@pytest.mark.parametrize("on_zero_rank", [True, False])
def test_distrib_save_model_optimizer_lr_scheduler_with_state_dict(distributed, on_zero_rank):
device = idist.device()
if idist.get_rank() == 0:
clearml.Task.current_task = MagicMock(spec=clearml.Task)
clearml.binding.frameworks.WeightsFileHandler.create_output_model = MagicMock()
Expand Down Expand Up @@ -991,47 +993,3 @@ def update_fn(engine, batch):
lr_scheduler_value = lr_scheduler_state_dict[key]
loaded_lr_scheduler_value = loaded_lr_scheduler_state_dict[key]
assert lr_scheduler_value == loaded_lr_scheduler_value


@pytest.mark.distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo):
device = idist.device()
_test_save_model_optimizer_lr_scheduler_with_state_dict(device)
_test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=True)


@pytest.mark.distributed
@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
def test_distrib_nccl_gpu(distributed_context_single_node_nccl):
device = idist.device()
_test_save_model_optimizer_lr_scheduler_with_state_dict(device)
_test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=True)


@pytest.mark.tpu
@pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
@pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device")
def test_distrib_single_device_xla():
device = idist.device()
assert "xla" in device.type
_test_save_model_optimizer_lr_scheduler_with_state_dict(device)


def _test_save_model_optimizer_lr_scheduler_with_state_dict_xla_nprocs(index):
device = idist.device()
_test_save_model_optimizer_lr_scheduler_with_state_dict(device)

import time

# hack to have all proc properly sync:
time.sleep(1)


@pytest.mark.tpu
@pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
@pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device")
def test_distrib_single_device_xla_nprocs(xmp_executor):
n = int(os.environ["NUM_TPU_WORKERS"])
xmp_executor(_test_save_model_optimizer_lr_scheduler_with_state_dict_xla_nprocs, args=(), nprocs=n)