def share_model_params(self, model: nn.Module) -> None:
shared_parameters: Set[int] = set()
for k, v in ModuleStateDict(model.state_dict()).items():
if v._cdata not in shared_parameters:
shared_parameters.add(v._cdata)
log("Adding %s (%d params) to parameter server" %
(k, v.numel()))
self.set_param(k, v.data)
def load_model_state_dict(hf: h5py.File, ) -> Optional[ModuleStateDict]:
if MODEL_STATE_DICT_GROUP not in hf:
return None
g = hf[MODEL_STATE_DICT_GROUP]
state_dict = ModuleStateDict({})
def process_dataset(public_name, dataset) -> None:
if not isinstance(dataset, h5py.Dataset):
return
state_dict[public_name] = torch.from_numpy(dataset[...])
g.visititems(process_dataset)
return state_dict
def model_state_dict_private_to_public(
private_state_dict: ModuleStateDict, ) -> Dict[str, ModelParameter]:
public_state_dict: Dict[str, ModelParameter] = {}
for private_name, tensor in private_state_dict.items():
if not isinstance(tensor, torch.Tensor):
raise RuntimeError("Isn't the state dict supposed to be "
"a shallow key-to-tensor mapping?!")
for mapping in MODEL_STATE_DICT_MAPPINGS:
try:
public_name = mapping.private_to_public.map(private_name)
except ValueError:
continue
else:
break
else:
raise RuntimeError(
f"Couldn't find a match for state dict key: {private_name}")
public_state_dict[public_name] = ModelParameter(private_name, tensor)
return public_state_dict
def save_model_state_dict(hf: h5py.File, state_dict: ModuleStateDict) -> None:
g = hf.create_group(MODEL_STATE_DICT_GROUP, track_order=True)
for private_key, tensor in state_dict.items():
if not isinstance(tensor, torch.Tensor):
raise RuntimeError("Isn't the state dict supposed to be "
"a shallow key-to-tensor mapping?!")
for mapping in MODEL_STATE_DICT_MAPPINGS:
try:
public_key = mapping.private_to_public.map(private_key)
except ValueError:
continue
else:
break
else:
raise RuntimeError("Couldn't find a match for state dict key: %s" %
private_key)
dataset = g.create_dataset(public_key, data=tensor.numpy())
dataset.attrs[STATE_DICT_KEY_ATTR] = private_key
def load_model_state_dict(hf: h5py.File) -> Optional[ModuleStateDict]:
if MODEL_STATE_DICT_GROUP not in hf:
return None
g = hf[MODEL_STATE_DICT_GROUP]
state_dict = ModuleStateDict({})
def process_dataset(public_key, dataset) -> None:
if not isinstance(dataset, h5py.Dataset):
return
for mapping in MODEL_STATE_DICT_MAPPINGS:
try:
private_key = mapping.public_to_private.map(public_key)
except ValueError:
continue
else:
break
else:
raise RuntimeError("Couldn't find a match for dataset name: %s" %
public_key)
state_dict[private_key] = torch.from_numpy(dataset[...])
g.visititems(process_dataset)
return state_dict
def train_and_report_stats(
config: ConfigSchema,
model: Optional[MultiRelationEmbedder] = None,
trainer: Optional[AbstractBatchProcessor] = None,
evaluator: Optional[AbstractBatchProcessor] = None,
rank: Rank = RANK_ZERO,
subprocess_init: Optional[Callable[[], None]] = None,
) -> Generator[Tuple[int, Optional[Stats], Stats, Optional[Stats]], None,
None]:
"""Each epoch/pass, for each partition pair, loads in embeddings and edgelist
from disk, runs HOGWILD training on them, and writes partitions back to disk.
"""
tag_logs_with_process_name(f"Trainer-{rank}")
if config.verbose > 0:
import pprint
pprint.PrettyPrinter().pprint(config.to_dict())
logger.info("Loading entity counts...")
entity_storage = ENTITY_STORAGES.make_instance(config.entity_path)
entity_counts: Dict[str, List[int]] = {}
for entity, econf in config.entities.items():
entity_counts[entity] = []
for part in range(econf.num_partitions):
entity_counts[entity].append(
entity_storage.load_count(entity, part))
# Figure out how many lhs and rhs partitions we need
nparts_lhs, lhs_partitioned_types = get_partitioned_types(config, Side.LHS)
nparts_rhs, rhs_partitioned_types = get_partitioned_types(config, Side.RHS)
logger.debug(f"nparts {nparts_lhs} {nparts_rhs} "
f"types {lhs_partitioned_types} {rhs_partitioned_types}")
total_buckets = nparts_lhs * nparts_rhs
sync: AbstractSynchronizer
bucket_scheduler: AbstractBucketScheduler
parameter_sharer: Optional[ParameterSharer]
partition_client: Optional[PartitionClient]
if config.num_machines > 1:
if not 0 <= rank < config.num_machines:
raise RuntimeError("Invalid rank for trainer")
if not td.is_available():
raise RuntimeError("The installed PyTorch version doesn't provide "
"distributed training capabilities.")
ranks = ProcessRanks.from_num_invocations(config.num_machines,
config.num_partition_servers)
if rank == RANK_ZERO:
logger.info("Setup lock server...")
start_server(
LockServer(
num_clients=len(ranks.trainers),
nparts_lhs=nparts_lhs,
nparts_rhs=nparts_rhs,
lock_lhs=len(lhs_partitioned_types) > 0,
lock_rhs=len(rhs_partitioned_types) > 0,
init_tree=config.distributed_tree_init_order,
),
process_name="LockServer",
init_method=config.distributed_init_method,
world_size=ranks.world_size,
server_rank=ranks.lock_server,
groups=[ranks.trainers],
subprocess_init=subprocess_init,
)
bucket_scheduler = DistributedBucketScheduler(
server_rank=ranks.lock_server,
client_rank=ranks.trainers[rank],
)
logger.info("Setup param server...")
