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# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
from functools import partial
import torch
import torch.distributed._functional_collectives as funcol
from torch.distributed._tensor import (
distribute_tensor,
DTensor,
init_device_mesh,
Replicate,
Shard,
)
from torch.distributed._tensor.experimental import local_map
from torch.distributed.tensor.debug import CommDebugMode
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
with_comms,
)
funcol_py = torch.ops.c10d_functional
row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh
col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh
replicate = [Replicate()] # replicate placements on 1-d mesh
def equal_allgather_forward(device_mesh, X, Y):
eq = torch.tensor([torch.equal(X, Y)], device=X.device)
eq_gather = funcol.all_gather_tensor(eq, 0, device_mesh)
return torch.all(eq_gather).item()
def mm_all_gather_forward(device_mesh, A, B):
local_mm_result = torch.mm(A, B)
return funcol.all_gather_tensor(local_mm_result, 0, device_mesh).wait()
def mm_forward(A, B): # no device mesh needed since we don't do collective
return torch.mm(A, B)
def mm_allreduce_forward(device_mesh, A, B):
partial_sum_tensor = torch.mm(A, B)
return funcol.all_reduce(partial_sum_tensor, "sum", device_mesh).wait()
@partial(
local_map,
out_placements=replicate,
in_placements=(None, col_wise, row_wise),
)
def mm_allreduce_forward_decorated(device_mesh, A, B):
partial_sum_tensor = torch.mm(A, B)
return funcol.all_reduce(partial_sum_tensor, "sum", device_mesh).wait()
def mul_forward(X, scalar): # no device mesh needed since we don't do collective
return torch.mul(X, scalar)
class TestLocalMap(DTensorTestBase):
@property
def world_size(self):
return 2
# simple correctness check
@with_comms
def test_local_map_correctness(self):
device_mesh = init_device_mesh(
device_type=self.device_type, mesh_shape=(self.world_size,)
)
comm_mode = CommDebugMode()
# Y = X @ W
X = torch.randn(16, 8, device=self.device_type, requires_grad=False)
W = torch.randn(8, 12, device=self.device_type, requires_grad=False)
Y = torch.mm(X, W)
X_dt = distribute_tensor(
X, device_mesh, col_wise
) # col-wisely sharded X tensor
W_dt = distribute_tensor(
W, device_mesh, row_wise
) # row-wisely sharded W tensor
# Test 1: use the function returned from calling local_map
# get the function wrapped with DTensor/Tensor convertion
# mm_allreduce_forward is a function that applies to Tensors with manual collective
# local_mm_allreduce_forward is the function that does the same but applies to
# DTensors' `_local_tensor`.
local_mm_allreduce_forward = local_map(
mm_allreduce_forward,
out_placements=replicate,
in_placements=(None, col_wise, row_wise),
device_mesh=device_mesh,
)
with comm_mode:
Y_dt = local_mm_allreduce_forward(device_mesh, X_dt, W_dt)
# output redistribution to Replicate
self.assertEqual(comm_mode.get_total_counts(), 1)
# check output placements
for placement in Y_dt.placements:
self.assertTrue(placement.is_replicate())
# check output value
self.assertEqual(Y_dt.to_local(), Y)
# Test 2: use the local_map decorator
with comm_mode:
Y_dt = mm_allreduce_forward_decorated(device_mesh, X_dt, W_dt)
# output redistribution to Replicate
self.assertEqual(comm_mode.get_total_counts(), 1)
# check output placements
for placement in Y_dt.placements:
self.assertTrue(placement.is_replicate())
# check output value
self.assertEqual(Y_dt.to_local(), Y)
# check for `out_placements`
@with_comms
def test_local_map_out_placements(self):
# Test 1: wrap out into DTensor w/ `out_placements`
device_mesh = init_device_mesh(
device_type=self.device_type, mesh_shape=(self.world_size,)
)
comm_mode = CommDebugMode()
# X.equal(Y)
X = torch.randn(8, 8, device=self.device_type, requires_grad=False)
Y = torch.randn(8, 8, device=self.device_type, requires_grad=False)
X_dt = distribute_tensor(X, device_mesh, row_wise)
Y_dt = distribute_tensor(Y, device_mesh, row_wise)
local_equal_allgather_forward = local_map(
equal_allgather_forward,
out_placements=None,
)
with comm_mode:
equal_dt = local_equal_allgather_forward(device_mesh, X_dt, Y_dt) # a bool
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertTrue(not equal_dt)
self.assertTrue(not (X.equal(Y)))
# Test 2: directly return out if no argument is DTensor
# matmul in DDP
X = torch.randn(
4 // self.world_size, 4, device=self.device_type, requires_grad=False
)
W = torch.randn(4, 4, device=self.device_type, requires_grad=False)
local_mm_all_gather_forward = local_map(
mm_all_gather_forward,
out_placements=row_wise,
in_placements=(None, row_wise, replicate),
)
with comm_mode:
Y = local_mm_all_gather_forward(device_mesh, X, W)
