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# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
from copy import deepcopy
import torch
import torch.nn as nn
from torch.distributed._tensor import (
distribute_tensor,
DTensor,
init_device_mesh,
Replicate,
Shard,
)
from torch.distributed.tensor.debug import CommDebugMode
from torch.distributed.tensor.parallel import parallelize_module
from torch.distributed.tensor.parallel.style import (
ColwiseParallel,
PrepareModuleInput,
PrepareModuleOutput,
RowwiseParallel,
SequenceParallel,
)
from torch.distributed.tensor.placement_types import _Partial
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
NUM_DEVICES,
RMSNormPython,
with_comms,
)
c10d_functional = torch.ops.c10d_functional
class TensorParallelStyleTest(DTensorTestBase):
@property
def world_size(self):
return NUM_DEVICES
@with_comms
def test_colwise_parallel_style(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
comm_mode = CommDebugMode()
tensor = torch.rand(8, 16, device=self.device_type, requires_grad=True)
model = nn.Linear(16, 16, device=self.device_type)
default_col_parallel = ColwiseParallel()
colwise_mod = parallelize_module(deepcopy(model), mesh, default_col_parallel)
with comm_mode:
out = colwise_mod(tensor)
# ensure output shard on the last dim
self.assertEqual(out.shape, (8, 16 // self.world_size))
# ensure no communication happened in fwd
self.assertEqual(comm_mode.get_total_counts(), 0)
out.sum().backward()
# allreduce in bwd
self.assertEqual(comm_mode.get_comm_counts()[c10d_functional.all_reduce], 1)
self.assertEqual(comm_mode.get_total_counts(), 1)
sharded_col_parallel = ColwiseParallel(input_layouts=Shard(0))
colwise_mod = parallelize_module(deepcopy(model), mesh, sharded_col_parallel)
with comm_mode:
out = colwise_mod(tensor)
# ensure output shard on the last dim
self.assertEqual(out.shape, (8 * self.world_size, 16 // self.world_size))
# allgather in fwd
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.all_gather_into_tensor], 1
)
self.assertEqual(comm_mode.get_total_counts(), 1)
out.sum().backward()
# reduce_scatter in bwd
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.reduce_scatter_tensor], 1
)
self.assertEqual(comm_mode.get_total_counts(), 2)
@with_comms
def test_colwise_parallel_embedding(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
comm_mode = CommDebugMode()
tensor = torch.arange(8, device=self.device_type).reshape(4, 2)
model = nn.Embedding(16, 16, device=self.device_type)
default_col_parallel = ColwiseParallel()
colwise_mod = parallelize_module(deepcopy(model), mesh, default_col_parallel)
with comm_mode:
out = colwise_mod(tensor)
# ensure output shard on the last dim
self.assertEqual(out.shape, (4, 2, 16 // self.world_size))
# ensure no communication happened in fwd
self.assertEqual(comm_mode.get_total_counts(), 0)
out.sum().backward()
# no comm in bwd
self.assertEqual(comm_mode.get_total_counts(), 0)
@with_comms
def test_rowwise_parallel_style(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
comm_mode = CommDebugMode()
tensor = torch.rand(
8, 16 // self.world_size, device=self.device_type, requires_grad=True
)
model = nn.Linear(16, 16, device=self.device_type)
default_row_parallel = RowwiseParallel()
rowwise_mod = parallelize_module(deepcopy(model), mesh, default_row_parallel)
with comm_mode:
out = rowwise_mod(tensor)
# ensure output replicated
self.assertEqual(out.shape, (8, 16))
# allreduce in fwd
self.assertEqual(comm_mode.get_comm_counts()[c10d_functional.all_reduce], 1)
self.assertEqual(comm_mode.get_total_counts(), 1)
out.sum().backward()
# no op in bwd
self.assertEqual(comm_mode.get_total_counts(), 1)
sharded_row_parallel = RowwiseParallel(output_layouts=Shard(0))
rowwise_mod = parallelize_module(deepcopy(model), mesh, sharded_row_parallel)
with comm_mode:
out = rowwise_mod(tensor)
# ensure output replicated
self.assertEqual(out.shape, (8 // self.world_size, 16))
# reduce_scatter in fwd
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.reduce_scatter_tensor], 1
)
self.assertEqual(comm_mode.get_total_counts(), 1)
out.sum().backward()
# allgather in bwd
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.all_gather_into_tensor], 1
)
self.assertEqual(comm_mode.get_total_counts(), 2)
@with_comms
def test_rowwise_parallel_embedding(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
comm_mode = CommDebugMode()
tensor = torch.arange(8, device=self.device_type).reshape(4, 2)