start_server(
ParameterServer(num_clients=len(ranks.trainers)),
process_name=f"ParamS-{rank}",
init_method=config.distributed_init_method,
world_size=ranks.world_size,
server_rank=ranks.parameter_servers[rank],
groups=[ranks.trainers],
subprocess_init=subprocess_init,
)
parameter_sharer = ParameterSharer(
process_name=f"ParamC-{rank}",
client_rank=ranks.parameter_clients[rank],
all_server_ranks=ranks.parameter_servers,
init_method=config.distributed_init_method,
world_size=ranks.world_size,
groups=[ranks.trainers],
subprocess_init=subprocess_init,
)
if config.num_partition_servers == -1:
start_server(
ParameterServer(num_clients=len(ranks.trainers),
log_stats=True),
process_name=f"PartS-{rank}",
init_method=config.distributed_init_method,
world_size=ranks.world_size,
server_rank=ranks.partition_servers[rank],
groups=[ranks.trainers],
subprocess_init=subprocess_init,
)
if len(ranks.partition_servers) > 0:
partition_client = PartitionClient(ranks.partition_servers,
log_stats=True)
else:
partition_client = None
groups = init_process_group(
rank=ranks.trainers[rank],
world_size=ranks.world_size,
init_method=config.distributed_init_method,
groups=[ranks.trainers],
)
trainer_group, = groups
sync = DistributedSynchronizer(trainer_group)
else:
sync = DummySynchronizer()
bucket_scheduler = SingleMachineBucketScheduler(
nparts_lhs, nparts_rhs, config.bucket_order)
parameter_sharer = None
partition_client = None
hide_distributed_logging()
# fork early for HOGWILD threads
logger.info("Creating workers...")
num_workers = get_num_workers(config.workers)
pool = create_pool(
num_workers,
subprocess_name=f"TWorker-{rank}",
subprocess_init=subprocess_init,
)
def make_optimizer(params: Iterable[torch.nn.Parameter],
is_emb: bool) -> Optimizer:
params = list(params)
if len(params) == 0:
optimizer = DummyOptimizer()
elif is_emb:
optimizer = RowAdagrad(params, lr=config.lr)
else:
if config.relation_lr is not None:
lr = config.relation_lr
else:
lr = config.lr
optimizer = Adagrad(params, lr=lr)
optimizer.share_memory()
return optimizer
# background_io is only supported in single-machine mode
background_io = config.background_io and config.num_machines == 1
checkpoint_manager = CheckpointManager(
config.checkpoint_path,
background=background_io,
rank=rank,
num_machines=config.num_machines,
partition_client=partition_client,
subprocess_name=f"BackgRW-{rank}",
subprocess_init=subprocess_init,
)
checkpoint_manager.register_metadata_provider(
ConfigMetadataProvider(config))
checkpoint_manager.write_config(config)
if config.num_edge_chunks is not None:
num_edge_chunks = config.num_edge_chunks
else:
num_edge_chunks = get_num_edge_chunks(config.edge_paths, nparts_lhs,
nparts_rhs,
config.max_edges_per_chunk)
iteration_manager = IterationManager(
config.num_epochs,
config.edge_paths,
num_edge_chunks,
iteration_idx=checkpoint_manager.checkpoint_version)
checkpoint_manager.register_metadata_provider(iteration_manager)
if config.init_path is not None:
loadpath_manager = CheckpointManager(config.init_path)
else:
loadpath_manager = None
def load_embeddings(
entity: EntityName,
part: Partition,
strict: bool = False,
force_dirty: bool = False,
) -> Tuple[torch.nn.Parameter, Optional[OptimizerStateDict]]:
if strict:
embs, optim_state = checkpoint_manager.read(
entity, part, force_dirty=force_dirty)
else:
# Strict is only false during the first iteration, because in that
# case the checkpoint may not contain any data (unless a previous
# run was resumed) so we fall back on initial values.
embs, optim_state = checkpoint_manager.maybe_read(
entity, part, force_dirty=force_dirty)
if embs is None and loadpath_manager is not None:
embs, optim_state = loadpath_manager.maybe_read(entity, part)
if embs is None:
embs, optim_state = init_embs(entity,
entity_counts[entity][part],
config.dimension,
config.init_scale)
assert embs.is_shared()
return torch.nn.Parameter(embs), optim_state
logger.info("Initializing global model...")
if model is None:
model = make_model(config)
model.share_memory()
if trainer is None:
trainer = Trainer(
global_optimizer=make_optimizer(model.parameters(), False),
loss_fn=config.loss_fn,
margin=config.margin,
relations=config.relations,
)
if evaluator is None:
evaluator = TrainingRankingEvaluator(
override_num_batch_negs=config.eval_num_batch_negs,
override_num_uniform_negs=config.eval_num_uniform_negs,
)
eval_batch_size = round_up_to_nearest_multiple(config.batch_size,
config.eval_num_batch_negs)
state_dict, optim_state = checkpoint_manager.maybe_read_model()
if state_dict is None and loadpath_manager is not None:
state_dict, optim_state = loadpath_manager.maybe_read_model()
if state_dict is not None:
model.load_state_dict(state_dict, strict=False)
if optim_state is not None:
trainer.global_optimizer.load_state_dict(optim_state)
logger.debug("Loading unpartitioned entities...")
