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|
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed.checkpoint as dcp
import torch.nn as nn
from torch.distributed._shard.sharded_tensor import (
Shard,
ShardedTensor,
ShardedTensorMetadata,
ShardMetadata,
)
from torch.distributed._shard.sharded_tensor.metadata import (
TensorProperties as TensorProperties_Shard,
)
from torch.distributed.checkpoint._dedup_save_plans import dedup_save_plans
from torch.distributed.checkpoint.api import CheckpointException
from torch.distributed.checkpoint.default_planner import (
_create_default_local_metadata,
create_default_global_save_plan,
create_default_local_load_plan,
create_default_local_save_plan,
DefaultLoadPlanner,
)
from torch.distributed.checkpoint.metadata import (
BytesStorageMetadata,
ChunkStorageMetadata,
MetadataIndex,
TensorProperties,
TensorStorageMetadata,
)
from torch.distributed.checkpoint.planner import LoadItemType, WriteItemType
from torch.distributed.checkpoint.planner_helpers import (
create_read_items_for_chunk_list,
)
from torch.testing._internal.common_utils import (
run_tests,
TEST_WITH_DEV_DBG_ASAN,
TestCase,
)
from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
from torch.testing._internal.distributed.distributed_utils import (
with_dist,
with_fake_comms,
)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
def create_sharded_tensor(rank, world_size, shards_per_rank, shard_size=8):
shards_metadata = []
local_shards = []
for idx in range(0, world_size * shards_per_rank):
shard_rank = idx // shards_per_rank
shard_md = ShardMetadata(
shard_offsets=[idx * shard_size],
shard_sizes=[shard_size],
placement=f"rank:{shard_rank}/cpu",
)
shards_metadata.append(shard_md)
if shard_rank == rank:
shard = Shard.from_tensor_and_offsets(
torch.rand(*shard_md.shard_sizes),
shard_offsets=shard_md.shard_offsets,
rank=rank,
)
local_shards.append(shard)
sharded_tensor_md = ShardedTensorMetadata(
shards_metadata=shards_metadata,
size=torch.Size([shard_size * len(shards_metadata)]),
tensor_properties=TensorProperties_Shard.create_from_tensor(torch.zeros(1)),
)
return ShardedTensor._init_from_local_shards_and_global_metadata(
local_shards=local_shards, sharded_tensor_metadata=sharded_tensor_md
)
class TestSavePlan(TestCase):
@with_fake_comms(rank=1, world_size=4)
def test_local_plan(self):
tensor = torch.rand(10)
val = [1, 2, 3]
st = create_sharded_tensor(rank=1, world_size=4, shards_per_rank=1)
state_dict = {"tensor": tensor, "value": val, "st": st}
plan = create_default_local_save_plan(state_dict, False)
self.assertEqual(3, len(plan.items))
wi = plan.items[0]
self.assertEqual(wi.index, MetadataIndex("tensor", [0]))
self.assertEqual(wi.type, WriteItemType.TENSOR)
self.assertEqual(wi.tensor_data.size, tensor.size())
self.assertEqual(
wi.tensor_data.properties,
TensorProperties.create_from_tensor(torch.zeros(1)),
)
self.assertEqual(wi.tensor_data.chunk.offsets, torch.Size([0]))
self.assertEqual(wi.tensor_data.chunk.sizes, torch.Size([10]))
st_wi = plan.items[2]
self.assertEqual(st_wi.index, MetadataIndex("st", [8]))
self.assertEqual(st_wi.type, WriteItemType.SHARD)
self.assertEqual(st_wi.tensor_data.size, st.size())
self.assertEqual(
st_wi.tensor_data.properties,
TensorProperties.create_from_tensor(torch.zeros(1)),