model = nn.Embedding(16, 16, device=self.device_type)
rowwise_mod = parallelize_module(
deepcopy(model), mesh, RowwiseParallel(input_layouts=Replicate())
)
with comm_mode:
out = rowwise_mod(tensor)
# ensure output shard on the last dim
self.assertEqual(out.shape, (4, 2, 16))
# ensure allreduce communication happened in fwd
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertEqual(comm_mode.get_comm_counts()[c10d_functional.all_reduce], 1)
out.sum().backward()
# no comm in bwd
self.assertEqual(comm_mode.get_total_counts(), 1)
sharded_row_parallel = RowwiseParallel(
input_layouts=Replicate(), output_layouts=Shard(1)
)
rowwise_mod = parallelize_module(deepcopy(model), mesh, sharded_row_parallel)
inp_indices = torch.arange(8, device=self.device_type)
with comm_mode:
out = rowwise_mod(inp_indices)
# ensure output shard on the last dim
self.assertEqual(out.shape, (8, 16 // self.world_size))
# reduce scatter in fwd
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.reduce_scatter_tensor], 1
)
out.sum().backward()
# allgather comm in bwd
self.assertEqual(comm_mode.get_total_counts(), 2)
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.all_gather_into_tensor], 1
)
@with_comms
def test_prepare_module_input(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
tensor = torch.ones(2, 16, device=self.device_type)
expected_tensor = torch.ones(2 * self.world_size, 16, device=self.device_type)
prepare_inp_style = PrepareModuleInput(
input_layouts=Shard(0), desired_input_layouts=Replicate()
)
model = nn.Identity()
allgather_mod = parallelize_module(model, mesh, prepare_inp_style)
output = allgather_mod(tensor).full_tensor()
self.assertEqual(output, expected_tensor)
@with_comms
def test_prepare_module_input_multiple_inputs(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
class TestModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(8, 8)
def forward(self, x, y):
return self.linear(x) + y
# Raise assertion error if input_layouts and desired_input_layouts do not have same length.
test_mod = TestModule().to(self.device_type)
with self.assertRaisesRegex(
AssertionError,
"input_layouts and desired_input_layouts should have same length!",
):
prepare_inps_dimension_mismatch = PrepareModuleInput(
input_layouts=Shard(0), desired_input_layouts=(Replicate(), None)
)
# Raise assertion error if module inputs and input_layouts do not have same length.
prepare_inps_short_dimension = PrepareModuleInput(
input_layouts=Shard(0), desired_input_layouts=Replicate()
)
parallelize_module(test_mod.linear, mesh, ColwiseParallel())
parallelize_module(test_mod, mesh, prepare_inps_short_dimension)
with self.assertRaisesRegex(
ValueError, "module inputs and input_layouts should have same length!"
):
output = test_mod(
torch.randn(2, 8, device=self.device_type),
torch.ones(
self.world_size * 2, 8 // self.world_size, device=self.device_type
),
)
test_mod = TestModule().to(self.device_type)
prepare_inps = PrepareModuleInput(
input_layouts=(Shard(0), None), desired_input_layouts=(Replicate(), None)
)
parallelize_module(test_mod.linear, mesh, ColwiseParallel())
parallelize_module(test_mod, mesh, prepare_inps)
output = test_mod(
torch.randn(2, 8, device=self.device_type),
torch.ones(
self.world_size * 2, 8 // self.world_size, device=self.device_type
),
)
self.assertEqual(output.shape, (self.world_size * 2, 8 // self.world_size))
@with_comms
def test_prepare_module_kwargs_input(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
class TestKwargModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(8, 8)
def forward(self, x, *, y, z=2):
return self.linear(x) + y + z
test_mod = TestKwargModule().to(self.device_type)
prepare_inps_simple = PrepareModuleInput(
input_kwarg_layouts={"y": Shard(0)},
desired_input_kwarg_layouts={"y": Replicate()},
)
parallelize_module(
test_mod.linear, mesh, ColwiseParallel(use_local_output=False)
)
parallelize_module(test_mod, mesh, prepare_inps_simple)
comm_mode = CommDebugMode()
with comm_mode:
output = test_mod(
torch.randn(1 * self.world_size, 8, device=self.device_type),
y=torch.ones(1, 8, device=self.device_type),
)
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertEqual(output.shape, (1 * self.world_size, 8))
class TestKwargOnlyModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(8, 8)
def forward(self, *, x, y=2, z=None):
return self.linear(x) + y + z
test_kwonly_mod = TestKwargOnlyModule().to(self.device_type)
prepare_inps_simple = PrepareModuleInput(
