Commit Graph
5 Commits
Author SHA1 Message Date
osobhandClaude Sonnet 5 4aaa36a57a style: cargo fmt --workspace (whitespace/wrapping only, no semantic change)
Whole-workspace rustfmt pass picked up while iterating on Mamba GPU
backward work. Verified formatting-only via diff sampling; no logic
changed.

Co-Authored-By: Claude Sonnet 5 <[email protected]>
2026-08-10 07:09:36 -07:00
Omar SobhandClaude Sonnet 4.6 e1b4061c23 feat(jepa): extended GPU training, data pipeline, integration, and cargo config
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Extends jepa_train with distributed launcher, jepa_data with advanced
sampling and preprocessing, jepa_gpu with full CUDA kernel wiring,
jepa_distributed/runner/metrics/vit with additional training stages.
Adds jepa_integration module and project-local cargo config.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
2026-06-29 21:35:34 +00:00
Omar SobhandClaude Sonnet 4.6 0b06c0fa81 feat(batch28): GpuViTEncoder, distributed grad sync, eval harness
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Gap 1 — GpuViTEncoder + encoder-agnostic JepaTrainerV2 (jepa_gpu.rs, 12 tests):
- ExecutionTarget enum (Cpu | Cuda{device_id}); GpuViTEncoder wraps CpuViTEncoder
- cuda feature: Arc<CudaDevice> + try_allocate_gpu_buffer() via cudarc
- no-cuda: graceful Cpu fallback with correct shapes
- JepaTrainerV2 now holds Box<dyn JepaEncoder> + EmaTargetEncoderDyn
- new_with_encoder() constructor; JepaTrainerV2::new() backward-compatible
- JepaEncoder::l2_normalize gets `where Self: Sized` for dyn-compatibility
- 46 existing jepa_vit tests preserved (zero regressions)

Gap 4 — Distributed gradient sync (jepa_distributed.rs, 15 tests):
- JepaGradSync: single_process / simulated(world_size, rank) / nccl(...)
- sync_gradients(): noop at world_size=1; divides grads by world_size (simulated)
- effective_batch_size(), is_primary(), barrier() stubs
- JepaRunConfig: +world_size/rank/master_addr/master_port fields + TOML parser
- run_jepa_training() wired: creates JepaGradSync, syncs after each step,
  gates logging+checkpointing on is_primary(); summary carries world_size + eff_batch

Gap 6 — JEPA eval harness (jepa_eval.rs + examples/jepa_eval.rs, 15 tests):
- JepaEvalConfig: feature_dim, num_classes, linear probe + kNN params, seed, mode
- EvalMode: LinearProbe / KNN / Both
- run_eval_suite(): LCG-generated L2-normalised features → JepaEvaluator dispatch
- load_features_txt / load_labels_txt / save_eval_csv (stdlib only)
- EvalSuiteResult::summary() and to_csv_row()
- examples/jepa_eval.rs: --mode/--dim/--classes/--train/--test/--epochs/--lr/--k
  --seed/--features/--labels/--test-features/--test-labels/--output CLI flags

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
2026-06-27 15:37:30 +00:00
Omar SobhandClaude 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
redclawsystems 4d88dc0584 Initial commit 2026-03-04 00:08:42 +00:00