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# Owner(s): ["oncall: distributed"]
from itertools import chain
import torch
from torch.distributed._tensor import DeviceMesh, DTensor
from torch.distributed._tensor.placement_types import (
DTensorSpec,
Partial,
Replicate,
Shard,
TensorMeta,
)
from torch.distributed.tensor._collective_utils import redistribute_cost
from torch.distributed.tensor._op_schema import OpSchema, OpStrategy, PlacementStrategy
from torch.distributed.tensor._ops._einsum_strategy import (
EinsumDims,
gen_einsum_strategies,
)
from torch.testing._internal.common_utils import run_tests, TestCase
from torch.testing._internal.distributed._tensor.common_dtensor import DTensorOpTestBase
class TestEinsumDims(TestCase):
def test_batch_dims(self):
equation = "abc,abc->abc"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, ["a", "b", "c"])
self.assertEqual(edims.contracting_dims, [])
self.assertEqual(edims.lhs_out_only_dims, [])
self.assertEqual(edims.rhs_out_only_dims, [])
def test_mm_dims(self):
equation = "mk,kn->mn"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, [])
self.assertEqual(edims.contracting_dims, ["k"])
self.assertEqual(edims.lhs_out_only_dims, ["m"])
self.assertEqual(edims.rhs_out_only_dims, ["n"])
def test_bmm_dims(self):
equation = "bmk,bkn->bmn"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, ["b"])
self.assertEqual(edims.contracting_dims, ["k"])
self.assertEqual(edims.lhs_out_only_dims, ["m"])
self.assertEqual(edims.rhs_out_only_dims, ["n"])
equation = "bcmk,bckn->bcmn"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, ["b", "c"])
self.assertEqual(edims.contracting_dims, ["k"])
self.assertEqual(edims.lhs_out_only_dims, ["m"])
self.assertEqual(edims.rhs_out_only_dims, ["n"])
def test_free_dims(self):
equation = "abc,ab->abc"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, ["a", "b"])
self.assertEqual(edims.contracting_dims, [])
self.assertEqual(edims.lhs_out_only_dims, ["c"])
self.assertEqual(edims.rhs_out_only_dims, [])
equation = "abd,bf->abfd"
input_dims, output_dim = EinsumDims.parse_equation(equation)
edims = EinsumDims.parse_dims(input_dims, output_dim)
self.assertEqual(edims.batch_dims, ["b"])
self.assertEqual(edims.contracting_dims, [])
self.assertEqual(edims.lhs_out_only_dims, ["a", "d"])
self.assertEqual(edims.rhs_out_only_dims, ["f"])
class TestEinsumStrategies(DTensorOpTestBase):
@property
def world_size(self) -> int:
return 4
def test_mm_1d_mesh(self):
mesh = self.build_device_mesh()
all_strats = gen_einsum_strategies("mk,kn->mn", mesh)
self.assertEqual(len(all_strats.strategies), 4)
def test_mm_2d_mesh(self):
mesh = DeviceMesh(self.device_type, torch.arange(self.world_size).reshape(2, 2))
all_strats = gen_einsum_strategies("mk,kn->mn", mesh)
self.assertEqual(len(all_strats.strategies), 16)
def test_bmm_1d_mesh(self):
mesh = self.build_device_mesh()
all_strats = gen_einsum_strategies("bmk,bkn->bmn", mesh)
self.assertEqual(len(all_strats.strategies), 5)
def test_bmm_2d_mesh(self):
mesh = DeviceMesh(self.device_type, torch.arange(self.world_size).reshape(2, 2))
all_strats = gen_einsum_strategies("bmk,bkn->bmn", mesh)
self.assertEqual(len(all_strats.strategies), 25)
def test_pointwise_1d_mesh(self):
mesh = self.build_device_mesh()
simple_strats = gen_einsum_strategies("abcd,abcd->abcd", mesh)
self.assertEqual(len(simple_strats.strategies), 5)
