def __init__(
self,
global_optimizer: Optimizer,
loss_fn: str,
margin: float,
relations: List[RelationSchema],
) -> None:
super().__init__()
self.global_optimizer = global_optimizer
self.entity_optimizers: Dict[Tuple[EntityName, Partition], Optimizer] = {}
loss_fn_class = LOSS_FUNCTIONS.get_class(loss_fn)
# TODO This is awful! Can we do better?
if loss_fn == "ranking":
self.loss_fn = loss_fn_class(margin)
else:
self.loss_fn = loss_fn_class()
self.relations = relations
def __init__(self, config: ConfigSchema, filter_paths: List[str]) -> None:
loss_fn = LOSS_FUNCTIONS.get_class(
config.loss_fn)(margin=config.margin)
relation_weights = [r.weight for r in config.relations]
super().__init__(loss_fn, relation_weights)
if len(config.relations) != 1 or len(config.entities) != 1:
raise RuntimeError(
"Filtered ranking evaluation should only be used "
"with dynamic relations and one entity type.")
if not config.relations[0].all_negs:
raise RuntimeError(
"Filtered Eval can only be done with all negatives.")
(entity, ) = config.entities.values()
if entity.featurized:
raise RuntimeError(
"Entity cannot be featurized for filtered eval.")
if entity.num_partitions > 1:
raise RuntimeError(
"Entity cannot be partitioned for filtered eval.")
self.lhs_map: Dict[Tuple[int, int], List[int]] = defaultdict(list)
self.rhs_map: Dict[Tuple[int, int], List[int]] = defaultdict(list)
for path in filter_paths:
logger.info(f"Building links map from path {path}")
e_storage = EDGE_STORAGES.make_instance(path)
# Assume unpartitioned.
edges = e_storage.load_edges(UNPARTITIONED, UNPARTITIONED)
for idx in range(len(edges)):
# Assume non-featurized.
cur_lhs = int(edges.lhs.to_tensor()[idx])
# Assume dynamic relations.
cur_rel = int(edges.rel[idx])
# Assume non-featurized.
cur_rhs = int(edges.rhs.to_tensor()[idx])
self.lhs_map[cur_lhs, cur_rel].append(cur_rhs)
self.rhs_map[cur_rhs, cur_rel].append(cur_lhs)
logger.info(f"Done building links map from path {path}")
def __init__( # noqa
self,
config: ConfigSchema,
model: Optional[MultiRelationEmbedder] = None,
trainer: Optional[AbstractBatchProcessor] = None,
evaluator: Optional[AbstractBatchProcessor] = None,
rank: Rank = SINGLE_TRAINER,
subprocess_init: Optional[Callable[[], None]] = None,
stats_handler: StatsHandler = NOOP_STATS_HANDLER,
):
"""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}")
self.config = config
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
holder = self.holder = EmbeddingHolder(config)
logger.debug(
f"nparts {holder.nparts_lhs} {holder.nparts_rhs} "
f"types {holder.lhs_partitioned_types} {holder.rhs_partitioned_types}"
)
# We know ahead of time that we wil need 1-2 storages for each embedding type,
# as well as the max size of this storage (num_entities x D).
# We allocate these storages n advance in `embedding_storage_freelist`.
# When we need storage for an entity type, we pop it from this free list,
# and then add it back when we 'delete' the embedding table.
embedding_storage_freelist: Dict[
EntityName, Set[torch.FloatStorage]
] = defaultdict(set)
for entity_type, counts in entity_counts.items():
max_count = max(counts)
num_sides = (
(1 if entity_type in holder.lhs_partitioned_types else 0)
+ (1 if entity_type in holder.rhs_partitioned_types else 0)
+ (
1
if entity_type
in (holder.lhs_unpartitioned_types | holder.rhs_unpartitioned_types)
else 0
)
)
for _ in range(num_sides):
embedding_storage_freelist[entity_type].add(
allocate_shared_tensor(
(max_count, config.entity_dimension(entity_type)),
dtype=torch.float,
).storage()
)
# create the handlers, threads, etc. for distributed training
if config.num_machines > 1 or config.num_partition_servers > 0:
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
)
num_ps_groups = config.num_groups_for_partition_server
groups: List[List[int]] = [ranks.trainers] # barrier group
groups += [
ranks.trainers + ranks.partition_servers
] * num_ps_groups # ps groups
group_idxs_for_partition_servers = range(1, len(groups))
if rank == SINGLE_TRAINER:
logger.info("Setup lock server...")
