def train(self) -> None:
holder = self.holder
config = self.config
iteration_manager = self.iteration_manager
total_buckets = holder.nparts_lhs * holder.nparts_rhs
# yield stats from checkpoint, to reconstruct
# saved part of the learning curve
if self.rank == SINGLE_TRAINER:
for stats_dict in self.checkpoint_manager.maybe_read_stats():
index: int = stats_dict["index"]
stats: Optional[Stats] = None
if "stats" in stats_dict:
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"])
eval_stats_chunk_avg: Optional[Stats] = None
if "eval_stats_chunk_avg" in stats_dict:
eval_stats_chunk_avg = Stats.from_dict(
stats_dict["eval_stats_chunk_avg"]
)
self.stats_handler.on_stats(
index,
eval_stats_before,
stats,
eval_stats_after,
eval_stats_chunk_avg,
)
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}")
self._barrier()
dist_logger.info("Lock client new epoch...")
self.bucket_scheduler.new_pass(
is_first=iteration_manager.iteration_idx == 0
)
self._barrier()
remaining = total_buckets
cur_b: Optional[Bucket] = None
cur_stats: Optional[BucketStats] = None
while remaining > 0:
old_b: Optional[Bucket] = cur_b
old_stats: Optional[BucketStats] = cur_stats
cur_b, remaining = self.bucket_scheduler.acquire_bucket()
logger.info(f"still in queue: {remaining}")
if cur_b is None:
cur_stats = None
if old_b is not None:
# if you couldn't get a new pair, release the lock
# to prevent a deadlock!
tic = time.perf_counter()
release_bytes = self._swap_partitioned_embeddings(
old_b, None, old_stats
)
release_time = time.perf_counter() - tic
logger.info(
f"Swapping old embeddings to release lock. io: {release_time:.2f} s for {release_bytes:,} bytes "
f"( {release_bytes / release_time / 1e6:.2f} MB/sec )"
)
time.sleep(1) # don't hammer td
continue
tic = time.perf_counter()
self.cur_b = cur_b
bucket_logger = BucketLogger(logger, bucket=cur_b)
self.bucket_logger = bucket_logger
io_bytes = self._swap_partitioned_embeddings(old_b, cur_b, old_stats)
self.model.set_all_embeddings(holder, cur_b)
current_index = (
iteration_manager.iteration_idx + 1
) * total_buckets - remaining
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,
shared=True,
)
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()
io_time = time.perf_counter() - tic
tic = time.perf_counter()
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.
# Note: for the sake of efficiency, we sample eval edge idxs
# from the edge set *with replacement*, meaning that there may
# be duplicates of the same edge in the eval set. When we swap
# edges into the eval set, if there are duplicates then all
# but one will be clobbered. These collisions are unlikely
# if eval_fraction is small.
#
# Importantly, this eval sampling strategy is theoretically
# sound:
# * Training and eval sets are (exactly) disjoint
# * Eval set may have (rare) duplicates, but they are
# uniformly sampled so it's still an unbiased estimator
# of the out-of-sample statistics
num_eval_edges = int(num_edges * config.eval_fraction)
num_train_edges = num_edges - num_eval_edges
if num_eval_edges > 0:
g = torch.Generator()
g.manual_seed(
hash((edge_path_idx, edge_chunk_idx, cur_b.lhs, cur_b.rhs))
)
eval_edge_idxs = torch.randint(
num_edges, (num_eval_edges,), dtype=torch.long, generator=g
)
else:
eval_edge_idxs = None
# HOGWILD evaluation before training
eval_stats_before = self._coordinate_eval(edges, eval_edge_idxs)
if eval_stats_before is not None:
bucket_logger.info(f"Stats before training: {eval_stats_before}")
eval_time = time.perf_counter() - tic
tic = time.perf_counter()
# HOGWILD training
bucket_logger.debug("Waiting for workers to perform training")
stats = self._coordinate_train(edges, eval_edge_idxs, epoch_idx)
if stats is not None:
bucket_logger.info(f"Training stats: {stats}")
train_time = time.perf_counter() - tic
tic = time.perf_counter()
# HOGWILD evaluation after training
eval_stats_after = self._coordinate_eval(edges, eval_edge_idxs)
if eval_stats_after is not None:
bucket_logger.info(f"Stats after training: {eval_stats_after}")
eval_time += time.perf_counter() - tic
bucket_logger.info(
f"bucket {total_buckets - remaining} / {total_buckets} : "
f"Trained {num_train_edges} edges in {train_time:.2f} s "
f"( {num_train_edges / train_time / 1e6:.2g} M/sec ); "
f"Eval 2*{num_eval_edges} edges in {eval_time:.2f} s "
f"( {2 * num_eval_edges / eval_time / 1e6:.2g} M/sec ); "
f"io: {io_time:.2f} s for {io_bytes:,} bytes ( {io_bytes / io_time / 1e6:.2f} MB/sec )"
)
self.model.clear_all_embeddings()
cur_stats = BucketStats(
lhs_partition=cur_b.lhs,
rhs_partition=cur_b.rhs,
index=current_index,
train=stats,
eval_before=eval_stats_before,
eval_after=eval_stats_after,
)
# release the final bucket
self._swap_partitioned_embeddings(cur_b, None, cur_stats)
