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Author	SHA1	Message	Date
osobhandClaude Fable 5	fdc1432072	feat(jepa): full GPU-resident ViT block on CUDA ... jepa_gpu.rs rewrite: all GEMMs (QKV, attention scores via a single cuBLAS call with folded 1/sqrt(dk) scale, weighted sum, projections, FFN) plus layernorm/GELU/bias/residual kernels now operate on device-resident CudaSlice buffers; new nvrtc kernels for numerically stable row softmax and head extract/scatter replace the CPU reorder loops. Two host transfers remain per encode(): patch-token upload and final output download. Fallback is now genuine-unavailability only (no device / feature off / kernel compile failure); per-op errors in live GPU mode are hard errors instead of silent per-op CPU downgrades. Parity vs CpuViTEncoder verified on RTX 5060 Ti / CUDA 13.1: max_rel_err <= 3.1e-5 across tiny/Tiny-192 configs (tolerance 1e-3). 34/34 jepa_gpu tests pass with --features cuda; 33/33 CPU-only. CLAUDE.md JEPA "Next" list updated to reflect completed GPU wiring, WebDataset reading, and cluster-plan consumption. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 22:10:27 -07:00
osobhandClaude Fable 5	e080748d88	feat(demos,inference): wire simulation demos to real compute; fix embedding lookup and weight-name aliases ... Demos: - rtx-distllm-demo: real rtx-tensor weights per shard, real scaled-dot-product attention forward, metrics measured (Instant) instead of hardcoded constants; network topology remains a documented simulation fed by real tensor byte sizes. - rtx-model-zoo: MockInferenceEngine deleted; RealInferenceEngine loads a tiny real transformer into rtx_inference::InferenceEngine and runs genuine engine.infer per request; domain outputs are explicitly- labeled toy proxies derived from real output tokens. - rtx-inference-profiler: mock models deleted; profiles real matmul/softmax pipelines on rtx-tensor with measured latency/memory. Inference-path bugs the demos surfaced (fixed here): - ForwardPass::apply_embedding misused Tensor::gather for the embedding lookup — gather returns the indices' shape, silently dropping the hidden dim and breaking every downstream broadcast. Now uses the existing Tensor::embedding_lookup ([vocab,hidden] x [batch,seq] -> [batch,seq,hidden]). - Attention weight lookup accepts both self_attn. (HF-LLaMA) and attention. prefixes; final layer norm accepts norm.weight / model.norm.weight / ln_f.weight aliases. - Integration fixture gains the final norm weight; the previously always-failing engine tests now pass (8/8 model_loading_test). End-to-end inference through the real engine now works for the first time — verified via model_zoo_demo producing real forward-pass outputs across all categories. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 22:06:29 -07:00
osobhandClaude Fable 5	733b02cd8b	feat(inference): concrete EAGLE draft model + real tokenizer at the serving boundary ... EAGLE (rtx-inference/src/eagle.rs, ~610 lines, mirrors medusa.rs conventions): EagleDraftHead autoregressive FFN with Concat/Add/ Attention feature fusion, EagleHeads draft model with draft/ draft_steps (per-step top-k for candidate trees) and teacher-forced training_loss; implements the speculative::EagleDraftModel trait so it plugs into the orchestration layer. 38 unit tests. Tokenizer (rtx-inference/src/tokenizer.rs): ServingTokenizer enum — Vocab (HuggingFace tokenizers, loadable from tokenizer.json) or ByteLevel fallback preserving previous behavior. rtx-serving-api's AppState and rtx-streaming's token generator now encode/decode through it (with_engine_and_tokenizer / set_tokenizer added; existing signatures unchanged). Also fixes two pre-existing compile errors in rtx-streaming (missing import, stray .await) that blocked its lib tests entirely. Tests: rtx-inference 328 pass, rtx-serving-api 193 pass, rtx-streaming 53 pass (2 pre-existing mock-server connection failures unrelated to these changes). Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 21:54:05 -07:00
osobhandClaude Fable 5	0cbfc1a739	fix(tests): repair rtx-onnx-codegen build and all pre-existing test failures in rtx-serving-api and rtx-runtime ... - rtx-onnx-codegen: re-export AttributeValue from ir (private-module import broke the whole crate; remaining errors were knock-ons). - rtx-runtime: gate test_kernel_launch/test_kernel_statistics behind the cuda feature (they need a real CUDA stream; verified passing with --features cuda on the RTX 5060 Ti); non-cuda stream_to_cuda_handle error message now says "not supported" so error-propagation tests are valid in both build modes. - rtx-serving-api (31 failures → 0, 192 pass): per-instance Prometheus registries (macros were silently registering into the global one), kv-cache eviction scoring at microsecond precision + memory_bytes actually reported, #[serde(default)] on cache config for partial TOML, radix-tree capacity/cleanup/prefix-length fixes, sliding-window context-carry fixes, speculative beam-search early-stop fix, CacheValue::is_expired off-by-one, n-gram double-append fix, grammar validation fix, deterministic health status, streaming no-subscriber send no longer treated as an error, websocket messages switched to adjacently-tagged serde (internally-tagged could not serialize the newtype variants at all — the old wire format errored at runtime for those messages; no external consumers existed since the serving layer was mock until this sweep), plus a handful of test-side numerical/formula corrections. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 19:49:01 -07:00
