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Drop a Worker #32

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Drop a Worker #32

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07441b6
Drop a Worker
faresobeid Sep 24, 2024
04ff611
drop half workers
faresobeid Sep 26, 2024
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25 changes: 16 additions & 9 deletions open_diloco/train_fsdp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,6 +91,7 @@ class HvConfig(BaseConfig):
world_rank: int
galaxy_size: int
fail_rank_drop: bool = False # fail if we lose a diloco worker


@model_validator(mode="before")
def cast_str_to_list(cls, values: dict[str, Any]) -> dict[str, Any]:
Expand Down Expand Up @@ -179,7 +180,7 @@ def train(config: Config):
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
rank = int(os.environ["RANK"])

world_rank_list = list(range(config.hv.galaxy_size))
world_messenger_hv = config.hv is not None and local_rank == 0

# batch_size is the total batch size for all GPUs
Expand Down Expand Up @@ -357,7 +358,7 @@ def scheduler_fn(opt):
max_num_peers = 0

log_activations = {}

log_drop = True
for step, batch in enumerate(iterable=train_dataloader, start=start_step * gradient_accumulation_steps):
real_step = (step + 1) // gradient_accumulation_steps
is_accumulating = bool((step + 1) % gradient_accumulation_steps)
Expand Down Expand Up @@ -449,12 +450,12 @@ def scheduler_fn(opt):
metrics.update(log_activations)
log_activations = {}

if world_messenger_hv and num_peers < max_num_peers:
log(message=f"Lost a diloco worker, num_peers: {num_peers}, galaxy_size: {config.hv.galaxy_size}")
if config.hv.fail_rank_drop:
raise ValueError(
f"Lost a diloco worker, num_peers: {num_peers}, galaxy_size: {config.hv.galaxy_size}"
)
# if world_messenger_hv and num_peers < max_num_peers:
#log(message=f"Lost a diloco worker, num_peers: {num_peers}, galaxy_size: {config.hv.galaxy_size}")
#if config.hv.fail_rank_drop:
#raise ValueError(
# f"Lost a diloco worker, num_peers: {num_peers}, galaxy_size: {config.hv.galaxy_size}"
#)

current_time = time.time()

Expand Down Expand Up @@ -510,7 +511,13 @@ def scheduler_fn(opt):

if config.max_steps is not None and real_step >= config.max_steps:
break


if real_step >= int(config.total_steps)//2:
if log_drop:
log(f"Dropping worker world ranks {world_rank_list[config.hv.galaxy_size//2:]}")
log_drop = False
if config.hv is not None and config.hv.world_rank in world_rank_list[config.hv.galaxy_size//2:]:
break
log("Training completed.")
if rank == 0:
metric_logger.finish()
Expand Down
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