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# Owner(s): ["oncall: distributed"]
import sys
from torch import distributed as dist
from torch.distributed.fsdp import CPUOffload
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
CUDAInitMode,
FSDPInitMode,
FSDPTest,
NestedWrappedModule,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
instantiate_parametrized_tests,
parametrize,
run_tests,
)
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class TestPureFP16(FSDPTest):
@property
def world_size(self):
# Test fails due to inaccuracies when using more than 5 GPUs
return min(5, super().world_size)
@skip_if_lt_x_gpu(2)
@parametrize(
"cpu_offload",
[CPUOffload(offload_params=True), CPUOffload(offload_params=False)],
)
def test_pure_fp16(self, cpu_offload: CPUOffload):
"""Tests pure FP16 training, including when the parameter's dtype is
changed after FSDP initialization and before training."""
self._test_fsdp_parity(
NestedWrappedModule,
FSDPInitMode.RECURSIVE,
cuda_init_mode=CUDAInitMode.CUDA_AFTER,
# Run one iteration to avoid NaN without a gradient scaler
num_iters=1,
cpu_offload=cpu_offload,
use_pure_fp16=True,
)
instantiate_parametrized_tests(TestPureFP16)
if __name__ == "__main__":
run_tests()
|
