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# Owner(s): ["oncall: distributed"]
import copy
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard.api import (
shard_parameter,
_collect_local_shard,
_reshard_output,
)
from torch.distributed._shard.sharded_optim import (
ShardedOptimizer,
)
from torch.distributed._shard.sharded_tensor import (
empty,
)
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 (
TEST_GPU_NUM,
ShardedTensorTestBase,
with_comms,
)
from torch.testing._internal.distributed._shard.sharded_tensor._test_ops_common import (
clone_module_parameter,
generate_chunk_sharding_specs_for_test,
generate_local_weight_sharding_params_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 TestShardedTensorOpsLinear(ShardedTensorTestBase):
def _run_sharded_linear(
self, spec, input_size, linear_size, sharded_dim, dtype
):
# Use same seed.
torch.manual_seed(0)
local_linear = torch.nn.Linear(*linear_size, dtype=dtype).cuda(self.rank)
sharded_linear = torch.nn.Linear(*linear_size, dtype=dtype)
# Copy the weights and bias from local linear
sharded_linear.weight = clone_module_parameter(local_linear, "weight")
sharded_linear.bias = clone_module_parameter(local_linear, "bias")
# Shard the parameter.
shard_parameter(sharded_linear, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.rand(*input_size, dtype=dtype).cuda(self.rank)
reshard_spec = copy.deepcopy(spec)
reshard_spec.dim = 0
reshard_spec.placements.sort(key=lambda placement: placement.rank())
sharded_linear = _collect_local_shard(
_reshard_output(sharded_linear, reshard_spec)
)
sharded_output = sharded_linear(inp)
# Run local computation
local_output = local_linear(inp)
# Verify
self.assertEqual(local_output, sharded_output, atol=1e-3, rtol=1e-3)
# Validate for torch.nn.functional.linear version.
local_output = torch.nn.functional.linear(
inp, local_linear.weight, local_linear.bias
)
sharded_output = torch.nn.functional.linear(
inp, sharded_linear.weight, sharded_linear.bias
)
sharded_output = sharded_output.reshard(reshard_spec).local_tensor()
# When local tensor only has one dimension, we increase one more dimension
# for reshard. We need to squeeze the # of dimensions manually.
if inp.dim() == 1:
sharded_output = sharded_output.squeeze(reshard_spec.dim)
self.assertEqual(local_output, sharded_output, atol=1e-3, rtol=1e-3)
# Compute loss and run backward pass.
local_output.sum().backward()
sharded_output.sum().backward()
local_grad = local_linear.weight.grad
# Verify that both weight and bias in the sharded linear has non-None grad.
sharded_weight = sharded_linear.weight.local_tensor()
self.assertNotEqual(sharded_linear.bias.grad, None)
self.assertNotEqual(sharded_weight.grad, None)
# Shard the local linear's weight grad so that we can compare.
dist.all_reduce(local_grad)
(start_pos, chunk_size) = generate_local_weight_sharding_params_for_test(
local_linear.weight, sharded_dim, TEST_GPU_NUM, spec, self.rank
)
local_grad_narrowed = local_grad.narrow(sharded_dim, start_pos, chunk_size)
local_bias_grad = local_linear.bias.grad
dist.all_reduce(local_bias_grad)
# Test backward gradient calculation.
self.assertEqual(sharded_linear.bias.grad, local_bias_grad, atol=1e-3, rtol=1e-3)
self.assertEqual(sharded_weight.grad, local_grad_narrowed, atol=1e-3, rtol=1e-3)
# Test optimizer.
previous = local_linear.weight.clone().detach()
optim = torch.optim.SGD(local_linear.parameters(), lr=0.1)
optim.step()
self.assertNotEqual(previous, local_linear.weight)
previous_sharded_weight = sharded_weight.clone()
previous_sharded_bias = sharded_linear.bias.clone()
sharded_optim = ShardedOptimizer(
dict(sharded_linear.named_parameters()),
torch.optim.SGD,
lr=0.1,
)
sharded_optim.step()
sharded_weight = sharded_linear.weight.local_tensor()
local_weight_narrowed = local_linear.weight.narrow(
sharded_dim, start_pos, chunk_size
)
self.assertEqual(sharded_weight.size(), local_weight_narrowed.size())
self.assertNotEqual(previous_sharded_weight, sharded_weight)
self.assertEqual(sharded_weight, local_weight_narrowed, atol=1e-3, rtol=1e-3)
self.assertNotEqual(previous_sharded_bias, sharded_linear.bias)
self.assertEqual(sharded_linear.bias, local_linear.bias, atol=1e-3, rtol=1e-3)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_sharded_linear_colwise(self):
for spec in generate_chunk_sharding_specs_for_test(0):
self._run_sharded_linear(spec, [2, 17], [17, 12], 0, torch.float16)
self._run_sharded_linear(spec, [8, 21], [21, 11], 0, torch.float32)
self._run_sharded_linear(spec, [7, 23], [23, 13], 0, torch.float64)
self._run_sharded_linear(spec, [4, 15], [15, 14], 0, torch.float16)
# Test multiple input dims
self._run_sharded_linear(spec, [10, 2, 17], [17, 12], 0, torch.float32)
self._run_sharded_linear(spec, [13, 8, 21], [21, 11], 0, torch.float64)
self._run_sharded_linear(spec, [27, 7, 23], [23, 13], 0, torch.float16)
self._run_sharded_linear(spec, [100, 12, 4, 15], [15, 14], 0, torch.float32)
# Test single input dim
self._run_sharded_linear(spec, [17], [17, 12], 0, torch.float64)
self._run_sharded_linear(spec, [21], [21, 11], 0, torch.float16)
self._run_sharded_linear(spec, [23], [23, 13], 0, torch.float32)
self._run_sharded_linear(spec, [15], [15, 14], 0, torch.float64)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_sharded_linear_rowwise(self):
for spec in generate_chunk_sharding_specs_for_test(1):