for entity, econfig in config.entities.items():
if econfig.num_partitions == 1:
embs, optim_state = load_embeddings(entity, Partition(0))
model.set_embeddings(entity, embs, Side.LHS)
model.set_embeddings(entity, embs, Side.RHS)
optimizer = make_optimizer([embs], True)
if optim_state is not None:
optimizer.load_state_dict(optim_state)
trainer.entity_optimizers[(entity, Partition(0))] = optimizer
# start communicating shared parameters with the parameter server
if parameter_sharer is not None:
parameter_sharer.share_model_params(model)
strict = False
def swap_partitioned_embeddings(
old_b: Optional[Bucket],
new_b: Optional[Bucket],
):
# 0. given the old and new buckets, construct data structures to keep
# track of old and new embedding (entity, part) tuples
io_bytes = 0
logger.info(f"Swapping partitioned embeddings {old_b} {new_b}")
types = ([(e, Side.LHS) for e in lhs_partitioned_types] +
[(e, Side.RHS) for e in rhs_partitioned_types])
old_parts = {(e, old_b.get_partition(side)): side
for e, side in types if old_b is not None}
new_parts = {(e, new_b.get_partition(side)): side
for e, side in types if new_b is not None}
to_checkpoint = set(old_parts) - set(new_parts)
preserved = set(old_parts) & set(new_parts)
# 1. checkpoint embeddings that will not be used in the next pair
#
if old_b is not None: # there are previous embeddings to checkpoint
logger.info("Writing partitioned embeddings")
for entity, part in to_checkpoint:
side = old_parts[(entity, part)]
side_name = side.pick("lhs", "rhs")
logger.debug(f"Checkpointing ({entity} {part} {side_name})")
embs = model.get_embeddings(entity, side)
optim_key = (entity, part)
optim_state = OptimizerStateDict(
trainer.entity_optimizers[optim_key].state_dict())
io_bytes += embs.numel() * embs.element_size(
) # ignore optim state
checkpoint_manager.write(entity, part, embs.detach(),
optim_state)
if optim_key in trainer.entity_optimizers:
del trainer.entity_optimizers[optim_key]
# these variables are holding large objects; let them be freed
del embs
del optim_state
bucket_scheduler.release_bucket(old_b)
# 2. copy old embeddings that will be used in the next pair
# into a temporary dictionary
#
tmp_emb = {
x: model.get_embeddings(x[0], old_parts[x])
for x in preserved
}
for entity, _ in types:
model.clear_embeddings(entity, Side.LHS)
model.clear_embeddings(entity, Side.RHS)
if new_b is None: # there are no new embeddings to load
return io_bytes
bucket_logger = BucketLogger(logger, bucket=new_b)
# 3. load new embeddings into the model/optimizer, either from disk
# or the temporary dictionary
#
bucket_logger.info("Loading entities")
for entity, side in types:
part = new_b.get_partition(side)
part_key = (entity, part)
if part_key in tmp_emb:
bucket_logger.debug(
f"Loading ({entity}, {part}) from preserved")
embs, optim_state = tmp_emb[part_key], None
else:
bucket_logger.debug(f"Loading ({entity}, {part})")
force_dirty = bucket_scheduler.check_and_set_dirty(
entity, part)
embs, optim_state = load_embeddings(entity,
part,
strict=strict,
force_dirty=force_dirty)
io_bytes += embs.numel() * embs.element_size(
) # ignore optim state
model.set_embeddings(entity, embs, side)
tmp_emb[part_key] = embs
optim_key = (entity, part)
if optim_key not in trainer.entity_optimizers:
bucket_logger.debug(f"Resetting optimizer {optim_key}")
optimizer = make_optimizer([embs], True)
if optim_state is not None:
bucket_logger.debug("Setting optim state")
optimizer.load_state_dict(optim_state)
trainer.entity_optimizers[optim_key] = optimizer
return io_bytes
if rank == RANK_ZERO:
for stats_dict in checkpoint_manager.maybe_read_stats():
index: int = stats_dict["index"]
stats: Stats = Stats.from_dict(stats_dict["stats"])
eval_stats_before: Optional[Stats] = None
if "eval_stats_before" in stats_dict:
eval_stats_before = Stats.from_dict(
stats_dict["eval_stats_before"])
eval_stats_after: Optional[Stats] = None
if "eval_stats_after" in stats_dict:
eval_stats_after = Stats.from_dict(
stats_dict["eval_stats_after"])
yield (index, eval_stats_before, stats, eval_stats_after)
# Start of the main training loop.
for epoch_idx, edge_path_idx, edge_chunk_idx in iteration_manager:
logger.info(
f"Starting epoch {epoch_idx + 1} / {iteration_manager.num_epochs}, "
f"edge path {edge_path_idx + 1} / {iteration_manager.num_edge_paths}, "
f"edge chunk {edge_chunk_idx + 1} / {iteration_manager.num_edge_chunks}"
)
edge_storage = EDGE_STORAGES.make_instance(iteration_manager.edge_path)
logger.info(f"Edge path: {iteration_manager.edge_path}")
sync.barrier()
dist_logger.info("Lock client new epoch...")
bucket_scheduler.new_pass(
is_first=iteration_manager.iteration_idx == 0)
sync.barrier()
remaining = total_buckets
cur_b = None
while remaining > 0:
old_b = cur_b
io_time = 0.
io_bytes = 0
cur_b, remaining = bucket_scheduler.acquire_bucket()
logger.info(f"still in queue: {remaining}")
if cur_b is None:
if old_b is not None:
# if you couldn't get a new pair, release the lock
# to prevent a deadlock!
tic = time.time()
io_bytes += swap_partitioned_embeddings(old_b, None)
io_time += time.time() - tic
time.sleep(1) # don't hammer td
continue
bucket_logger = BucketLogger(logger, bucket=cur_b)
tic = time.time()
io_bytes += swap_partitioned_embeddings(old_b, cur_b)
current_index = \
(iteration_manager.iteration_idx + 1) * total_buckets - remaining
next_b = bucket_scheduler.peek()
if next_b is not None and background_io:
# Ensure the previous bucket finished writing to disk.
checkpoint_manager.wait_for_marker(current_index - 1)
bucket_logger.debug("Prefetching")
for entity in lhs_partitioned_types:
checkpoint_manager.prefetch(entity, next_b.lhs)
for entity in rhs_partitioned_types:
checkpoint_manager.prefetch(entity, next_b.rhs)
checkpoint_manager.record_marker(current_index)
bucket_logger.debug("Loading edges")
edges = edge_storage.load_chunk_of_edges(
cur_b.lhs, cur_b.rhs, edge_chunk_idx,
iteration_manager.num_edge_chunks)
num_edges = len(edges)
# this might be off in the case of tensorlist or extra edge fields
io_bytes += edges.lhs.tensor.numel(
) * edges.lhs.tensor.element_size()
io_bytes += edges.rhs.tensor.numel(
) * edges.rhs.tensor.element_size()
io_bytes += edges.rel.numel() * edges.rel.element_size()
bucket_logger.debug("Shuffling edges")
# Fix a seed to get the same permutation every time; have it
# depend on all and only what affects the set of edges.
g = torch.Generator()
g.manual_seed(
hash((edge_path_idx, edge_chunk_idx, cur_b.lhs, cur_b.rhs)))
num_eval_edges = int(num_edges * config.eval_fraction)
if num_eval_edges > 0:
edge_perm = torch.randperm(num_edges, generator=g)
eval_edge_perm = edge_perm[-num_eval_edges:]
num_edges -= num_eval_edges
edge_perm = edge_perm[torch.randperm(num_edges)]
else:
edge_perm = torch.randperm(num_edges)
# HOGWILD evaluation before training
eval_stats_before: Optional[Stats] = None
if num_eval_edges > 0:
bucket_logger.debug(
"Waiting for workers to perform evaluation")
future_all_eval_stats_before = pool.map_async(
call, [
partial(
process_in_batches,
batch_size=eval_batch_size,
model=model,
batch_processor=evaluator,
edges=edges,
indices=eval_edge_perm[s],
) for s in split_almost_equally(eval_edge_perm.size(0),
num_parts=num_workers)
])
all_eval_stats_before = \
get_async_result(future_all_eval_stats_before, pool)
eval_stats_before = Stats.sum(all_eval_stats_before).average()
bucket_logger.info(
f"Stats before training: {eval_stats_before}")
io_time += time.time() - tic
tic = time.time()
# HOGWILD training
bucket_logger.debug("Waiting for workers to perform training")
# FIXME should we only delay if iteration_idx == 0?
future_all_stats = pool.map_async(call, [
partial(
process_in_batches,
batch_size=config.batch_size,
model=model,
batch_processor=trainer,
edges=edges,
indices=edge_perm[s],
delay=config.hogwild_delay
if epoch_idx == 0 and rank > 0 else 0,
) for rank, s in enumerate(
split_almost_equally(edge_perm.size(0),
num_parts=num_workers))
])
all_stats = get_async_result(future_all_stats, pool)
stats = Stats.sum(all_stats).average()
compute_time = time.time() - tic
bucket_logger.info(
f"bucket {total_buckets - remaining} / {total_buckets} : "
f"Processed {num_edges} edges in {compute_time:.2f} s "
f"( {num_edges / compute_time / 1e6:.2g} M/sec ); "
f"io: {io_time:.2f} s ( {io_bytes / io_time / 1e6:.2f} MB/sec )"
)
bucket_logger.info(f"{stats}")
# HOGWILD eval after training
eval_stats_after: Optional[Stats] = None
if num_eval_edges > 0:
bucket_logger.debug(
"Waiting for workers to perform evaluation")
future_all_eval_stats_after = pool.map_async(
call, [
partial(
process_in_batches,
batch_size=eval_batch_size,
model=model,
batch_processor=evaluator,
edges=edges,
indices=eval_edge_perm[s],
) for s in split_almost_equally(eval_edge_perm.size(0),
num_parts=num_workers)
])
all_eval_stats_after = \
get_async_result(future_all_eval_stats_after, pool)
eval_stats_after = Stats.sum(all_eval_stats_after).average()
bucket_logger.info(f"Stats after training: {eval_stats_after}")
# Add train/eval metrics to queue
stats_dict = {
"index": current_index,
"stats": stats.to_dict(),
}
if eval_stats_before is not None:
stats_dict["eval_stats_before"] = eval_stats_before.to_dict()
if eval_stats_after is not None:
stats_dict["eval_stats_after"] = eval_stats_after.to_dict()
checkpoint_manager.append_stats(stats_dict)
yield current_index, eval_stats_before, stats, eval_stats_after
swap_partitioned_embeddings(cur_b, None)
# Distributed Processing: all machines can leave the barrier now.
sync.barrier()
# Preserving a checkpoint requires two steps:
# - create a snapshot (w/ symlinks) after it's first written;
# - don't delete it once the following one is written.
# These two happen in two successive iterations of the main loop: the
# one just before and the one just after the epoch boundary.
preserve_old_checkpoint = should_preserve_old_checkpoint(
iteration_manager, config.checkpoint_preservation_interval)
preserve_new_checkpoint = should_preserve_old_checkpoint(
iteration_manager + 1, config.checkpoint_preservation_interval)
# Write metadata: for multiple machines, write from rank-0
logger.info(
f"Finished epoch {epoch_idx + 1} / {iteration_manager.num_epochs}, "
f"edge path {edge_path_idx + 1} / {iteration_manager.num_edge_paths}, "
f"edge chunk {edge_chunk_idx + 1} / {iteration_manager.num_edge_chunks}"
)
if rank == 0:
for entity, econfig in config.entities.items():
if econfig.num_partitions == 1:
embs = model.get_embeddings(entity, Side.LHS)
optimizer = trainer.entity_optimizers[(entity,
Partition(0))]
checkpoint_manager.write(
entity, Partition(0), embs.detach(),
OptimizerStateDict(optimizer.state_dict()))
sanitized_state_dict: ModuleStateDict = {}
for k, v in ModuleStateDict(model.state_dict()).items():
if k.startswith('lhs_embs') or k.startswith('rhs_embs'):
# skipping state that's an entity embedding
continue
sanitized_state_dict[k] = v
logger.info("Writing the metadata")
checkpoint_manager.write_model(
sanitized_state_dict,
OptimizerStateDict(trainer.global_optimizer.state_dict()),
)
logger.info("Writing the checkpoint")
checkpoint_manager.write_new_version(config)
dist_logger.info(
"Waiting for other workers to write their parts of the checkpoint")
sync.barrier()
dist_logger.info("All parts of the checkpoint have been written")
logger.info("Switching to the new checkpoint version")
checkpoint_manager.switch_to_new_version()
dist_logger.info(
"Waiting for other workers to switch to the new checkpoint version"
)
sync.barrier()
dist_logger.info(
"All workers have switched to the new checkpoint version")
# After all the machines have finished committing
# checkpoints, we either remove the old checkpoints
# or we preserve it
if preserve_new_checkpoint:
# Add 1 so the index is a multiple of the interval, it looks nicer.
checkpoint_manager.preserve_current_version(config, epoch_idx + 1)
if not preserve_old_checkpoint:
checkpoint_manager.remove_old_version(config)
# now we're sure that all partition files exist,
# so be strict about loading them
strict = True
# quiescence
pool.close()
pool.join()
sync.barrier()
checkpoint_manager.close()
if loadpath_manager is not None:
loadpath_manager.close()
# FIXME join distributed workers (not really necessary)
logger.info("Exiting")
def train_and_report_stats(
config: ConfigSchema,
model: Optional[MultiRelationEmbedder] = None,
trainer: Optional[AbstractBatchProcessor] = None,
evaluator: Optional[AbstractBatchProcessor] = None,
rank: Rank = RANK_ZERO,
) -> Generator[Tuple[int, Optional[Stats], Stats, Optional[Stats]], None, None]:
"""Each epoch/pass, for each partition pair, loads in embeddings and edgelist
from disk, runs HOGWILD training on them, and writes partitions back to disk.
"""
if config.verbose > 0:
import pprint
pprint.PrettyPrinter().pprint(config.to_dict())
log("Loading entity counts...")
if maybe_old_entity_path(config.entity_path):
log("WARNING: It may be that your entity path contains files using the "
"old format. See D14241362 for how to update them.")
entity_counts: Dict[str, List[int]] = {}
for entity, econf in config.entities.items():
entity_counts[entity] = []
for part in range(econf.num_partitions):
with open(os.path.join(
config.entity_path, "entity_count_%s_%d.txt" % (entity, part)
), "rt") as tf:
entity_counts[entity].append(int(tf.read().strip()))
# Figure out how many lhs and rhs partitions we need
nparts_lhs, lhs_partitioned_types = get_partitioned_types(config, Side.LHS)
nparts_rhs, rhs_partitioned_types = get_partitioned_types(config, Side.RHS)
vlog("nparts %d %d types %s %s" %
(nparts_lhs, nparts_rhs, lhs_partitioned_types, rhs_partitioned_types))
total_buckets = nparts_lhs * nparts_rhs
sync: AbstractSynchronizer
bucket_scheduler: AbstractBucketScheduler
parameter_sharer: Optional[ParameterSharer]
partition_client: Optional[PartitionClient]
if config.num_machines > 1:
if not 0 <= rank < config.num_machines:
raise RuntimeError("Invalid rank for trainer")
if not td.is_available():
raise RuntimeError("The installed PyTorch version doesn't provide "
"distributed training capabilities.")
ranks = ProcessRanks.from_num_invocations(
config.num_machines, config.num_partition_servers)
if rank == RANK_ZERO:
log("Setup lock server...")
start_server(
LockServer(
num_clients=len(ranks.trainers),
nparts_lhs=nparts_lhs,
nparts_rhs=nparts_rhs,
lock_lhs=len(lhs_partitioned_types) > 0,
lock_rhs=len(rhs_partitioned_types) > 0,
init_tree=config.distributed_tree_init_order,
),
server_rank=ranks.lock_server,
world_size=ranks.world_size,
init_method=config.distributed_init_method,
groups=[ranks.trainers],
)
bucket_scheduler = DistributedBucketScheduler(
server_rank=ranks.lock_server,
client_rank=ranks.trainers[rank],
)
log("Setup param server...")
start_server(
ParameterServer(num_clients=len(ranks.trainers)),
server_rank=ranks.parameter_servers[rank],
init_method=config.distributed_init_method,
world_size=ranks.world_size,
groups=[ranks.trainers],
)
parameter_sharer = ParameterSharer(
client_rank=ranks.parameter_clients[rank],
all_server_ranks=ranks.parameter_servers,
init_method=config.distributed_init_method,
world_size=ranks.world_size,
groups=[ranks.trainers],
)
if config.num_partition_servers == -1:
start_server(
ParameterServer(num_clients=len(ranks.trainers)),
server_rank=ranks.partition_servers[rank],
world_size=ranks.world_size,
init_method=config.distributed_init_method,
groups=[ranks.trainers],
)
if len(ranks.partition_servers) > 0:
partition_client = PartitionClient(ranks.partition_servers)
else:
partition_client = None
groups = init_process_group(
rank=ranks.trainers[rank],
world_size=ranks.world_size,
init_method=config.distributed_init_method,
groups=[ranks.trainers],
)
trainer_group, = groups
sync = DistributedSynchronizer(trainer_group)
dlog = log
else:
sync = DummySynchronizer()
bucket_scheduler = SingleMachineBucketScheduler(
nparts_lhs, nparts_rhs, config.bucket_order)
parameter_sharer = None
partition_client = None
dlog = lambda msg: None
# fork early for HOGWILD threads
log("Creating workers...")
