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# Owner(s): ["oncall: distributed"]
from copy import deepcopy
import torch
import torch.optim as optim
from torch.distributed._shard import shard_parameter, sharded_tensor
from torch.distributed._shard.sharded_optim import ShardedOptimizer
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.common_utils import run_tests
from torch.testing._internal.distributed._shard.sharded_tensor import (
ShardedTensorTestBase,
with_comms,
)
class MyShardedModel(torch.nn.Module):
def __init__(self, spec=None, group=None):
super().__init__()
# Use same seed.
torch.manual_seed(0)
self.param = torch.nn.Parameter(torch.rand(5, 10))
if spec is not None:
self.sharded_param = torch.nn.Parameter(
sharded_tensor.rand(
spec, 20, 10, requires_grad=True, process_group=group
)
)
else:
self.sharded_param = torch.nn.Parameter(torch.rand(5, 10))
def forward(self, input):
if isinstance(self.sharded_param, sharded_tensor.ShardedTensor):
return self.param + self.sharded_param.local_shards()[0].tensor + input
else:
return self.sharded_param + self.param + input
class MyShardedLinear(torch.nn.Module):
def __init__(self, rank=None):
super().__init__()
# Use same seed.
torch.manual_seed(0)
self.linear1 = torch.nn.Linear(17, 12)
self.linear2 = torch.nn.Linear(12, 29)
self.gelu = torch.nn.GELU()
if rank:
self.linear1.cuda(rank)
self.linear2.cuda(rank)
def shard_parameter(self):
rowwise_sharding_spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
colwise_sharding_spec = ChunkShardingSpec(
dim=1,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
shard_parameter(self.linear1, "weight", rowwise_sharding_spec)
shard_parameter(self.linear2, "weight", colwise_sharding_spec)
def forward(self, inp):
return self.linear2(self.gelu(self.linear1(inp)))
class TestShardedOptimizer(ShardedTensorTestBase):
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(4)
@requires_nccl()
def test_sharded_optim(self):
rowwise_spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
local_model = MyShardedModel().cuda()
sharded_model = MyShardedModel(spec=rowwise_spec).cuda()
# copy the parameters from local model
sharded_model.sharded_param.local_shards()[0].tensor = (
local_model.sharded_param.detach().clone().requires_grad_()
)
local_optim = optim.SGD(local_model.parameters(), lr=0.1)
sharded_model_params = dict(sharded_model.named_parameters())
sharded_optim = ShardedOptimizer(sharded_model_params, optim.SGD, lr=0.1)
local_optim.zero_grad()
sharded_optim.zero_grad()
before_update = deepcopy(sharded_optim.named_params)
inp = torch.rand([5, 10]).cuda(self.rank).requires_grad_()
# run forward
local_output = local_model(inp)
sharded_output = sharded_model(inp)
# backward
local_output.sum().backward()
sharded_output.sum().backward()
# optimizer update
local_optim.step()
sharded_optim.step()
# make sure the parameters (including sharded param)
# get updated by the optimizer, and the updated
# local params are the same as the sharded params
for key, val in before_update.items():
new_val = sharded_optim.named_params[key]
if isinstance(val, sharded_tensor.ShardedTensor):
self.assertNotEqual(
val.local_shards()[0].tensor, new_val.local_shards()[0].tensor
)
self.assertEqual(
new_val.local_shards()[0].tensor, local_model.sharded_param
)
else:
self.assertNotEqual(val, new_val)
self.assertEqual(new_val, local_model.param)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(4)
@requires_nccl()
def test_named_params_with_sharded_tensor(self):
rowwise_spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
sharded_model = MyShardedModel(spec=rowwise_spec).cuda()
sharded_model_params = dict(sharded_model.named_parameters())
param_keys = list(sharded_model_params.keys())
self.assertEqual(len(param_keys), 2)
self.assertTrue("param" in param_keys)
self.assertTrue("sharded_param" in param_keys)
sharded_linear = MyShardedLinear(rank=self.rank).cuda()
sharded_linear.shard_parameter()
sharded_linear_params = dict(sharded_linear.named_parameters())
param_keys = list(sharded_linear_params.keys())
self.assertEqual(len(param_keys), 4)
self.assertTrue("linear1.bias" in param_keys)
self.assertTrue("linear2.bias" in param_keys)
self.assertTrue("linear1.weight" in param_keys)
self.assertTrue("linear2.weight" in param_keys)
self.assertFalse("bias" in param_keys)
if __name__ == "__main__":
run_tests()
|