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertEqual(
comm_mode.get_comm_counts()[funcol_py.all_gather_into_tensor], 1
)
X_replicate = funcol.all_gather_tensor(X, 0, device_mesh).wait()
Y_replicate = torch.mm(X_replicate, W)
self.assertEqual(Y, Y_replicate) # Y is a torch.Tensor
# check for `in_placements` handling
@with_comms
def test_local_map_in_placements(self):
device_mesh = init_device_mesh(
device_type=self.device_type, mesh_shape=(self.world_size,)
)
comm_mode = CommDebugMode()
# Y = X @ W
X = torch.randn(16, 8, device=self.device_type, requires_grad=False)
W = torch.randn(8, 12, device=self.device_type, requires_grad=False)
Y = torch.mm(X, W)
X_dt = distribute_tensor(
X, device_mesh, row_wise
) # row-wisely sharded X tensor
W_dt = distribute_tensor(W, device_mesh, replicate) # replicate W tensor
# Test 1: explicitly pass `in_placements`
local_mm_forward = local_map(
mm_forward,
out_placements=row_wise,
in_placements=(row_wise, replicate),
device_mesh=device_mesh,
)
with comm_mode:
Y_dt = local_mm_forward(X_dt, W_dt)
# no communication should occur in this case
self.assertEqual(comm_mode.get_total_counts(), 0)
for placement in Y_dt.placements:
self.assertTrue(placement.is_shard(dim=0))
self.assertEqual(Y_dt.full_tensor(), Y)
# Test 2: `in_placements=None`
local_mm_forward = local_map(
mm_forward,
out_placements=row_wise,
device_mesh=device_mesh,
)
with comm_mode:
Y_dt = local_mm_forward(X_dt, W_dt)
self.assertEqual(comm_mode.get_total_counts(), 0)
for placement in Y_dt.placements:
self.assertTrue(placement.is_shard(dim=0))
self.assertEqual(Y_dt.full_tensor(), Y)
# Test 3: `None` placements for non-Tensor input argument
# Y = X * 2.0
local_mul_forward = local_map(
mul_forward,
in_placements=(row_wise, None),
out_placements=row_wise,
device_mesh=device_mesh,
)
Y = torch.mul(X, 2.0)
with comm_mode:
Y_dt = local_mul_forward(X_dt, 2.0)
self.assertEqual(comm_mode.get_total_counts(), 0)
for placement in Y_dt.placements:
self.assertTrue(placement.is_shard(dim=0))
self.assertEqual(Y_dt.full_tensor(), Y)
# Test 4: `None` placements for Tensor input argument
local_mm_forward = local_map(
mm_forward,
out_placements=None,
in_placements=(None, None),
device_mesh=device_mesh,
)
with comm_mode:
Y_dt_local = local_mm_forward(X_dt.to_local(), W_dt.to_local())
self.assertEqual(comm_mode.get_total_counts(), 0)
self.assertEqual(
DTensor.from_local(Y_dt_local, device_mesh, row_wise).full_tensor(),
torch.mm(X, W),
)
# Test 5: Some placements for Tensor input argument
local_mm_forward = local_map(
mm_forward,
out_placements=None,
in_placements=(replicate, row_wise),
device_mesh=device_mesh,
)
with comm_mode:
Y_dt_local = local_mm_forward(X_dt.to_local(), W_dt.to_local())
self.assertEqual(comm_mode.get_total_counts(), 0)
self.assertEqual(
DTensor.from_local(Y_dt_local, device_mesh, row_wise).full_tensor(),
torch.mm(X, W),
)
# Test 6: expect error - `None` placements for DTensor input argument
local_mm_forward = local_map(
mm_forward,
out_placements=row_wise,
in_placements=(row_wise, None),
device_mesh=device_mesh,
)
with self.assertRaisesRegex(AssertionError, "expects placements"):
Y_dt = local_mm_forward(X_dt, W_dt)
# check for `redistribute_inputs` handling
@with_comms
def test_local_map_redistribute(self):
device_mesh = init_device_mesh(
device_type=self.device_type, mesh_shape=(self.world_size,)
)
comm_mode = CommDebugMode()
# Y = X @ W
X = torch.randn(16, 8, device=self.device_type, requires_grad=False)
W = torch.randn(8, 12, device=self.device_type, requires_grad=False)
Y = torch.mm(X, W)
X_dt = distribute_tensor(
X, device_mesh, row_wise
) # row-wisely sharded X tensor which will be redistributed
W_dt = distribute_tensor(
W, device_mesh, col_wise
) # col-wisely sharded W tensor which will be redistributed
# Test 1: allow input redistribution
local_mm_allreduce_forward = local_map(
mm_allreduce_forward,
out_placements=replicate,
in_placements=(None, col_wise, row_wise),
device_mesh=device_mesh,
redistribute_inputs=True,
)
with comm_mode:
Y_dt = local_mm_allreduce_forward(device_mesh, X_dt, W_dt)
# 2 for input redistribution and 1 for output
self.assertEqual(comm_mode.get_total_counts(), 3)
for placement in Y_dt.placements:
self.assertTrue(placement.is_replicate())
self.assertEqual(Y_dt.to_local(), Y)
# Test 2: no input redistribution is allowed
local_mm_allreduce_forward = local_map(
mm_allreduce_forward,
out_placements=replicate,
in_placements=(None, col_wise, row_wise),
device_mesh=device_mesh,
redistribute_inputs=False,
)
with self.assertRaisesRegex(ValueError, "set redistribute_inputs=True"):
Y_dt = local_mm_allreduce_forward(device_mesh, X_dt, W_dt)
if __name__ == "__main__":
run_tests()
|
