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# Owner(s): ["oncall: distributed"]
import functools
import itertools
import sys
import unittest
from typing import Optional
import torch
from torch import distributed as dist
from torch.cuda.amp.common import amp_definitely_not_available
from torch.distributed.fsdp import CPUOffload, MixedPrecision
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
CUDAInitMode,
DummyProcessGroup,
FSDPInitMode,
FSDPTest,
NestedWrappedModule,
subtest_name,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
TestCase,
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)
params = "cpu_offload,sharding_strategy,mixed_precision"
cpu_offload_config = [CPUOffload(offload_params=True), CPUOffload(offload_params=False)]
sharding_strategy_config = [ShardingStrategy.SHARD_GRAD_OP, None]
mixed_precision = ["enable_mixed_precision", None]
configs = list(itertools.product(cpu_offload_config,
sharding_strategy_config,
mixed_precision))
test_name_mapping = {
str(CPUOffload(offload_params=True)): "offload_true",
str(CPUOffload(offload_params=False)): "offload_false",
str(ShardingStrategy.SHARD_GRAD_OP): "shard_grad_op",
"enable_mixed_precision": "mixed_precision"
}
subtest_name = functools.partial(subtest_name, test_name_mapping)
class TestShardGradScaler(TestCase):
@unittest.skipIf(amp_definitely_not_available(), "no supported device (cuda, xla) found")
def test_grad_scaling(self):
pg = DummyProcessGroup(0, 1)
scaler = ShardedGradScaler(init_scale=2.0, process_group=pg, enabled=True)
t0 = torch.full((1,), 4.0, dtype=torch.float32, device="cpu")
t1 = torch.full((1,), 8.0, dtype=torch.float32, device="cpu")
outputs = [t1.clone(), (t0.clone(), t1.clone()), [t0.clone(), t1.clone()]]
outputs = scaler.scale(outputs)
self.assertTrue(outputs[0] == 16.0 and outputs[1][0] == 8.0 and outputs[1][1] == 16.0)
self.assertTrue(outputs[2][0] == 8.0 and outputs[2][1] == 16.0)
self.assertTrue(scaler._scale.device == t1.device)
@unittest.skipIf(amp_definitely_not_available(), "no supported device (cuda, xla) found")
def test_scaling_unscaling_sparse(self):
pg = DummyProcessGroup(0, 1)
scaler = ShardedGradScaler(init_scale=2.0, process_group=pg, enabled=True)
inv_scale = torch.full((1,), 0.5, dtype=torch.float, device="cpu")
found_inf = torch.full((1,), 0, dtype=torch.float, device="cpu")
i = torch.tensor([[0, 1, 1],
[2, 0, 2]], device="cpu", dtype=torch.int64)
v = torch.tensor([16.0, 32.0, 64.0], dtype=torch.float, device="cpu")
s = torch.sparse_coo_tensor(i, v, torch.Size([2, 3]), device="cpu", dtype=torch.float)
# unscale sparse tensors
s1 = s.clone()
s1.grad = s.clone()
opt = torch.optim.SGD([s1], lr=1.0)
found_inf.zero_()
found_inf = scaler._unscale_grads_(opt, inv_scale, found_inf)[s1.device]
self.assertEqual(found_inf, 0.0)
self.assertEqual(s1.grad.to_dense(), (s / 2).to_dense())
# unscale sparse tensor: inf
v = torch.tensor([16.0, 32.0, float('inf')], dtype=torch.float, device="cpu")
s1.grad = torch.sparse_coo_tensor(i, v, torch.Size([2, 3]), device="cpu", dtype=torch.float)
found_inf.zero_()
found_inf = scaler._unscale_grads_(opt, inv_scale, found_inf)[s1.device]
self.assertEqual(found_inf, 1.0)
# unscale sparse tensor: overflow (marked as inf)
i = torch.tensor([[1, 1, 1],
[0, 0, 2]], device="cpu", dtype=torch.int64)
# coalescing sparse tensor here will cause the value to be Inf
v = torch.tensor([2**15, 2**15, 1.0], dtype=torch.float16, device="cpu")
s1 = torch.sparse_coo_tensor(i, v, torch.Size([2, 3]), device="cpu", dtype=torch.float16)
s1.grad = s1.clone()
found_inf.zero_()
found_inf = scaler._unscale_grads_(opt, inv_scale, found_inf)[s1.device]
self.assertEqual(found_inf, 1.0)
@unittest.skipIf(amp_definitely_not_available(), "no supported device (cuda, xla) found")
def test_inf_gradients_skip_optim_step(self):
pg = DummyProcessGroup(0, 1)
scaler = ShardedGradScaler(init_scale=2.0, process_group=pg, enabled=True)
loss = torch.full((1,), 4.0, dtype=torch.float32, device="cpu")
t0 = torch.tensor([float('inf')], dtype=torch.float32, device="cpu")
t0.grad = t0.clone()
opt = torch.optim.SGD([t0], lr=1.0)
scaler.scale(loss)
ret_val = scaler.step(opt)
self.assertTrue(ret_val is None)
class TestShardedGradScalerParityWithDDP(FSDPTest):
def _get_init_modes_for_test(self, cpu_offload):
modes = [
CUDAInitMode.CUDA_AFTER,
CUDAInitMode.CUDA_BEFORE
]
# Note that CUDAInitMode.CUDA_NEVER works currently only with CPU
# offload as we explicitly bring the param back to CUDA device. In
# general, it will not work since we try to all_gather p.data which is
# on CPU but NCCL only supports GPU.
if cpu_offload.offload_params:
modes.append(CUDAInitMode.CUDA_NEVER)
return modes
@skip_if_lt_x_gpu(2)
@parametrize(params, configs, subtest_name)
def test_fsdp_ddp_parity_with_grad_scaler(
self,
cpu_offload: CPUOffload,
sharding_strategy: Optional[ShardingStrategy],
mixed_precision: Optional[str],
):
init_modes = self._get_init_modes_for_test(cpu_offload)
mp = MixedPrecision(
param_dtype=torch.float16,
reduce_dtype=torch.float16,
buffer_dtype=torch.float16,
) if mixed_precision is not None else None
for cuda_init_mode in init_modes:
self._test_fsdp_parity(
NestedWrappedModule,
FSDPInitMode.RECURSIVE,
cuda_init_mode=cuda_init_mode,
cpu_offload=cpu_offload,
sharding_strategy=sharding_strategy,
mixed_precision=mp,
enable_sharded_grad_scaler=True,
)
instantiate_parametrized_tests(TestShardGradScaler)
instantiate_parametrized_tests(TestShardedGradScalerParityWithDDP)
if __name__ == "__main__":
run_tests()
|