broadcast_strats = gen_einsum_strategies("bcd,abcd->abcd", mesh)
self.assertEqual(len(broadcast_strats.strategies), 5)
def test_linearity_1d_mesh(self):
mesh = self.build_device_mesh()
all_strats = gen_einsum_strategies("abcd,abcd->abcd", mesh, linearity=True)
self.assertEqual(len(all_strats.strategies), 6)
class TestCostModel(DTensorOpTestBase):
def _extract_tensor_meta(self, t) -> TensorMeta:
return TensorMeta(t.shape, t.stride(), t.dtype)
@property
def world_size(self) -> int:
return 4
def test_redistribute_cost_mesh_1d(self):
mesh_1d = self.build_device_mesh()
shard_placement = (Shard(0),)
replica_placement = (Replicate(),)
partial_placement = (Partial(),)
global_tensor = torch.randn(10, 10)
global_tensor_meta = self._extract_tensor_meta(global_tensor)
# shard spec
shard_spec = DTensorSpec(mesh_1d, shard_placement, global_tensor_meta)
# replica spec
replica_spec = DTensorSpec(mesh_1d, replica_placement, global_tensor_meta)
# partial spec
partial_spec = DTensorSpec(mesh_1d, partial_placement, global_tensor_meta)
# make sure reshard cost is 0 for the same spec redistribute
for spec in [shard_spec, replica_spec, partial_spec]:
cost = redistribute_cost(spec, spec)
self.assertEqual(cost, 0)
# shard -> replicate
allgather_cost = redistribute_cost(shard_spec, replica_spec)
# partial -> shard
reduce_scatter_cost = redistribute_cost(partial_spec, shard_spec)
# partial -> replicate
allreduce_cost = redistribute_cost(partial_spec, replica_spec)
self.assertEqual(allgather_cost, reduce_scatter_cost)
self.assertTrue(allreduce_cost + 1 < allgather_cost + reduce_scatter_cost)
# shard to partial
cost = redistribute_cost(shard_spec, partial_spec)
self.assertEqual(cost, float("inf"))
def test_redistribute_cost_latency(self):
# test cost model on addmm op
from torch.distributed.tensor._ops._matrix_ops import addmm_strategy
mesh = self.build_device_mesh()
shard0_placement = (Shard(0),)
partial_placement = (Partial(),)
shard1_placement = (Shard(1),)
shard0_tensor_meta = self._extract_tensor_meta(torch.randn(8))
partial_tensor_meta = self._extract_tensor_meta(torch.randn(50, 6))
shard1_tensor_meta = self._extract_tensor_meta(torch.randn(6, 8))
# shard spec
shard0_spec = DTensorSpec(mesh, shard0_placement, shard0_tensor_meta)
# replica spec
partial_spec = DTensorSpec(mesh, partial_placement, partial_tensor_meta)
# partial spec
shard1_spec = DTensorSpec(mesh, shard1_placement, shard1_tensor_meta)
op_schema = OpSchema(
torch.ops.aten.addmm.default,
(
OpStrategy([PlacementStrategy(shard0_spec)]),
OpStrategy([PlacementStrategy(partial_spec)]),
OpStrategy([PlacementStrategy(shard1_spec)]),
),
{},
)
output_strategy = addmm_strategy(mesh, op_schema)
strategy_costs = {}
for strategy in output_strategy.strategies:
redistribute_cost = sum(chain.from_iterable(strategy.redistribute_cost))
strategy_costs[str(strategy)] = redistribute_cost
# assert that cost model counts for collective latency (i.e. multiple comm is penalized)
self.assertTrue(
strategy_costs["(S(0), R, S(1)) -> S(1)"]
< strategy_costs["(R, S(0), R) -> S(0)"]
)
# assert a single allreduce is the best one
self.assertEqual(
strategy_costs["(S(0), R, S(1)) -> S(1)"], min(strategy_costs.values())
)
def test_redistribute_cost_mesh_2d(self):
mesh_2d = DeviceMesh(
self.device_type, torch.arange(self.world_size).reshape(2, 2)
)
shard_placement = (Shard(0), Shard(0))
replica_placement = (Replicate(), Replicate())
