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>
- examples/scaling/train_streaming_multinode.py: Accelerate-based distributed/
resumable streaming training (no DistributedSampler; rank/world_size auto-resolved),
checkpoints the dataset stream state, and supports a --dummy pure-dataloading path
with throughput logging. SLURM launcher in slurm/train_streaming_robocasa.sh.
- benchmarks/streaming/benchmark_streaming.py: dummy-consumer dataloading benchmark
(single / sarm frame modes) emitting frames/s/node, p50/p95/p99 sample latency,
first-batch latency, and VideoDecoderCache reuse stats as JSON + CSV. SLURM launcher
+ README documenting the source/node/mode matrix and manual bucket prewarming.
- VideoDecoderCache: add hit/miss/eviction counters and a stats() method so the
benchmark can surface decoder thrash (no new cache, no eviction-policy change).
- tests/datasets/test_streaming_distributed.py: accelerate-launch smoke test asserting
per-rank disjointness; skips (does not false-pass) when <2 processes spawn.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add the large-scale streaming pieces that were missing from the frame-streaming
internals, keeping the existing Backtrackable + output-reservoir frame-shuffle:
- split_dataset_by_node(rank, world_size) before the per-shard loop so each rank
streams a disjoint set of shards (fixes duplicate data across GPUs). rank and
world_size auto-resolve from Accelerate state / RANK,WORLD_SIZE env / (0, 1).
- get_worker_info() shard splitting so DataLoader workers within a rank don't
yield duplicate frames.
- Dynamic Backtrackable window (dynamic_bounds=True) sized to the requested
delta_timestamps, removing the fixed 100-frame ceiling so long horizons (e.g. a
SARM window ~160 frames) reach real frames instead of silently padding. Fix the
peek_back off-by-one: history = lookback + 1.
- video_decoder_cache_size knob; default (active_shards + 1) x num_cameras so the
live decoder working set does not thrash the VideoDecoderCache LRU.
- state_dict()/load_state_dict() for resume (per-shard HF stream state + exhausted
set + RNG). Reservoir is re-warmed, so resumption is not bit-exact (documented).
- factory.py wires buffer_size from a new DatasetConfig.streaming_buffer_size field
instead of repurposing max_num_shards as the worker count.
Tests: tests/datasets/test_streaming_native.py covers distributed disjointness,
worker de-duplication, the SARM-length window, resume, schema parity vs map-style,
local video path resolution, and shuffle decorrelation. 21 passed (13 existing + 8).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Pepijn