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# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._shard import sharded_tensor
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.distributed._shard.sharded_tensor import (
ShardedTensorTestBase,
with_comms,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_tests,
)
if TEST_WITH_DEV_DBG_ASAN:
print("Skip dev-asan as torch + multiprocessing spawn have known issues", file=sys.stderr)
sys.exit(0)
class TestShardedTensorNNInit(ShardedTensorTestBase):
""" Testing torch.nn.init functions for ShardedTensor """
@with_comms
@skip_if_lt_x_gpu(4)
@requires_nccl()
def test_init_sharded_tensor_with_uniform(self):
""" Test torch.nn.init.uniform_(ShardedTensor, a, b) """
spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
h, w = 8, 2
expected_h = 2
expected_device = torch.device(f"cuda:{self.rank}")
a, b = 10, 20
seed = 1234
dtype = torch.double
st = sharded_tensor.empty(spec, h, w, dtype=dtype)
self.assertEqual(1, len(st.local_shards()))
# Clone local tensor to ensure torch.nn.init starts from the same input
local_tensor_clone = torch.clone(st.local_shards()[0].tensor)
torch.manual_seed(seed)
torch.nn.init.uniform_(st, a=a, b=b)
torch.manual_seed(seed)
torch.nn.init.uniform_(local_tensor_clone, a=a, b=b)
self.assertEqual(local_tensor_clone, st.local_shards()[0].tensor)
@with_comms
@skip_if_lt_x_gpu(4)
@requires_nccl()
def test_init_sharded_tensor_with_normal(self):
""" Test torch.nn.init.normal_(ShardedTensor, mean, std) """
spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
h, w = 8, 2
expected_h = 2
expected_device = torch.device(f"cuda:{self.rank}")
mean, std = 10, 5
seed = 1234
dtype = torch.double
st = sharded_tensor.empty(spec, h, w, dtype=dtype)
self.assertEqual(1, len(st.local_shards()))
# Clone local tensor to ensure torch.nn.init starts from the same input
local_tensor_clone = torch.clone(st.local_shards()[0].tensor)
torch.manual_seed(seed)
torch.nn.init.normal_(st, mean=mean, std=std)
torch.manual_seed(seed)
torch.nn.init.normal_(local_tensor_clone, mean=mean, std=std)
self.assertEqual(local_tensor_clone, st.local_shards()[0].tensor)
@with_comms
@skip_if_lt_x_gpu(4)
@requires_nccl()
def test_init_sharded_tensor_with_kaiming_uniform(self):
""" Test torch.nn.init.kaiming_uniform_(ShardedTensor, a, mode, nonlinearit) """
spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
h, w = 8, 2
expected_h = 2
expected_device = torch.device(f"cuda:{self.rank}")
a, mode, nonlinearity = 0, 'fan_in', 'leaky_relu'
seed = 1234
dtype = torch.double
st = sharded_tensor.empty(spec, h, w, dtype=dtype)
self.assertEqual(1, len(st.local_shards()))
# Clone local tensor to ensure torch.nn.init starts from the same input
local_tensor_clone = torch.clone(st.local_shards()[0].tensor)
torch.manual_seed(seed)
torch.nn.init.kaiming_uniform_(st, a=a, mode=mode, nonlinearity=nonlinearity)
torch.manual_seed(seed)
torch.nn.init.kaiming_uniform_(local_tensor_clone, a=a, mode=mode, nonlinearity=nonlinearity)
self.assertEqual(local_tensor_clone, st.local_shards()[0].tensor)
if __name__ == '__main__':
run_tests()
|