input_kwarg_layouts={"x": Shard(0), "z": Shard(0)},
desired_input_kwarg_layouts={"x": Replicate(), "z": Replicate()},
)
parallelize_module(
test_kwonly_mod.linear, mesh, ColwiseParallel(use_local_output=False)
)
parallelize_module(test_kwonly_mod, mesh, prepare_inps_simple)
with comm_mode:
output = test_kwonly_mod(
x=torch.randn(1, 8, device=self.device_type),
z=torch.ones(1, 8, device=self.device_type),
)
self.assertEqual(comm_mode.get_total_counts(), 2)
self.assertEqual(output.shape, (1 * self.world_size, 8))
# test the case where x is a DTensor
x_dt = DTensor.from_local(
torch.randn(1, 8, device=self.device_type), mesh, [Shard(0)]
)
with comm_mode:
output = test_kwonly_mod(
x=x_dt, z=torch.ones(1, 8, device=self.device_type)
)
self.assertEqual(comm_mode.get_total_counts(), 2)
self.assertEqual(output.shape, (1 * self.world_size, 8))
@with_comms
def test_prepare_module_output(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
tensor = torch.ones(8, 16, device=self.device_type)
expected_tensor = torch.ones(8 // self.world_size, 16, device=self.device_type)
prepare_out_style = PrepareModuleOutput(
output_layouts=Replicate(), desired_output_layouts=Shard(0)
)
model = nn.Identity()
chunk_mod = parallelize_module(model, mesh, prepare_out_style)
output = chunk_mod(tensor)
self.assertEqual(output, expected_tensor)
@with_comms
def test_sequence_parallel_style(self):
mesh = init_device_mesh(self.device_type, (self.world_size,))
comm_mode = CommDebugMode()
batch, N, embedding_dim = 20, 8, 12
global_input = torch.rand(
batch,
N * self.world_size,
embedding_dim,
device=self.device_type,
requires_grad=True,
)
sharded_input = distribute_tensor(global_input, mesh, [Shard(1)])
# test LayerNorm
for elementwise_affine in [True, False]:
norm = nn.LayerNorm(
embedding_dim,
elementwise_affine=elementwise_affine,
device=self.device_type,
)
sp_norm = parallelize_module(deepcopy(norm), mesh, SequenceParallel())
output = norm(global_input)
output.sum().backward()
with comm_mode:
sharded_out = sp_norm(sharded_input)
grad_out = torch.ones_like(sharded_out)
sharded_out.backward(grad_out)
self.assertIsInstance(sharded_out, DTensor)
self.assertEqual(sharded_out.placements, (Shard(1),))
self.assertEqual(comm_mode.get_total_counts(), 0)
self.assertEqual(
comm_mode.get_comm_counts()[c10d_functional.all_reduce], 0
)
if elementwise_affine:
self.assertEqual(sp_norm.weight.grad.placements, (_Partial(),))
self.assertEqual(sp_norm.bias.grad.placements, (_Partial(),))
self.assertEqual(sharded_out.full_tensor(), output)
# test RMSNorm
rmsnorm = RMSNormPython(embedding_dim).to(self.device_type)
sp_rmsnorm = parallelize_module(deepcopy(rmsnorm), mesh, SequenceParallel())
output = rmsnorm(global_input)
output.sum().backward()
with comm_mode:
sharded_out = sp_rmsnorm(sharded_input)
grad_out = torch.ones_like(sharded_out)
sharded_out.backward(grad_out)
self.assertIsInstance(sharded_out, DTensor)
self.assertEqual(sharded_out.placements, (Shard(1),))
self.assertEqual(sp_rmsnorm.weight.grad.placements, (_Partial(),))
self.assertEqual(comm_mode.get_total_counts(), 0)
self.assertEqual(comm_mode.get_comm_counts()[c10d_functional.all_reduce], 0)
self.assertEqual(sharded_out.full_tensor(), output)
# test dropout
dropout = nn.Dropout(0.5).to(self.device_type)
sp_dropout = parallelize_module(deepcopy(dropout), mesh, SequenceParallel())
output = dropout(global_input)
output.sum().backward()
with comm_mode:
sharded_out = sp_dropout(sharded_input)
grad_out = torch.ones_like(sharded_out)
sharded_out.backward(grad_out)
self.assertIsInstance(sharded_out, DTensor)
self.assertEqual(sharded_out.placements, (Shard(1),))
self.assertEqual(comm_mode.get_total_counts(), 0)
# test sharded on non-sequence dim input
sharded_batch_input = distribute_tensor(global_input, mesh, [Shard(0)])
rmsnorm = RMSNormPython(embedding_dim).to(self.device_type)
sp_rmsnorm = parallelize_module(deepcopy(rmsnorm), mesh, SequenceParallel())
with comm_mode:
sharded_out = sp_rmsnorm(sharded_batch_input)
grad_out = torch.ones_like(sharded_out)
sharded_out.backward(grad_out)
self.assertIsInstance(sharded_out, DTensor)
# output still sharded on sequence dimension
self.assertEqual(sharded_out.placements, (Shard(1),))
self.assertEqual(sp_rmsnorm.weight.grad.placements, (_Partial(),))
# communication happens in both fwd/bwd to redistribute input
self.assertEqual(comm_mode.get_total_counts(), 2)
if __name__ == "__main__":
run_tests()
|