# Test even split.
self._run_sharded_linear(spec, [8, 16], [16, 11], 1, torch.float16)
# Test uneven split.
self._run_sharded_linear(spec, [5, 19], [19, 11], 1, torch.float32)
self._run_sharded_linear(spec, [10, 21], [21, 11], 1, torch.float64)
# Test multiple input dims
self._run_sharded_linear(spec, [13, 8, 16], [16, 11], 1, torch.float16)
self._run_sharded_linear(spec, [10, 5, 19], [19, 11], 1, torch.float32)
self._run_sharded_linear(spec, [12, 15, 10, 21], [21, 11], 1, torch.float64)
# Test single input dim
self._run_sharded_linear(spec, [16], [16, 11], 1, torch.float16)
self._run_sharded_linear(spec, [19], [19, 11], 1, torch.float32)
self._run_sharded_linear(spec, [21], [21, 11], 1, torch.float64)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_sharded_linear_errors(self):
for spec in generate_chunk_sharding_specs_for_test(0):
fc1 = torch.nn.Linear(10, 10).cuda(self.rank)
shard_parameter(fc1, "weight", spec)
shard_parameter(fc1, "bias", spec)
with self.assertRaisesRegex(TypeError, 'bias needs to be torch.Tensor'):
fc1(torch.rand(10, 10).cuda(self.rank))
fc2 = torch.nn.Linear(10, 10).cuda(self.rank)
shard_parameter(fc2, "weight", spec)
with self.assertRaisesRegex(ValueError, 'Input needs to have at least 1 dim'):
fc2(torch.tensor(1).cuda(self.rank))
fc3 = torch.nn.Linear(10, 10).cuda(self.rank)
fc3.weight = torch.nn.Parameter(torch.rand(10, 10, 10).cuda(self.rank))
shard_parameter(fc3, "weight", spec)
with self.assertRaisesRegex(ValueError, 'Weight needs to have exactly 2 dims'):
fc3(torch.rand(10, 10).cuda(self.rank))
fc4 = torch.nn.Linear(10, 10).cuda(self.rank)
fc4.bias = torch.nn.Parameter(torch.rand(10, 10).cuda(self.rank))
shard_parameter(fc4, "weight", spec)
with self.assertRaisesRegex(ValueError, 'Bias needs to have exactly 1 dim'):
fc4(torch.rand(10, 10).cuda(self.rank))
fc5 = torch.nn.Linear(7, 10).cuda(self.rank)
shard_parameter(fc5, "weight", spec)
with self.assertRaisesRegex(ValueError, 'Input dim: 13 does not match appropriate weight dim: 7'):
fc5(torch.rand(20, 10, 13).cuda(self.rank))
fc6 = torch.nn.Linear(10, 10).cuda(self.rank)
del fc6.weight
enumerable_spec = EnumerableShardingSpec([
ShardMetadata(
shard_offsets=[0, 0],
shard_sizes=[5, 5],
placement="rank:0/cuda:0",
),
ShardMetadata(
shard_offsets=[0, 5],
shard_sizes=[5, 5],
placement="rank:1/cuda:1",
),
ShardMetadata(
shard_offsets=[5, 0],
shard_sizes=[5, 5],
placement="rank:2/cuda:2",
),
ShardMetadata(
shard_offsets=[5, 5],
shard_sizes=[5, 5],
placement="rank:3/cuda:3",
)
])
fc6.weight = empty(enumerable_spec, 10, 10)
# Sharded Tensor metadata has parenthesis imbalance issue when using re.compile
error_msg = r"torch function 'linear', with args: (?s).* "
r"and kwargs: None not supported for ShardedTensor!"
with self.assertRaisesRegex(RuntimeError, error_msg):
fc6(torch.rand(10, 10).cuda(self.rank))
fc7 = torch.nn.Linear(10, 80).cuda(self.rank)
multiple_local_shard_spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:2/cuda:2",
"rank:3/cuda:3",
"rank:3/cuda:3",
],
)
del fc7.weight
fc7.weight = empty(multiple_local_shard_spec, 80, 10)
with self.assertRaisesRegex(ValueError, 'Only one local shard supported!'):
fc7(torch.rand(10, 10).cuda(self.rank))
if __name__ == "__main__":
run_tests()
|