num_workers = get_num_workers(config.workers)
pool = create_pool(num_workers)
def make_optimizer(params: Iterable[torch.nn.Parameter], is_emb: bool) -> Optimizer:
params = list(params)
if len(params) == 0:
optimizer = DummyOptimizer()
elif is_emb:
optimizer = RowAdagrad(params, lr=config.lr)
else:
if config.relation_lr is not None:
lr = config.relation_lr
else:
lr = config.lr
optimizer = Adagrad(params, lr=lr)
optimizer.share_memory()
return optimizer
# background_io is only supported in single-machine mode
background_io = config.background_io and config.num_machines == 1
checkpoint_manager = CheckpointManager(
config.checkpoint_path,
background=background_io,
rank=rank,
num_machines=config.num_machines,
partition_client=partition_client,
)
checkpoint_manager.register_metadata_provider(ConfigMetadataProvider(config))
checkpoint_manager.write_config(config)
iteration_manager = IterationManager(
config.num_epochs, config.edge_paths, config.num_edge_chunks,
iteration_idx=checkpoint_manager.checkpoint_version)
checkpoint_manager.register_metadata_provider(iteration_manager)
if config.init_path is not None:
loadpath_manager = CheckpointManager(config.init_path)
else:
loadpath_manager = None
def load_embeddings(
entity: EntityName,
part: Partition,
strict: bool = False,
force_dirty: bool = False,
) -> Tuple[torch.nn.Parameter, Optional[OptimizerStateDict]]:
if strict:
embs, optim_state = checkpoint_manager.read(entity, part,
force_dirty=force_dirty)
else:
# Strict is only false during the first iteration, because in that
# case the checkpoint may not contain any data (unless a previous
# run was resumed) so we fall back on initial values.
embs, optim_state = checkpoint_manager.maybe_read(entity, part,
force_dirty=force_dirty)
if embs is None and loadpath_manager is not None:
embs, optim_state = loadpath_manager.maybe_read(entity, part)
if embs is None:
embs, optim_state = init_embs(entity, entity_counts[entity][part],
config.dimension, config.init_scale)
assert embs.is_shared()
return torch.nn.Parameter(embs), optim_state
log("Initializing global model...")
if model is None:
model = make_model(config)
model.share_memory()
if trainer is None:
trainer = Trainer(
global_optimizer=make_optimizer(model.parameters(), False),
loss_fn=config.loss_fn,
margin=config.margin,
relations=config.relations,
)
if evaluator is None:
evaluator = TrainingRankingEvaluator(
override_num_batch_negs=config.eval_num_batch_negs,
override_num_uniform_negs=config.eval_num_uniform_negs,
)
eval_batch_size = round_up_to_nearest_multiple(config.batch_size, config.eval_num_batch_negs)
state_dict, optim_state = checkpoint_manager.maybe_read_model()
if state_dict is None and loadpath_manager is not None:
state_dict, optim_state = loadpath_manager.maybe_read_model()
if state_dict is not None:
model.load_state_dict(state_dict, strict=False)
if optim_state is not None:
trainer.global_optimizer.load_state_dict(optim_state)
vlog("Loading unpartitioned entities...")
for entity, econfig in config.entities.items():
if econfig.num_partitions == 1:
embs, optim_state = load_embeddings(entity, Partition(0))
model.set_embeddings(entity, embs, Side.LHS)
model.set_embeddings(entity, embs, Side.RHS)
optimizer = make_optimizer([embs], True)
if optim_state is not None:
optimizer.load_state_dict(optim_state)
trainer.entity_optimizers[(entity, Partition(0))] = optimizer
# start communicating shared parameters with the parameter server
if parameter_sharer is not None:
parameter_sharer.share_model_params(model)
strict = False
def swap_partitioned_embeddings(
old_b: Optional[Bucket],
new_b: Optional[Bucket],
):
# 0. given the old and new buckets, construct data structures to keep
# track of old and new embedding (entity, part) tuples
io_bytes = 0
log("Swapping partitioned embeddings %s %s" % (old_b, new_b))
types = ([(e, Side.LHS) for e in lhs_partitioned_types]
+ [(e, Side.RHS) for e in rhs_partitioned_types])
old_parts = {(e, old_b.get_partition(side)): side
for e, side in types if old_b is not None}
new_parts = {(e, new_b.get_partition(side)): side
for e, side in types if new_b is not None}
to_checkpoint = set(old_parts) - set(new_parts)
preserved = set(old_parts) & set(new_parts)
# 1. checkpoint embeddings that will not be used in the next pair
#
if old_b is not None: # there are previous embeddings to checkpoint
log("Writing partitioned embeddings")
for entity, part in to_checkpoint:
side = old_parts[(entity, part)]
vlog("Checkpointing (%s %d %s)" %
(entity, part, side.pick("lhs", "rhs")))
embs = model.get_embeddings(entity, side)
optim_key = (entity, part)
optim_state = OptimizerStateDict(trainer.entity_optimizers[optim_key].state_dict())
io_bytes += embs.numel() * embs.element_size() # ignore optim state
checkpoint_manager.write(entity, part, embs.detach(), optim_state)
if optim_key in trainer.entity_optimizers:
del trainer.entity_optimizers[optim_key]
# these variables are holding large objects; let them be freed
del embs
del optim_state
bucket_scheduler.release_bucket(old_b)
# 2. copy old embeddings that will be used in the next pair
# into a temporary dictionary
#
tmp_emb = {x: model.get_embeddings(x[0], old_parts[x]) for x in preserved}
for entity, _ in types:
model.clear_embeddings(entity, Side.LHS)
model.clear_embeddings(entity, Side.RHS)
if new_b is None: # there are no new embeddings to load
return io_bytes
# 3. load new embeddings into the model/optimizer, either from disk
# or the temporary dictionary
#
log("Loading entities")
for entity, side in types:
part = new_b.get_partition(side)
part_key = (entity, part)
if part_key in tmp_emb:
vlog("Loading (%s, %d) from preserved" % (entity, part))
embs, optim_state = tmp_emb[part_key], None
else:
vlog("Loading (%s, %d)" % (entity, part))
force_dirty = bucket_scheduler.check_and_set_dirty(entity, part)
embs, optim_state = load_embeddings(
entity, part, strict=strict, force_dirty=force_dirty)
io_bytes += embs.numel() * embs.element_size() # ignore optim state
model.set_embeddings(entity, embs, side)
tmp_emb[part_key] = embs
optim_key = (entity, part)
if optim_key not in trainer.entity_optimizers:
vlog("Resetting optimizer %s" % (optim_key,))
optimizer = make_optimizer([embs], True)
if optim_state is not None:
vlog("Setting optim state")
optimizer.load_state_dict(optim_state)