partial_placement = (Partial(), Partial())
global_tensor = torch.randn(8, 8)
global_tensor_meta = self._extract_tensor_meta(global_tensor)
# shard spec
shard_spec = DTensorSpec(mesh_2d, shard_placement, global_tensor_meta)
# replica spec
replica_spec = DTensorSpec(mesh_2d, replica_placement, global_tensor_meta)
# partial spec
partial_spec = DTensorSpec(mesh_2d, partial_placement, global_tensor_meta)
# make sure reshard cost is 0 for the same spec redistribute
for spec in [shard_spec, replica_spec, partial_spec]:
cost = redistribute_cost(spec, spec)
self.assertEqual(cost, 0)
# shard -> replicate
allgather_cost = redistribute_cost(shard_spec, replica_spec)
# partial -> replicate
allreduce_cost = redistribute_cost(partial_spec, replica_spec)
# partial -> shard
reduce_scatter_cost = redistribute_cost(partial_spec, shard_spec)
self.assertTrue(allreduce_cost > allgather_cost)
self.assertTrue(allreduce_cost > reduce_scatter_cost)
def test_mm_strategies(self):
from torch.distributed.tensor._ops._matrix_ops import mm_strategy
mesh = self.build_device_mesh()
lhs_tensor = torch.randn(6, 8)
rhs_tensor = torch.randn(8, 12)
lhs_tensor_meta = self._extract_tensor_meta(lhs_tensor)
rhs_tensor_meta = self._extract_tensor_meta(rhs_tensor)
mm_combs = (
(Shard(0), Replicate()),
(Replicate(), Shard(1)),
(Shard(1), Shard(0)),
(Replicate(), Replicate()),
)
for lhs, rhs in mm_combs:
lhs_spec = DTensorSpec(mesh, (lhs,), lhs_tensor_meta)
rhs_spec = DTensorSpec(mesh, (rhs,), rhs_tensor_meta)
op_schema = OpSchema(
torch.ops.aten.mm.default,
(
OpStrategy([PlacementStrategy(lhs_spec)]),
OpStrategy([PlacementStrategy(rhs_spec)]),
),
{},
)
# test the strategy
res_strategies = mm_strategy(mesh, op_schema)
for strtgy in res_strategies.strategies:
if strtgy.input_specs == (lhs_spec, rhs_spec):
self.assertEqual(strtgy.redistribute_cost, [[0.0], [0.0]])
break
op_schema = OpSchema(
torch.ops.aten.mm.default,
(lhs_spec, rhs_spec),
{},
)
# test sharding prop
output_sharding = DTensor._op_dispatcher.sharding_propagator.propagate_op_sharding_non_cached(
op_schema
)
self.assertFalse(output_sharding.needs_redistribute)
def test_bmm_strategies(self):
from torch.distributed.tensor._ops._matrix_ops import bmm_strategy
mesh = self.build_device_mesh()
lhs_tensor = torch.randn(8, 6, 8)
rhs_tensor = torch.randn(8, 8, 12)
lhs_tensor_meta = self._extract_tensor_meta(lhs_tensor)
rhs_tensor_meta = self._extract_tensor_meta(rhs_tensor)
bmm_combs = (
(Shard(0), Shard(0)),
(Shard(1), Replicate()),
(Replicate(), Shard(2)),
(Shard(2), Shard(1)),
(Replicate(), Replicate()),
)
for lhs, rhs in bmm_combs:
lhs_spec = DTensorSpec(mesh, (lhs,), lhs_tensor_meta)
rhs_spec = DTensorSpec(mesh, (rhs,), rhs_tensor_meta)
op_schema = OpSchema(
torch.ops.aten.bmm.default,
(
OpStrategy([PlacementStrategy(lhs_spec)]),
OpStrategy([PlacementStrategy(rhs_spec)]),
),
{},
)
# test the strategy
res_strategies = bmm_strategy(mesh, op_schema)
for strtgy in res_strategies.strategies:
if strtgy.input_specs == (lhs_spec, rhs_spec):
self.assertEqual(strtgy.redistribute_cost, [[0.0], [0.0]])
break
op_schema = OpSchema(
torch.ops.aten.bmm.default,
(lhs_spec, rhs_spec),
{},
)
# test sharding prop
output_sharding = DTensor._op_dispatcher.sharding_propagator.propagate_op_sharding_non_cached(
op_schema
)
self.assertFalse(output_sharding.needs_redistribute)
if __name__ == "__main__":
run_tests()
|
