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Pepijn
1050c2fb6c
Replace the shard/Backtrackable/decoded-shuffle-buffer internals with an
episode pool: each (rank x worker) consumer keeps episode_pool_size whole
episodes' tabular rows in RAM and emits uniformly random frames across
them. delta_timestamps windows become exact in-RAM slices with correct
boundary padding (the Backtrackable machinery and its lookback/lookahead
ceilings are gone), and video is decoded only when a sample is emitted,
so pool memory stays tabular-sized instead of buffer_size decoded
samples.
- Prefetch-on-admit: when streaming from a remote source, each pooled
episode's video files download to a local cache in the background
(refcounted, since v3 packs several episodes per file; deleted on
eviction), so decode-on-exit reads local bytes instead of paying
network seek latency.
- Per-consumer RNG derived from (seed, epoch, rank, worker): consumers
decorrelated, runs reproducible, epochs reshuffle automatically.
- Deterministic fast-forward resume: load_state_dict takes the trainer's
{batches_consumed, batch_size}; each worker re-derives its own skip
from the DataLoader's round-robin batch assignment and replays
tabular-only (no decode). Exact within an epoch, works with
num_workers > 0, and the same state file serves every rank. Replaces
the per-shard HF state_dict approach, which lived in worker processes
and could not be captured from the trainer.
- Shard-cap default removed (max_num_shards=None uses every parquet
shard); runtime warnings for non-divisible world sizes (datasets
degrades to read-everything splitting) and workers left without
shards.
- episode_pool_size replaces buffer_size (deprecated, ignored with a
warning); decoder cache sized to the pool working set, capped at 128.
Legacy order-replication tests asserted the old buffer algorithm
step-by-step and are rewritten as behavior contracts (exactly-once
coverage, per-seed determinism, epoch reshuffle). Value-level parity
tests against the map-style dataset pass unchanged.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
50 lines
1.7 KiB
Bash
50 lines
1.7 KiB
Bash
#!/bin/bash
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#SBATCH --job-name=stream_robocasa
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#SBATCH --nodes=2
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#SBATCH --ntasks-per-node=1
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#SBATCH --gpus-per-node=8
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#SBATCH --cpus-per-task=96
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#SBATCH --exclusive
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#SBATCH --time=24:00:00
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#SBATCH --output=logs/%x-%j.out
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# Multinode streaming training over a large HF-hosted RoboCasa dataset (never touches local disk).
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# Launches examples/scaling/train_streaming_multinode.py with Accelerate. Each rank streams a disjoint
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# set of shards via split_dataset_by_node (auto-resolved from the Accelerate state), so per-node
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# throughput scales independently. For an even split, ensure n_shards % (nodes * gpus_per_node) == 0.
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#
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# Submit with: sbatch slurm/train_streaming_robocasa.sh
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set -euo pipefail
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REPO_ID=${REPO_ID:-pepijn223/robocasa_pretrain_human300_v4}
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GPUS_PER_NODE=8
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NUM_PROCESSES=$((SLURM_NNODES * GPUS_PER_NODE))
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# Rendezvous: use the first node in the allocation as the main process.
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MAIN_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1)
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MAIN_PORT=${MAIN_PORT:-29500}
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export HF_HOME=${HF_HOME:-$SCRATCH/hf_home}
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# Avoid each rank fighting over the tokenizers' internal thread pool.
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export TOKENIZERS_PARALLELISM=false
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srun --kill-on-bad-exit=1 bash -c '
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accelerate launch \
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--num_machines '"$SLURM_NNODES"' \
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--num_processes '"$NUM_PROCESSES"' \
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--machine_rank $SLURM_NODEID \
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--main_process_ip '"$MAIN_ADDR"' \
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--main_process_port '"$MAIN_PORT"' \
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--mixed_precision bf16 \
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--dynamo_backend no \
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examples/scaling/train_streaming_multinode.py \
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--repo_id '"$REPO_ID"' \
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--batch_size 64 \
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--num_workers 12 \
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--episode_pool_size 64 \
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--steps 200000 \
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--save_freq 2000 \
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--log_freq 50
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'
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