trainer.entity_optimizers[optim_key] = optimizer
return io_bytes
# Start of the main training loop.
for epoch_idx, edge_path_idx, edge_chunk_idx \
in iteration_manager.remaining_iterations():
log("Starting epoch %d / %d edge path %d / %d edge chunk %d / %d" %
(epoch_idx + 1, iteration_manager.num_epochs,
edge_path_idx + 1, iteration_manager.num_edge_paths,
edge_chunk_idx + 1, iteration_manager.num_edge_chunks))
edge_reader = EdgeReader(iteration_manager.edge_path)
log("edge_path= %s" % iteration_manager.edge_path)
sync.barrier()
dlog("Lock client new epoch...")
bucket_scheduler.new_pass(is_first=iteration_manager.iteration_idx == 0)
sync.barrier()
remaining = total_buckets
cur_b = None
while remaining > 0:
old_b = cur_b
io_time = 0.
io_bytes = 0
cur_b, remaining = bucket_scheduler.acquire_bucket()
print('still in queue: %d' % remaining, file=sys.stderr)
if cur_b is None:
if old_b is not None:
# if you couldn't get a new pair, release the lock
# to prevent a deadlock!
tic = time.time()
io_bytes += swap_partitioned_embeddings(old_b, None)
io_time += time.time() - tic
time.sleep(1) # don't hammer td
continue
def log_status(msg, always=False):
f = log if always else vlog
f("%s: %s" % (cur_b, msg))
tic = time.time()
io_bytes += swap_partitioned_embeddings(old_b, cur_b)
current_index = \
(iteration_manager.iteration_idx + 1) * total_buckets - remaining
next_b = bucket_scheduler.peek()
if next_b is not None and background_io:
# Ensure the previous bucket finished writing to disk.
checkpoint_manager.wait_for_marker(current_index - 1)
log_status("Prefetching")
for entity in lhs_partitioned_types:
checkpoint_manager.prefetch(entity, next_b.lhs)
for entity in rhs_partitioned_types:
checkpoint_manager.prefetch(entity, next_b.rhs)
checkpoint_manager.record_marker(current_index)
log_status("Loading edges")
edges = edge_reader.read(
cur_b.lhs, cur_b.rhs, edge_chunk_idx, config.num_edge_chunks)
num_edges = len(edges)
# this might be off in the case of tensorlist or extra edge fields
io_bytes += edges.lhs.tensor.numel() * edges.lhs.tensor.element_size()
io_bytes += edges.rhs.tensor.numel() * edges.rhs.tensor.element_size()
io_bytes += edges.rel.numel() * edges.rel.element_size()
log_status("Shuffling edges")
# Fix a seed to get the same permutation every time; have it
# depend on all and only what affects the set of edges.
g = torch.Generator()
g.manual_seed(hash((edge_path_idx, edge_chunk_idx, cur_b.lhs, cur_b.rhs)))
num_eval_edges = int(num_edges * config.eval_fraction)
if num_eval_edges > 0:
edge_perm = torch.randperm(num_edges, generator=g)
eval_edge_perm = edge_perm[-num_eval_edges:]
num_edges -= num_eval_edges
edge_perm = edge_perm[torch.randperm(num_edges)]
else:
edge_perm = torch.randperm(num_edges)
# HOGWILD evaluation before training
eval_stats_before: Optional[Stats] = None
if num_eval_edges > 0:
log_status("Waiting for workers to perform evaluation")
all_eval_stats_before = pool.map(call, [
partial(
process_in_batches,
batch_size=eval_batch_size,
model=model,
batch_processor=evaluator,
edges=edges,
indices=eval_edge_perm[s],
)
for s in split_almost_equally(eval_edge_perm.size(0),
num_parts=num_workers)
])
eval_stats_before = Stats.sum(all_eval_stats_before).average()
log("stats before %s: %s" % (cur_b, eval_stats_before))
io_time += time.time() - tic
tic = time.time()
# HOGWILD training
log_status("Waiting for workers to perform training")
# FIXME should we only delay if iteration_idx == 0?
all_stats = pool.map(call, [
partial(
process_in_batches,
batch_size=config.batch_size,
model=model,
batch_processor=trainer,
edges=edges,
indices=edge_perm[s],
delay=config.hogwild_delay if epoch_idx == 0 and rank > 0 else 0,
)
for rank, s in enumerate(split_almost_equally(edge_perm.size(0),
num_parts=num_workers))
])
stats = Stats.sum(all_stats).average()
compute_time = time.time() - tic
log_status(
"bucket %d / %d : Processed %d edges in %.2f s "
"( %.2g M/sec ); io: %.2f s ( %.2f MB/sec )" %
(total_buckets - remaining, total_buckets,
num_edges, compute_time, num_edges / compute_time / 1e6,
io_time, io_bytes / io_time / 1e6),
always=True)
log_status("%s" % stats, always=True)
# HOGWILD eval after training
eval_stats_after: Optional[Stats] = None
if num_eval_edges > 0:
log_status("Waiting for workers to perform evaluation")
all_eval_stats_after = pool.map(call, [
partial(
process_in_batches,
batch_size=eval_batch_size,
model=model,
batch_processor=evaluator,
edges=edges,
indices=eval_edge_perm[s],
)
for s in split_almost_equally(eval_edge_perm.size(0),
num_parts=num_workers)
])
eval_stats_after = Stats.sum(all_eval_stats_after).average()
log("stats after %s: %s" % (cur_b, eval_stats_after))
# Add train/eval metrics to queue
yield current_index, eval_stats_before, stats, eval_stats_after
swap_partitioned_embeddings(cur_b, None)
# Distributed Processing: all machines can leave the barrier now.
sync.barrier()
# Write metadata: for multiple machines, write from rank-0
log("Finished epoch %d path %d pass %d; checkpointing global state."