)
self.assertEqual(st_wi.tensor_data.chunk.offsets, torch.Size([8]))
self.assertEqual(st_wi.tensor_data.chunk.sizes, torch.Size([8]))
# Coordinator rank, should include replicated items as well
plan = create_default_local_save_plan(state_dict, True)
self.assertEqual(3, len(plan.items))
tensor_wi = next(wi for wi in plan.items if wi.type == WriteItemType.TENSOR)
self.assertEqual(tensor_wi.index, MetadataIndex("tensor", [0]))
self.assertEqual(tensor_wi.tensor_data.size, tensor.size())
self.assertEqual(
tensor_wi.tensor_data.properties,
TensorProperties.create_from_tensor(tensor),
)
self.assertEqual(tensor_wi.tensor_data.chunk.offsets, torch.Size([0]))
self.assertEqual(tensor_wi.tensor_data.chunk.sizes, torch.Size([10]))
bytes_wi = next(wi for wi in plan.items if wi.type == WriteItemType.BYTE_IO)
self.assertEqual(bytes_wi.index, MetadataIndex("value"))
self.assertIsNone(bytes_wi.tensor_data)
def test_global_plan(self):
def create_data(rank):
with with_dist(rank=rank, world_size=4):
tensor = torch.rand(10)
val = [1, 2, 3]
st = create_sharded_tensor(rank=rank, world_size=4, shards_per_rank=1)
state_dict = {"tensor": tensor, "value": val, "st": st}
return create_default_local_save_plan(state_dict, rank == 0)
all_plans = [create_data(0), create_data(1), create_data(2), create_data(3)]
all_plans = dedup_save_plans(all_plans)
final_plans, metadata = create_default_global_save_plan(all_plans=all_plans)
# The default global plan updates all indexes to include hints
for new_plan, old_plan in zip(final_plans, all_plans):
for new_item, old_item in zip(new_plan.items, old_plan.items):
self.assertEqual(new_item.index, old_item.index)
self.assertEqual(new_item.type, old_item.type)
self.assertEqual(new_item.tensor_data, old_item.tensor_data)
self.assertIn(new_item.index.fqn, metadata.state_dict_metadata)
item_md = metadata.state_dict_metadata[new_item.index.fqn]
if new_item.type == WriteItemType.BYTE_IO:
self.assertTrue(isinstance(item_md, BytesStorageMetadata))
else:
self.assertTrue(isinstance(item_md, TensorStorageMetadata))
self.assertEqual(item_md.size, old_item.tensor_data.size)
self.assertEqual(
item_md.properties, old_item.tensor_data.properties
)
self.assertIsNotNone(new_item.index.index)
# Make sure the hint is correct
self.assertEqual(
item_md.chunks[new_item.index.index], old_item.tensor_data.chunk
)
def test_local_load_plan(self):
def create_state_dict(rank):
with with_dist(rank=rank, world_size=4):
tensor = torch.rand(10)
val = [1, 2, 3]
st = create_sharded_tensor(rank=rank, world_size=4, shards_per_rank=1)
return {"tensor": tensor, "value": val, "st": st}
state_dict = create_state_dict(1)
metadata = _create_default_local_metadata(state_dict)
load_plan = create_default_local_load_plan(state_dict, metadata)
# This will create 3 entries
self.assertEqual(3, len(load_plan.items))
st_item = next(ri for ri in load_plan.items if ri.dest_index.fqn == "st")
tensor_item = next(
ri for ri in load_plan.items if ri.dest_index.fqn == "tensor"
)
bytes_item = next(ri for ri in load_plan.items if ri.dest_index.fqn == "value")
self.assertEqual(st_item.type, LoadItemType.TENSOR)
# This is an exact copy
self.assertEqual(st_item.dest_index, MetadataIndex("st", [8]))
self.assertEqual(st_item.dest_offsets, torch.Size([0]))
