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# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard.partial_tensor import (
_PartialTensor,
)
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
EnumerableShardingSpec,
ShardMetadata,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_tests,
)
from torch.testing._internal.distributed._shard.sharded_tensor import (
ShardedTensorTestBase,
with_comms,
TEST_GPU_NUM
)
from torch.testing._internal.distributed._shard.sharded_tensor._test_st_common import (
_chunk_sharding_specs_list_for_test,
)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class TestPartialTensorReshard(ShardedTensorTestBase):
def _run_partial_tensor_n_reshard(
self, reshard_spec, input_size, world_size, reduce_op, dtype=torch.float, pg=None
):
results_compare = []
local_result = []
pg = pg if pg is not None else dist.distributed_c10d._get_default_group()
for rank in range(pg.size()):
torch.manual_seed(rank)
results = []
for _ in range(world_size):
tensor = torch.rand(*input_size, dtype=dtype).cuda(self.rank)
results.append(tensor)
if self.rank % pg.size() == rank:
local_result.append(tensor.clone().detach())
results_compare.append(torch.cat(results))
parital_tensor = _PartialTensor(
torch.cat(local_result), pg, reduce_op=reduce_op
)
local_sharded_result = parital_tensor.reshard(reshard_spec)
local_shards = local_sharded_result.local_shards()
results_compare = torch.stack(results_compare)
if reduce_op == dist.ReduceOp.SUM:
results_compare = torch.sum(results_compare, dim=0)
else:
results_compare = torch.max(results_compare, dim=0).values
rank_idx = None
for idx, placement in enumerate(reshard_spec.placements):
if placement.rank() == self.rank % pg.size():
rank_idx = idx
local_result_compare = results_compare.chunk(pg.size())[rank_idx]
self.assertEqual(1, len(local_shards))
self.assertEqual(local_shards[0].tensor, local_result_compare)
def _reshard_spec_for_subgroup(self, rank):
if rank in [0, 1]:
return ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
],
)
else:
return ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:2",
"rank:1/cuda:3",
],
)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_partial_tensor_reshard(self):
specs = _chunk_sharding_specs_list_for_test([0], seed=7)
spec = specs[0]
self._run_partial_tensor_n_reshard(spec, [13, 21], 4, dist.ReduceOp.SUM)
self._run_partial_tensor_n_reshard(spec, [12, 22], 4, dist.ReduceOp.MAX)
self._run_partial_tensor_n_reshard(spec, [13, 21], 3, dist.ReduceOp.SUM)
self._run_partial_tensor_n_reshard(spec, [17, 21], 2, dist.ReduceOp.MAX)
sub_pgs = [dist.new_group([0, 1]), dist.new_group([2, 3])]
pg = sub_pgs[self.rank // 2]
spec = self._reshard_spec_for_subgroup(self.rank)
self._run_partial_tensor_n_reshard(spec, [12, 22], 4, dist.ReduceOp.MAX, pg=pg)
self._run_partial_tensor_n_reshard(spec, [13, 22], 3, dist.ReduceOp.SUM, pg=pg)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_partial_tensor_reshard_errors(self):
enumerable_sharding_spec = EnumerableShardingSpec(
[
ShardMetadata(
shard_offsets=[0, 0],
shard_sizes=[5, 5],
placement="rank:0/cuda:0",
),
ShardMetadata(
shard_offsets=[5, 0],
shard_sizes=[5, 5],
placement="rank:1/cuda:1",
),
]
)
with self.assertRaisesRegex(
NotImplementedError, "Only ChunkShardingSpec supported for reshard."
):
self._run_partial_tensor_n_reshard(
enumerable_sharding_spec, [13, 21], 4, dist.ReduceOp.SUM
)
self._run_partial_tensor_n_reshard(
enumerable_sharding_spec, [12, 22], 4, dist.ReduceOp.MAX
)
specs = _chunk_sharding_specs_list_for_test([0], seed=7)
spec = specs[0]
with self.assertRaisesRegex(
NotImplementedError, "Only real partial tensor supported for reshard."
):
self._run_partial_tensor_n_reshard(
spec, [13, 21], 4, dist.ReduceOp.SUM, dtype=torch.cfloat
)
self._run_partial_tensor_n_reshard(
spec, [12, 22], 4, dist.ReduceOp.MAX, dtype=torch.cfloat
)
class TestPartialTensorOps(ShardedTensorTestBase):
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_transpose(self):
partial_tensor = _PartialTensor(torch.rand(5, 10))
partial_tensor = partial_tensor.transpose(0, 1)
self.assertEqual(partial_tensor.size(), torch.Size((10, 5)))
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_cat(self):
t1 = torch.rand(5, 10)
t2 = torch.rand(3, 10)
t3 = torch.rand(4, 10)
partial_tensors = [_PartialTensor(t1), _PartialTensor(t2), _PartialTensor(t3)]
partial_concat = torch.cat(partial_tensors)
local_concat = torch.cat([t1, t2, t3])
self.assertEqual(local_concat.size(), partial_concat.size())
# Test dim kwarg
t1 = torch.rand(5, 10)
t2 = torch.rand(5, 12)
t3 = torch.rand(5, 11)
partial_tensors = [_PartialTensor(t1), _PartialTensor(t2), _PartialTensor(t3)]
partial_concat = torch.cat(partial_tensors, dim=1)
local_concat = torch.cat([t1, t2, t3], dim=1)
self.assertEqual(local_concat.size(), partial_concat.size())
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_cat_errors(self):
with self.assertRaisesRegex(
RuntimeError, 'All inputs need to be an instance of _PartialTensor'
):
torch.cat([_PartialTensor(torch.rand(10)), torch.rand(10)])
with self.assertRaisesRegex(
RuntimeError, 'reduce_ops need to be the same'
):
torch.cat([_PartialTensor(torch.rand(10)), _PartialTensor(torch.rand(10), reduce_op=dist.ReduceOp.MAX)])
with self.assertRaisesRegex(
RuntimeError, '"out" kwarg is not supported'
):
torch.cat([_PartialTensor(torch.rand(10)), _PartialTensor(torch.rand(10))], out=torch.rand(10))
if __name__ == "__main__":
run_tests()
|