# Distributed Processing: all machines can leave the barrier now.
self._barrier()
current_index = (iteration_manager.iteration_idx + 1) * total_buckets - 1
self._maybe_write_checkpoint(
epoch_idx, edge_path_idx, edge_chunk_idx, current_index
)
# now we're sure that all partition files exist,
# so be strict about loading them
self.strict = True
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 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
def train_and_report_stats(
self
) -> Generator[Tuple[int, Optional[Stats], Stats, Optional[Stats]], None,
None]:
holder = self.holder
config = self.config
iteration_manager = self.iteration_manager
total_buckets = holder.nparts_lhs * holder.nparts_rhs
# yield stats from checkpoint, to reconstruct
# saved part of the learning curve
if self.rank == RANK_ZERO:
for stats_dict in self.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)
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}")
self._barrier()
dist_logger.info("Lock client new epoch...")
self.bucket_scheduler.new_pass(
is_first=iteration_manager.iteration_idx == 0)
self._barrier()
remaining = total_buckets
cur_b: Optional[Bucket] = None
cur_stats: Optional[BucketStats] = None
while remaining > 0:
old_b: Optional[Bucket] = cur_b
old_stats: Optional[BucketStats] = cur_stats
io_time = 0.
io_bytes = 0
cur_b, remaining = self.bucket_scheduler.acquire_bucket()
logger.info(f"still in queue: {remaining}")
if cur_b is None:
cur_stats = 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 += self._swap_partitioned_embeddings(
old_b, None, old_stats)
io_time += time.time() - tic
time.sleep(1) # don't hammer td
continue
tic = time.time()
self.cur_b = cur_b
bucket_logger = BucketLogger(logger, bucket=cur_b)
self.bucket_logger = bucket_logger
io_bytes += self._swap_partitioned_embeddings(
old_b, cur_b, old_stats)
self.model.set_all_embeddings(holder, cur_b)
current_index = \
(iteration_manager.iteration_idx + 1) * total_buckets - remaining
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,
shared=True)
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()
io_time += time.time() - tic
tic = time.time()
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:
eval_edge_perm = None
edge_perm = torch.randperm(num_edges)
# HOGWILD evaluation before training
eval_stats_before = self._coordinate_eval(
edges, eval_edge_perm)
if eval_stats_before is not None:
bucket_logger.info(
f"Stats before training: {eval_stats_before}")
# HOGWILD training
bucket_logger.debug("Waiting for workers to perform training")
stats = self._coordinate_train(edges, edge_perm, epoch_idx)
# HOGWILD evaluation after training
eval_stats_after = self._coordinate_eval(edges, eval_edge_perm)
if eval_stats_after is not None:
bucket_logger.info(
f"Stats before training: {eval_stats_after}")
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}")
self.model.clear_all_embeddings()
yield current_index, eval_stats_before, stats, eval_stats_after
cur_stats = BucketStats(
lhs_partition=cur_b.lhs,
rhs_partition=cur_b.rhs,
index=current_index,
train=stats,
eval_before=eval_stats_before,
eval_after=eval_stats_after,
)
# release the final bucket
self._swap_partitioned_embeddings(cur_b, None, cur_stats)
# Distributed Processing: all machines can leave the barrier now.
self._barrier()
self._maybe_write_checkpoint(epoch_idx, edge_path_idx,
edge_chunk_idx)
# now we're sure that all partition files exist,
# so be strict about loading them
self.strict = True