self.assertEqual(st_item.storage_index, MetadataIndex("st", [8]))
self.assertEqual(st_item.storage_offsets, torch.Size([0]))
self.assertEqual(st_item.lengths, torch.Size([8]))
self.assertEqual(tensor_item.type, LoadItemType.TENSOR)
self.assertEqual(tensor_item.dest_index, MetadataIndex("tensor", [0]))
self.assertEqual(tensor_item.dest_offsets, torch.Size([0]))
self.assertEqual(tensor_item.storage_index, MetadataIndex("tensor", [0]))
self.assertEqual(tensor_item.storage_offsets, torch.Size([0]))
self.assertEqual(tensor_item.lengths, torch.Size([10]))
self.assertEqual(bytes_item.type, LoadItemType.BYTE_IO)
self.assertEqual(bytes_item.dest_index, MetadataIndex("value"))
def test_load_with_resharding(self):
def create_state_dict(rank, world_size):
with with_dist(rank=rank, world_size=world_size):
return {
"st": create_sharded_tensor(
rank=rank,
world_size=world_size,
shards_per_rank=1,
shard_size=128 // world_size,
)
}
# Rank 1 has a 16 bytes shard from [16, 32[
world8_state_dict = create_state_dict(rank=1, world_size=8)
world8_metadata = _create_default_local_metadata(world8_state_dict)
# Rank 1 has a 32 bytes shard from [32, 64[
world4_state_dict = create_state_dict(rank=1, world_size=4)
world4_metadata = _create_default_local_metadata(world4_state_dict)
# First scenario, going from world=8 to world=4, need to load 2 shards
# Each 4-world shard has 32 elements, so it needs to load 2 shards
load_plan = create_default_local_load_plan(world4_state_dict, world8_metadata)
self.assertEqual(2, len(load_plan.items))
low_ri = next(
ri for ri in load_plan.items if ri.dest_offsets == torch.Size([0])
)
high_ri = next(
ri for ri in load_plan.items if ri.dest_offsets == torch.Size([16])
)
self.assertEqual(low_ri.storage_index, MetadataIndex("st", [32]))
self.assertEqual(low_ri.storage_offsets, torch.Size([0]))
self.assertEqual(low_ri.dest_index, MetadataIndex("st", [32]))
self.assertEqual(low_ri.dest_offsets, torch.Size([0]))
self.assertEqual(low_ri.lengths, torch.Size([16]))
self.assertEqual(high_ri.storage_index, MetadataIndex("st", [48]))
self.assertEqual(high_ri.storage_offsets, torch.Size([0]))
self.assertEqual(high_ri.dest_index, MetadataIndex("st", [32]))
self.assertEqual(high_ri.dest_offsets, torch.Size([16]))
self.assertEqual(high_ri.lengths, torch.Size([16]))
# Second scenario, going from world=4 to world=8, need to load half of 1 shard
# rank1 on 8-world needs to load the upper half of the rank0 4-world shard
load_plan = create_default_local_load_plan(world8_state_dict, world4_metadata)
self.assertEqual(1, len(load_plan.items))
ri = load_plan.items[0]
self.assertEqual(ri.storage_index, MetadataIndex("st", [0]))
self.assertEqual(ri.storage_offsets, torch.Size([16]))
self.assertEqual(ri.dest_index, MetadataIndex("st", [16]))
self.assertEqual(ri.dest_offsets, torch.Size([0]))
self.assertEqual(ri.lengths, torch.Size([16]))
def test_load_with_world_size_diff_by_one(self):
def create_state_dict(rank, world_size):
with with_dist(rank=rank, world_size=world_size):
return {
"st": create_sharded_tensor(
rank=rank,
world_size=world_size,
shards_per_rank=1,
shard_size=120 // world_size,
)
}
# rank 1 has a 30 bytes shard from [30, 60[
world4_state_dict = create_state_dict(rank=1, world_size=4)
world4_metadata = _create_default_local_metadata(world4_state_dict)
