Omar Sobh and Claude Sonnet 4.6
f487196367
feat(batch27): JEPA ViT bridge, WebDataset shard reading, training loop
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Gap 2 — rtx-vision ViT bridge (jepa_vision_bridge.rs, 8 tests):
- ViT::forward_features(): patch reps without classification head
- ViT::encode_patch_indices(): shape-correct placeholder for GPU dispatch
- RtxVisionJepaEncoder implementing JepaEncoder (vision-bridge feature)
- From<&ViTConfig> for JepaViTConfig config conversion
- rtx-vision added as optional dep; vision-bridge feature gate
Gap 3 — WebDataset tar-shard reading (jepa_data.rs, +12 tests, 47 total):
- parse_tar_bytes(): pure stdlib tar parser (512-byte block format)
- read_webdataset_shard(): file reader with ShardLoadStats timing
- WebDatasetRecord: key, image_bytes, label, extension
- ShuffleBuffer: fixed-capacity reservoir sampling via LCG PRNG
- JepaDataPipeline::from_filesystem(): validates paths, loads shards, builds pipeline
Gap 5 — Training loop runner (jepa_runner.rs + examples/jepa_train.rs, 15 tests):
- JepaRunConfig with TOML-style key=value parser
- run_jepa_training(): full training loop (JepaTrainerV2, cosine LR, checkpointing)
- JepaCheckpoint::save() writes JSON summary; load() stub
- examples/jepa_train.rs: --config/--size/--steps/--dry-run CLI flags
Co-Authored-By: Claude Sonnet 4.6 <[email protected] >
2026-06-27 15:23:55 +00:00
Omar Sobh and Claude Sonnet 4.6
1c822120d3
feat(batch24-26): JEPA ViT wiring, data pipeline, cluster-scale config
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Batch 24 — Real ViT encoder integration (ssl/jepa_vit.rs):
- JepaEncoder trait: encode(patch_indices) + embed_dim + num_patches
- CpuViTEncoder: sinusoidal+learned pos embed, LCG-init weights, GELU FFN,
MHSA scaled dot-product; runs min(depth,2) blocks for CPU test speed
- EmaViTEncoder: shadow weights, tau-weighted update, τ=1 frozen / τ=0 copy
- JepaTrainerV2: mask→CpuViTEncoder→predictor→EmaViT→L2→EMA; timing metrics
- JepaViTConfig: tiny/small/base/large/huge presets (embed_dim, depth, heads)
- 46 tests
Batch 24b — ViT-S/T/small-14/large-14 configs (rtx-vision/configs.rs):
- Added ViTConfig::tiny() d=192/depth=12/heads=3
- Added ViTConfig::small() d=384/depth=12/heads=6
- Added ViTConfig::small_14() d=384/patch=14
- Added ViTConfig::large_14() d=1024/depth=24/patch=14
Batch 25 — ImageNet-scale streaming data pipeline (ssl/jepa_data.rs):
- ImageRecord: HWC pixel buffer with label and key
- MultiScaleRandomCrop: LCG PRNG + bilinear resampling, scale 0.2-1.0
- RandomHorizontalFlip: stochastic row mirror
- JepaAugmentationPipeline: crop→flip→ImageNet normalize (mean/std)
- InMemoryShard: synthetic LCG data for testing
- JepaBatch: augmented images + context/target indices per sample
- JepaDataPipeline: streaming iterator, Fisher-Yates epoch shuffle,
next_batch() → None at epoch end, reset_epoch()
- DatasetStats: mask efficiency, avg context/target patch counts
- WebDatasetShard: filesystem shard descriptor stub (to_in_memory for tests)
- 35 tests
Batch 26 — Cluster-scale training configuration (ssl/jepa_cluster.rs):
- GpuSpec: RTX 5060 Ti (SM_120), RTX 4090, A100-80GB specs
- NodeSpec + ClusterTopology: homogeneous/heterogeneous cluster descriptors
- JepaParallelConfig: TP/PP/DP with for_model_and_cluster() auto-select
(TP≥4 for ViT-L 300M+, TP=8/PP=2 for ViT-H 600M+)
- GradientCompressionConfig: TopK/PowerSGD/1-bit SGD with error feedback
- DcpCheckpointConfig: async save, EMA weights, keep-last-N
- JepaClusterConfig: validate(), memory_per_gpu_gb(), throughput estimate
- ClusterTrainingPlan: steps_per_epoch, total_steps, estimated_hours, summary
- AdaptiveBatchSizer: GNS-based batch doubling/halving with [min,max] clamp
- 42 tests
Total new: 163 JEPA tests (0 failures), 3,350 lines
Co-Authored-By: Claude Sonnet 4.6 <[email protected] >
2026-06-27 14:48:31 +00:00
Omar Sobh
16161bb9df
deps: align all 56 per-crate Cargo.toml files to thiserror v2
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The workspace root was upgraded to thiserror = "2" in an earlier commit,
but 56 per-crate Cargo.toml files still independently declared "1.0".
These crates do not use workspace.dependencies inheritance for thiserror.
All updated to thiserror = "2" for complete fleet alignment.
Includes: rtx-backend, rtx-tensor, rtx-losses, rtx-backend-cuda/rocm/metal,
all training crates (rtx-auto, rtx-rl, rtx-distributed, rtx-federated, etc.),
specialized crates (rtx-science, rtx-platform, rtx-nmf, rtx-neuro-*),
production crates (rtx-streaming, rtx-serving-api), and all demo crates.
cargo check --workspace: PASSES.
2026-04-26 11:45:14 -07:00
osobh and Claude Opus 4.6
02d382d5f6
style: apply rustfmt across all crates and demos
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Consistent formatting pass: line wrapping, import sorting, trailing
whitespace removal, let-chain indentation, merged derive attributes,
and unsafe block reformatting.
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected] >
2026-04-12 07:01:58 -07:00