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# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._tensor import (
distribute_module,
distribute_tensor,
DTensor,
Replicate,
Shard,
)
from torch.distributed.tensor.debug import CommDebugMode
from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
with_comms,
)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
funcol = torch.ops.c10d_functional
class TestEmbeddingOp(DTensorTestBase):
def _apply_sharding(self, embedding_mod, shard_dim, device_mesh):
def shard_embedding_fn(name, module, device_mesh):
for name, param in module.named_parameters():
dist_param = torch.nn.Parameter(
distribute_tensor(param, device_mesh, [Shard(shard_dim)])
)
module.register_parameter(name, dist_param)
sharded_embedding = distribute_module(
embedding_mod, device_mesh, shard_embedding_fn
)
return sharded_embedding
def _run_embedding_op_test(
self,
device_mesh,
shard_dim,
input_size,
num_embeddings,
embedding_dim,
**kwargs,
):
# Use same seed.
torch.manual_seed(0)
local_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
device=self.device_type,
**kwargs,
)
sharded_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
device=self.device_type,
**kwargs,
)
# Shard the parameter of local embedding and set it to sharded embedding.
sharded_embedding.weight = torch.nn.Parameter(
local_embedding.weight.detach().clone()
)
sharded_embedding = self._apply_sharding(
sharded_embedding, shard_dim, device_mesh
)
# Run sharded computation
torch.manual_seed(10)
inp = torch.randint(
0, num_embeddings, tuple(input_size), device=self.device_type
)
target = torch.empty(
*inp.size(), embedding_dim, dtype=torch.float, device=self.device_type
).random_(0, 1)
dist_inp = distribute_tensor(inp, device_mesh, [Replicate()])
# fwd computation, ensure no comm happened
with CommDebugMode() as fwd_mode:
dist_output = sharded_embedding(dist_inp)
self.assertEqual(fwd_mode.get_total_counts(), 0)
output = dist_output.full_tensor()
# Run local computation
local_output = local_embedding(inp)
# Verify
self.assertEqual(local_output, output)
# Use a sample cross entry loss to verify backward and grad computation.
loss = torch.nn.CrossEntropyLoss()
emb_loss = loss(
output,
target,
)
emb_dup_loss = loss(
local_output,
target,
)
# local embedding backward
emb_dup_loss.backward()
# sharded embedding bwd computation, ensure no comm happened
with CommDebugMode() as bwd_mode:
emb_loss.backward()
self.assertEqual(bwd_mode.get_total_counts(), 0)
gradient = sharded_embedding.weight.grad.full_tensor()
local_grad = local_embedding.weight.grad
# Verify gradient.
self.assertEqual(gradient, local_grad)
# Validate for torch.nn.functional.embedding version.
local_output = torch.nn.functional.embedding(
inp,
local_embedding.weight,
**kwargs,
)
sharded_output = torch.nn.functional.embedding(
DTensor.from_local(inp, device_mesh, [Replicate()], run_check=False),
sharded_embedding.weight,
**kwargs,
)
self.assertEqual(local_output, sharded_output.full_tensor())
@with_comms
def test_sharded_embedding_colwise(self):
mesh = self.build_device_mesh()
self._run_embedding_op_test(mesh, 1, [5, 4], 17, 12)
self._run_embedding_op_test(mesh, 1, [6, 7, 6], 21, 11)
self._run_embedding_op_test(mesh, 1, [8, 6, 5, 4], 23, 13)
self._run_embedding_op_test(mesh, 1, [8, 6, 5, 4, 7], 23, 16)
self._run_embedding_op_test(mesh, 1, [4], 15, 14)
self._run_embedding_op_test(mesh, 1, [34], 15, 14, padding_idx=10)
self._run_embedding_op_test(mesh, 1, [8, 6, 5, 4], 23, 13, padding_idx=12)
@with_comms
def test_sharded_embedding_colwise_max_norm_errors(self):
mesh = self.build_device_mesh()
with self.assertRaisesRegex(
NotImplementedError,
"aten.embedding_renorm_.default does not have a sharding strategy registered.",
):
self._run_embedding_op_test(
mesh, 1, [8, 6, 5, 4], 23, 13, padding_idx=12, max_norm=2.0
)
@with_comms
def test_sharded_embedding_rowwise(self):
mesh = self.build_device_mesh()
# test correctness
self._run_embedding_op_test(mesh, 0, [5, 12], 16, 22)
self._run_embedding_op_test(mesh, 0, [6, 7, 6], 13, 22)
self._run_embedding_op_test(mesh, 0, [34], 15, 14, padding_idx=10)
from torch.distributed.tensor._ops._embedding_ops import _MaskPartial
# test collectives
embedding_mod = torch.nn.Embedding(10, 20, device=self.device_type)
sharded_embedding = self._apply_sharding(embedding_mod, 0, mesh)
inp = torch.randint(0, 10, (8, 8), device=self.device_type)
replicated_inp = DTensor.from_local(inp, mesh, [Replicate()], run_check=False)
output = sharded_embedding(replicated_inp)
self.assertIsInstance(output.placements[0], _MaskPartial)
comm_mode = CommDebugMode()
with comm_mode:
output.full_tensor()
self.assertEqual(comm_mode.get_total_counts(), 1)
self.assertEqual(comm_mode.get_comm_counts()[funcol.all_reduce], 1)
@with_comms
def test_multiple_embeddings_rowwise(self):
mesh = self.build_device_mesh()
inp = torch.randint(0, 10, (4, 4), device=self.device_type)
replicated_inp = DTensor.from_local(inp, mesh, [Replicate()], run_check=False)
from torch.distributed.tensor._ops._embedding_ops import _MaskPartial
# case 1: two embeddings with the same shape, thus sharing the underying _MaskPartial
# and MaskBuffer, because of cache hit from sharding propagation
emb1 = torch.nn.Embedding(10, 23, device=self.device_type)
sharded_emb1 = self._apply_sharding(emb1, 0, mesh)
output1 = sharded_emb1(replicated_inp)
emb2 = torch.nn.Embedding(10, 29, device=self.device_type)
sharded_emb2 = self._apply_sharding(emb2, 0, mesh)
output2 = sharded_emb2(replicated_inp)
partial_placement1 = output1.placements[0]
self.assertIsInstance(partial_placement1, _MaskPartial)
output1.full_tensor()
partial_placement2 = output2.placements[0]
self.assertIsInstance(partial_placement2, _MaskPartial)
output2.full_tensor()
self.assertTrue(id(partial_placement1), id(partial_placement2))
# case 2: two embeddings with the same logical_dim_size, but different logical_shape
# thus they will have different _MaskPartial placements (with no cache hit)
emb3 = torch.nn.Embedding(10, 29, device=self.device_type)
sharded_emb3 = self._apply_sharding(emb3, 0, mesh)
output3 = sharded_emb3(replicated_inp)
partial_placement3 = output3.placements[0]
self.assertIsInstance(partial_placement3, _MaskPartial)
output2.full_tensor()
# not equal because of different logical_shape, despite of same logical_dim_size
self.assertNotEqual(partial_placement1, partial_placement3)
if __name__ == "__main__":
run_tests()
|