# rank 1 has a 40 bytes shard from [40, 80[
world3_state_dict = create_state_dict(rank=1, world_size=3)
load_plan = create_default_local_load_plan(world3_state_dict, world4_metadata)
self.assertEqual(2, len(load_plan.items))
# this is [30, 60] to load [40, 60]
low_ri = next(
ri for ri in load_plan.items if ri.dest_offsets == torch.Size([0])
)
# this is [60, 90] to load [60, 80]
high_ri = next(
ri for ri in load_plan.items if ri.dest_offsets == torch.Size([20])
)
self.assertEqual(low_ri.storage_index, MetadataIndex("st", [30]))
self.assertEqual(low_ri.storage_offsets, torch.Size([10]))
self.assertEqual(low_ri.dest_index, MetadataIndex("st", [40]))
self.assertEqual(low_ri.dest_offsets, torch.Size([0]))
self.assertEqual(low_ri.lengths, torch.Size([20]))
self.assertEqual(high_ri.storage_index, MetadataIndex("st", [60]))
self.assertEqual(high_ri.storage_offsets, torch.Size([0]))
self.assertEqual(high_ri.dest_index, MetadataIndex("st", [40]))
self.assertEqual(high_ri.dest_offsets, torch.Size([20]))
self.assertEqual(high_ri.lengths, torch.Size([20]))
class TestPlannerHelpers(TestCase):
def test_create_read_item_from_chunks(self):
tensor_md = TensorStorageMetadata(
properties=TensorProperties.create_from_tensor(torch.empty([16])),
size=torch.Size([16]),
chunks=[
ChunkStorageMetadata(offsets=torch.Size([0]), sizes=torch.Size([8])),
ChunkStorageMetadata(offsets=torch.Size([8]), sizes=torch.Size([8])),
],
)
chunk = ChunkStorageMetadata(offsets=torch.Size([4]), sizes=torch.Size([7]))
read_items = create_read_items_for_chunk_list("foo", tensor_md, [chunk])
self.assertEqual(2, len(read_items))
self.assertEqual(MetadataIndex("foo", [4]), read_items[0].dest_index)
self.assertEqual(torch.Size([0]), read_items[0].dest_offsets)
self.assertEqual(MetadataIndex("foo", [0]), read_items[0].storage_index)
self.assertEqual(torch.Size([4]), read_items[0].storage_offsets)
self.assertEqual(torch.Size([4]), read_items[0].lengths)
self.assertEqual(MetadataIndex("foo", [4]), read_items[1].dest_index)
self.assertEqual(torch.Size([4]), read_items[1].dest_offsets)
self.assertEqual(MetadataIndex("foo", [8]), read_items[1].storage_index)
self.assertEqual(torch.Size([0]), read_items[1].storage_offsets)
self.assertEqual(torch.Size([3]), read_items[1].lengths)
class TestLoadPlanner(TestCase):
@with_temp_dir
def test_strict(self):
original_module = nn.Linear(2, 2)
dcp.save(state_dict={"module": original_module}, checkpoint_id=self.temp_dir)
new_module = nn.Linear(2, 2)
new_module.extra_param = nn.Parameter(torch.randn(2, 2))
dcp.load(
state_dict={"module": new_module},
checkpoint_id=self.temp_dir,
planner=DefaultLoadPlanner(allow_partial_load=True),
)
with self.assertRaisesRegex(CheckpointException, "Missing key in checkpoint"):
dcp.load(
state_dict={"module": new_module},
checkpoint_id=self.temp_dir,
planner=DefaultLoadPlanner(allow_partial_load=False),
)
@with_temp_dir
def test_load_different_sizes_throws(self):
original_module = nn.Linear(2, 2)
dcp.save(state_dict={"module": original_module}, checkpoint_id=self.temp_dir)
new_module = nn.Linear(3, 2)
with self.assertRaisesRegex(CheckpointException, "Size mismatch"):
dcp.load(
state_dict={"module": new_module},
checkpoint_id=self.temp_dir,
planner=DefaultLoadPlanner(),
)
if __name__ == "__main__":
run_tests()
|