start_server(
LockServer(
num_clients=len(ranks.trainers),
nparts_lhs=holder.nparts_lhs,
nparts_rhs=holder.nparts_rhs,
entities_lhs=holder.lhs_partitioned_types,
entities_rhs=holder.rhs_partitioned_types,
entity_counts=entity_counts,
init_tree=config.distributed_tree_init_order,
stats_handler=stats_handler,
),
process_name="LockServer",
init_method=config.distributed_init_method,
world_size=ranks.world_size,
server_rank=ranks.lock_server,
groups=groups,
subprocess_init=subprocess_init,
)
self.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=groups,
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=groups,
subprocess_init=subprocess_init,
)
if config.num_partition_servers == -1:
start_server(
ParameterServer(
num_clients=len(ranks.trainers),
group_idxs=group_idxs_for_partition_servers,
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=groups,
subprocess_init=subprocess_init,
)
groups = init_process_group(
rank=ranks.trainers[rank],
world_size=ranks.world_size,
init_method=config.distributed_init_method,
groups=groups,
)
trainer_group, *groups_for_partition_servers = groups
self.barrier_group = trainer_group
if len(ranks.partition_servers) > 0:
partition_client = PartitionClient(
ranks.partition_servers,
groups=groups_for_partition_servers,
log_stats=True,
)
else:
partition_client = None
else:
self.barrier_group = None
self.bucket_scheduler = SingleMachineBucketScheduler(
holder.nparts_lhs, holder.nparts_rhs, config.bucket_order, stats_handler
)
parameter_sharer = None
partition_client = None
hide_distributed_logging()
# fork early for HOGWILD threads
logger.info("Creating workers...")
self.num_workers = get_num_workers(config.workers)
self.pool = create_pool(
self.num_workers,
subprocess_name=f"TWorker-{rank}",
subprocess_init=subprocess_init,
)
checkpoint_manager = CheckpointManager(
config.checkpoint_path,
rank=rank,
num_machines=config.num_machines,
partition_client=partition_client,
subprocess_name=f"BackgRW-{rank}",
subprocess_init=subprocess_init,
)
self.checkpoint_manager = checkpoint_manager
checkpoint_manager.register_metadata_provider(ConfigMetadataProvider(config))
if rank == 0:
checkpoint_manager.write_config(config)
num_edge_chunks = get_num_edge_chunks(config)
self.iteration_manager = IterationManager(
config.num_epochs,
config.edge_paths,
num_edge_chunks,
iteration_idx=checkpoint_manager.checkpoint_version,
)
checkpoint_manager.register_metadata_provider(self.iteration_manager)
logger.info("Initializing global model...")
if model is None:
model = make_model(config)
model.share_memory()
loss_fn = LOSS_FUNCTIONS.get_class(config.loss_fn)(margin=config.margin)
relation_weights = [relation.weight for relation in config.relations]
if trainer is None:
trainer = Trainer(
model_optimizer=make_optimizer(config, model.parameters(), False),
loss_fn=loss_fn,
relation_weights=relation_weights,
)
if evaluator is None:
eval_overrides = {}
if config.eval_num_batch_negs is not None:
eval_overrides["num_batch_negs"] = config.eval_num_batch_negs
if config.eval_num_uniform_negs is not None:
eval_overrides["num_uniform_negs"] = config.eval_num_uniform_negs
evaluator = RankingEvaluator(
loss_fn=loss_fn,
relation_weights=relation_weights,
overrides=eval_overrides,
)
if config.init_path is not None:
self.loadpath_manager = CheckpointManager(config.init_path)
else:
self.loadpath_manager = None
# load model from checkpoint or loadpath, if available
state_dict, optim_state = checkpoint_manager.maybe_read_model()
if state_dict is None and self.loadpath_manager is not None:
state_dict, optim_state = self.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.model_optimizer.load_state_dict(optim_state)
logger.debug("Loading unpartitioned entities...")
for entity in holder.lhs_unpartitioned_types | holder.rhs_unpartitioned_types:
count = entity_counts[entity][0]
s = embedding_storage_freelist[entity].pop()
dimension = config.entity_dimension(entity)
embs = torch.FloatTensor(s).view(-1, dimension)[:count]
embs, optimizer = self._load_embeddings(entity, UNPARTITIONED, out=embs)
holder.unpartitioned_embeddings[entity] = embs
trainer.unpartitioned_optimizers[entity] = optimizer
# start communicating shared parameters with the parameter server
if parameter_sharer is not None:
shared_parameters: Set[int] = set()
for name, param in model.named_parameters():
if id(param) in shared_parameters:
continue
shared_parameters.add(id(param))
key = f"model.{name}"
logger.info(
f"Adding {key} ({param.numel()} params) to parameter server"
)
parameter_sharer.set_param(key, param.data)
for entity, embs in holder.unpartitioned_embeddings.items():
key = f"entity.{entity}"
logger.info(f"Adding {key} ({embs.numel()} params) to parameter server")
parameter_sharer.set_param(key, embs.data)
# store everything in self
self.model = model
self.trainer = trainer
self.evaluator = evaluator
self.rank = rank
self.entity_counts = entity_counts
self.embedding_storage_freelist = embedding_storage_freelist
self.stats_handler = stats_handler
self.strict = False
def do_eval_and_report_stats(
config: ConfigSchema,
model: Optional[MultiRelationEmbedder] = None,
evaluator: Optional[AbstractBatchProcessor] = None,
subprocess_init: Optional[Callable[[], None]] = None,
) -> Generator[Tuple[Optional[int], Optional[Bucket], Stats], None, None]:
"""Computes eval metrics (mr/mrr/r1/r10/r50) for a checkpoint with trained
embeddings.
