6 Commits

Include renames

Author	SHA1	Message	Date
osobhandClaude Fable 5	ac3f2af06b	feat(jepa): eval in the training loop, NCCL GPU AllReduce, GPU checkpointing ... - Eval: run_jepa_training now runs k-NN (k=5) + linear-probe evaluation every eval_every steps and at the end (deduped when aligned); JepaEvalResult recorded in JepaTrainingSummary (final_knn_acc / final_probe_acc), printed by the CLI, appended as an # eval section to the metrics CSV. Probe set is deterministic LCG synthetic (offset seed, never aliases training batches) or real shard labels when loaded. - NCCL: real GPU-direct AllReduce backend behind the new `nccl` feature (cudarc/nccl, dlopen-based so builds don't need libnccl). NCCL unique id is bootstrapped over the existing TCP rendezvous (master_port+137); data path is htod -> ncclAllReduce(Sum) -> dtoh -> mean. catch_unwind guards cudarc's panic-on-missing-lib so training falls back instead of aborting. Verified for real on the RTX 5060 Ti: single-rank GPU all_reduce identity test passes (26/26 with --features nccl). - GPU checkpointing/eval: JepaTrainerV2::context_encoder_cpu_weights() exposes host-side weights for both CPU and GPU encoders (GpuViTEncoder::cpu_weights); checkpoint save and eval now work for GPU training runs (verified: .jepa binaries written and 2 eval passes during a live GPU CLI run). Resume with a GPU encoder restores the step counter and warns that weight re-upload is not yet implemented rather than silently training on stale weights. 125 runner/distributed/vit tests pass; CLI 8/8; cuda check clean. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-10 03:51:36 -07:00
osobhandClaude Fable 5	b6440905e7	feat(jepa): gzip-compressed WebDataset shard support ... read_webdataset_shard detects the gzip magic bytes (1F 8B, not extension) and decompresses via flate2 before tar parsing; WebDatasetShard::load no longer rejects .tar.gz/.tgz. Round-trip test writes a real gzipped tar and loads it back. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 22:37:05 -07:00
osobhandClaude Fable 5	1e3c604896	feat(meta,jepa): expose GPU features through meta-crates; wire JEPA cluster plan and real shard loading ... Meta-crates (Phase 2): - rtx-core / rtx-training / rtx-inference-stack gain cuda and metal features threading into their sub-crates; GPU was previously unreachable through the user-facing bundles. - rtx-training restores rtx-distributed (the hpc-channels blocker is gone) so the advertised DistributedTransformerTrainer resolves; drops the unused rtx-runtime dep. - rtx-transformers drops unused rtx-backend/rtx-backend-cpu deps (stale comment referenced a teacher that never used them). Never-compiled CUDA paths fixed (surfaced by the new feature wiring, verified on RTX 5060 Ti / CUDA 13.1): - rtx-compress build.rs: missing Path/Command/fs imports. - rtx-flash-attention flash_decode_forward: reborrow &mut kernel args. - rtx-transformers: rope kernel include path, cudarc 0.18 Arc<CudaModule>, PushKernelArg imports in jepa_gpu, edition-2024 ref patterns. - rtx-memory: full cudarc 0.18 port (CudaContext, stream-based alloc, DevicePtr accessors, error enum formatting) across gpu_pinning, gpu_transfer, gpu_real, gpu_allocator/arena, gpu_tests. JEPA (Phase 3): - JepaRunConfig::apply_cluster_plan consumes ClusterTrainingPlan (batch size, TP/DP, world size, total steps) so jepa_cluster is no longer standalone dead config; ViTSizeStr::approx_params_m feeds JepaParallelConfig::for_model_and_cluster. - WebDatasetShard::load reads real .tar shards from disk via the existing parser (gzip rejected explicitly); to_in_memory documented as synthetic/test-only. - New image-decode feature actually defines the dep for the previously unreachable cfg(feature = "image-decode") JPEG/PNG decode path. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 19:25:51 -07:00
Omar SobhandClaude Sonnet 4.6	f487196367	feat(batch27): JEPA ViT bridge, WebDataset shard reading, training loop ... 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
osobhandClaude Opus 4.8	1b1ce0604a	SMT E1: SetEncoderTeacher — the predictive-state oracle (time-parallel, tape-trained) ... Promotes the proven attention set-encoder smoke test (PR #4, tape_train_smoke.rs) into a reusable rtx-transformers layer for Supervised Memory Training. The teacher maps a window of past tokens to a fixed-size memory M via embed → self-attention → residual → mean-pool → memory projection, with a decoder head supervised by predict-the-future MSE so that M becomes a sufficient statistic of the past. - Trains end-to-end on Autodiff<CpuBackend> (the gradient-correct real backend from PR #3), with a self-contained deterministic host-side Adam. - Time-parallel by construction (one window → one memory, no recurrence to unroll) — this is the oracle whose trajectory the recurrent Mamba updater is later behaviorally cloned against, so the recurrent net never needs BPTT. - Exposes named_params/set_named_params so the caller (omni-think's PredictiveStateTeacher facade) owns safetensors persistence + BLAKE3 sealing. - Adds rtx-backend + rtx-backend-cpu deps (the tape needs a concrete backend). Tests: teacher trains (loss >5x drop), encode is deterministic + fixed-size, params round-trip. fmt + clippy(-D warnings) clean on the new module. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-16 09:37:55 -07:00
redclawsystems	4d88dc0584	Initial commit	2026-03-04 00:08:42 +00:00