% (epoch_idx + 1, edge_path_idx + 1, edge_chunk_idx + 1))
log("My rank: %d" % rank)
if rank == 0:
for entity, econfig in config.entities.items():
if econfig.num_partitions == 1:
embs = model.get_embeddings(entity, Side.LHS)
optimizer = trainer.entity_optimizers[(entity, Partition(0))]
checkpoint_manager.write(
entity, Partition(0),
embs.detach(), OptimizerStateDict(optimizer.state_dict()))
sanitized_state_dict: ModuleStateDict = {}
for k, v in ModuleStateDict(model.state_dict()).items():
if k.startswith('lhs_embs') or k.startswith('rhs_embs'):
# skipping state that's an entity embedding
continue
sanitized_state_dict[k] = v
log("Writing metadata...")
checkpoint_manager.write_model(
sanitized_state_dict,
OptimizerStateDict(trainer.global_optimizer.state_dict()),
)
log("Writing the checkpoint...")
checkpoint_manager.write_new_version(config)
dlog("Waiting for other workers to write their parts of the checkpoint: rank %d" % rank)
sync.barrier()
dlog("All parts of the checkpoint have been written")
log("Switching to new checkpoint version...")
checkpoint_manager.switch_to_new_version()
dlog("Waiting for other workers to switch to the new checkpoint version: rank %d" % rank)
sync.barrier()
dlog("All workers have switched to the new checkpoint version")
# After all the machines have finished committing
# checkpoints, we remove the old checkpoints.
checkpoint_manager.remove_old_version(config)
# now we're sure that all partition files exist,
# so be strict about loading them
strict = True
# quiescence
pool.close()
pool.join()
sync.barrier()
checkpoint_manager.close()
if loadpath_manager is not None:
loadpath_manager.close()
# FIXME join distributed workers (not really necessary)
log("Exiting")
def _maybe_write_checkpoint(
self,
epoch_idx: int,
edge_path_idx: int,
edge_chunk_idx: int,
) -> None:
config = self.config
# Preserving a checkpoint requires two steps:
# - create a snapshot (w/ symlinks) after it's first written;
# - don't delete it once the following one is written.
# These two happen in two successive iterations of the main loop: the
# one just before and the one just after the epoch boundary.
preserve_old_checkpoint = should_preserve_old_checkpoint(
self.iteration_manager, config.checkpoint_preservation_interval)
preserve_new_checkpoint = should_preserve_old_checkpoint(
self.iteration_manager + 1,
config.checkpoint_preservation_interval)
# Write metadata: for multiple machines, write from rank-0
logger.info(
f"Finished epoch {epoch_idx + 1} / {self.iteration_manager.num_epochs}, "
f"edge path {edge_path_idx + 1} / {self.iteration_manager.num_edge_paths}, "
f"edge chunk {edge_chunk_idx + 1} / {self.iteration_manager.num_edge_chunks}"
)
if self.rank == 0:
for entity, econfig in config.entities.items():
if econfig.num_partitions == 1:
embs = self.holder.unpartitioned_embeddings[entity]
optimizer = self.trainer.unpartitioned_optimizers[entity]
self.checkpoint_manager.write(
entity, Partition(0), embs.detach(),
OptimizerStateDict(optimizer.state_dict()))
logger.info("Writing the metadata")
state_dict: ModuleStateDict = ModuleStateDict(
self.model.state_dict())
self.checkpoint_manager.write_model(
state_dict,
OptimizerStateDict(self.trainer.model_optimizer.state_dict()))
logger.info("Writing the training stats")
all_stats_dicts: List[Dict[...]] = []
for stats in self.bucket_scheduler.get_stats_for_pass():
stats_dict = {
"epoch_idx": epoch_idx,
"edge_path_idx": edge_path_idx,
"edge_chunk_idx": edge_chunk_idx,
"lhs_partition": stats.lhs_partition,
"rhs_partition": stats.rhs_partition,
"index": stats.index,
"stats": stats.train.to_dict(),
}
if stats.eval_before is not None:
stats_dict[
"eval_stats_before"] = stats.eval_before.to_dict()
if stats.eval_after is not None:
stats_dict["eval_stats_after"] = stats.eval_after.to_dict()
all_stats_dicts.append(stats_dict)
self.checkpoint_manager.append_stats(all_stats_dicts)
logger.info("Writing the checkpoint")
self.checkpoint_manager.write_new_version(
config, self.entity_counts, self.embedding_storage_freelist)
dist_logger.info(
"Waiting for other workers to write their parts of the checkpoint")
self._barrier()
dist_logger.info("All parts of the checkpoint have been written")
logger.info("Switching to the new checkpoint version")
self.checkpoint_manager.switch_to_new_version()
dist_logger.info(
"Waiting for other workers to switch to the new checkpoint version"
)
self._barrier()
dist_logger.info(
"All workers have switched to the new checkpoint version")
# After all the machines have finished committing
# checkpoints, we either remove the old checkpoints
# or we preserve it
if preserve_new_checkpoint:
# Add 1 so the index is a multiple of the interval, it looks nicer.
self.checkpoint_manager.preserve_current_version(
config, epoch_idx + 1)
if not preserve_old_checkpoint:
self.checkpoint_manager.remove_old_version(config)