"""
tag_logs_with_process_name(f"Evaluator")
if evaluator is None:
evaluator = RankingEvaluator(
loss_fn=LOSS_FUNCTIONS.get_class(
config.loss_fn)(margin=config.margin),
relation_weights=[
relation.weight for relation in config.relations
],
)
if config.verbose > 0:
import pprint
pprint.PrettyPrinter().pprint(config.to_dict())
checkpoint_manager = CheckpointManager(config.checkpoint_path)
def load_embeddings(entity: EntityName,
part: Partition) -> torch.nn.Parameter:
embs, _ = checkpoint_manager.read(entity, part)
assert embs.is_shared()
return torch.nn.Parameter(embs)
holder = EmbeddingHolder(config)
num_workers = get_num_workers(config.workers)
pool = create_pool(num_workers,
subprocess_name="EvalWorker",
subprocess_init=subprocess_init)
if model is None:
model = make_model(config)
model.share_memory()
state_dict, _ = checkpoint_manager.maybe_read_model()
if state_dict is not None:
model.load_state_dict(state_dict, strict=False)
model.eval()
for entity in holder.lhs_unpartitioned_types | holder.rhs_unpartitioned_types:
embs = load_embeddings(entity, UNPARTITIONED)
holder.unpartitioned_embeddings[entity] = embs
all_stats: List[Stats] = []
for edge_path_idx, edge_path in enumerate(config.edge_paths):
logger.info(
f"Starting edge path {edge_path_idx + 1} / {len(config.edge_paths)} "
f"({edge_path})")
edge_storage = EDGE_STORAGES.make_instance(edge_path)
all_edge_path_stats = []
# FIXME This order assumes higher affinity on the left-hand side, as it's
# the one changing more slowly. Make this adaptive to the actual affinity.
for bucket in create_buckets_ordered_lexicographically(
holder.nparts_lhs, holder.nparts_rhs):
tic = time.perf_counter()
# logger.info(f"{bucket}: Loading entities")
old_parts = set(holder.partitioned_embeddings.keys())
new_parts = {(e, bucket.lhs)
for e in holder.lhs_partitioned_types
} | {(e, bucket.rhs)
for e in holder.rhs_partitioned_types}
for entity, part in old_parts - new_parts:
del holder.partitioned_embeddings[entity, part]
for entity, part in new_parts - old_parts:
embs = load_embeddings(entity, part)
holder.partitioned_embeddings[entity, part] = embs
model.set_all_embeddings(holder, bucket)
# logger.info(f"{bucket}: Loading edges")
edges = edge_storage.load_edges(bucket.lhs, bucket.rhs)
num_edges = len(edges)
load_time = time.perf_counter() - tic
tic = time.perf_counter()
# logger.info(f"{bucket}: Launching and waiting for workers")
future_all_bucket_stats = pool.map_async(
call,
[
partial(
process_in_batches,
batch_size=config.batch_size,
model=model,
batch_processor=evaluator,
edges=edges[s],
) for s in split_almost_equally(num_edges,
num_parts=num_workers)
],
)
all_bucket_stats = get_async_result(future_all_bucket_stats, pool)
compute_time = time.perf_counter() - tic
logger.info(
f"{bucket}: Processed {num_edges} edges in {compute_time:.2g} s "
f"({num_edges / compute_time / 1e6:.2g}M/sec); "
f"load time: {load_time:.2g} s")
total_bucket_stats = Stats.sum(all_bucket_stats)
all_edge_path_stats.append(total_bucket_stats)
mean_bucket_stats = total_bucket_stats.average()
logger.info(
f"Stats for edge path {edge_path_idx + 1} / {len(config.edge_paths)}, "
f"bucket {bucket}: {mean_bucket_stats}")
model.clear_all_embeddings()
yield edge_path_idx, bucket, mean_bucket_stats
total_edge_path_stats = Stats.sum(all_edge_path_stats)
all_stats.append(total_edge_path_stats)
mean_edge_path_stats = total_edge_path_stats.average()
logger.info("")
logger.info(
f"Stats for edge path {edge_path_idx + 1} / {len(config.edge_paths)}: "
f"{mean_edge_path_stats}")
logger.info("")
yield edge_path_idx, None, mean_edge_path_stats
mean_stats = Stats.sum(all_stats).average()
logger.info("")
logger.info(f"Stats: {mean_stats}")
logger.info("")
yield None, None, mean_stats
pool.close()
pool.join()