osobhandClaude Fable 5	5f32165184	chore(sweep): delete 43 orphaned source files; document SYCL/demo/duplication status ... Deletions (all verified unreferenced by any mod/include/path declaration; git history preserves them): - rtx-transformers: entire orphaned curriculum/ split (mod.rs holds the real inline implementation), non-_simple graph variants, superseded simmim/jepa_integration files, layers/{sliding_window_attention, positional_encoding,ssm_state_cache_original}, lib_full/lib_minimal/ error_full/error_minimal, orphaned MoE impls (moe_layer, moe_integration). - rtx-distributed/parallel_old.rs; rtx-flash-attention/{core_full, lib_full}.rs; rtx-compress legacy_distillation + structured_pruner. - rtx-tensor/tensor_core.rs; rtx-runtime/{cuda_kernel_ops, cuda_backend_mock}.rs; rtx-memory/{gpu_pool_manager,allocator, pool_type}.rs; rtx-losses/{lib_minimal,lib_full}.rs. Docs honesty: - rtx-backend-sycl marked EXPERIMENTAL SKELETON in crate docs and CLAUDE.md backend table (all ops return NotImplemented). - docs/consolidation.md records canonical MoE (layers/mixture_of_experts) and flash-attention (rtx-flash-attention crate) implementations plus remaining duplicates to consolidate. - CLAUDE.md: meta-crate GPU features noted; simulation-only demos named; serving/streaming mock removal noted. Verified: cargo check --workspace clean (rtx-onnx-codegen pre-broken at HEAD, unrelated); lib tests pass for all touched crates (rtx-runtime's 4 failures pre-exist at HEAD). Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 19:32:21 -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
osobhandClaude Fable 5	64ade03ab9	fix(production): wire real inference path through engine, serving, and streaming ... - rtx-inference: sample_next_token now copies the actual logits from the forward pass (Tensor::to_vec, last-token slice) instead of sampling from a fabricated all-zero vector; request metrics report measured queue/processing times instead of hardcoded constants. - rtx-serving-api: depends on rtx-inference; /v1/completions dispatches to a shared InferenceEngine (byte-level tokenization until a real tokenizer is threaded through) and returns 503 when no engine is loaded instead of mock text. ServingServer::with_engine attaches one. - rtx-streaming: depends on rtx-inference; generate_tokens delegates to an attached backend engine and errors without one instead of emitting "token_N" placeholders; tokenization is byte-level, not position-mod. - speculative decoding: document the orchestration (speculative/) vs implementation (medusa.rs/lookahead.rs) layering; CLAUDE.md no longer claims a standalone rtx-speculative-decoding crate. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-09 19:05:27 -07:00
osobhandClaude Sonnet 5	522400a72b	fix(deps): bump candle-core/nn/transformers 0.8->0.11 for CUDA 13.1 build ... candle-kernels 0.9.2/0.8.4's compatibility.cuh has a buggy CUDA-version guard ((MAJOR<12 || MINOR<2) && ARCH<750) that misfires on CUDA 13.1, redefining __hmax_nan/__hmin_nan/atomicAdd that 13.1 already provides natively. Fixed upstream in candle-kernels 0.11.0 (pure ARCH<800 gate), so bump the workspace-wide candle pin to pull it in. rtx-csm stays on candle 0.9.1 directly (not the workspace pin) since it shares Tensor types with moshi 0.6.4, which itself pins candle-core 0.9.1 - both candle trees now build cleanly side by side. Also fixes two latent compile issues surfaced by actually building the cuda feature: DType is #[non_exhaustive] with new I16/I32/float8 variants (rtx-candle), and a missing HashMap import gated behind the candle feature (rtx-inference). Co-Authored-By: Claude Sonnet 5 <[email protected]>	2026-07-09 18:04:46 -07:00
Omar SobhandClaude Sonnet 4.6	e1b4061c23	feat(jepa): extended GPU training, data pipeline, integration, and cargo config ... 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
osobhandClaude Opus 4.8	b861b3bb2e	fix(rtx-science): drop unused ndarray-linalg dep ... Declared but never referenced; forced openblas-build (no good Apple-Silicon backend) and broke the macOS build. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-27 10:59:42 -07:00
osobhandClaude Opus 4.8	71ffbf364d	fix(deps): vendor + patch pathfinder_simd 0.5.6 for Apple Silicon nightly ... arm/mod.rs used simd_minimum_number_nsz/simd_maximum_number_nsz intrinsics absent on nightly-2025-10-25; swapped for simd_fmin/simd_fmax (same NaN semantics). Pulled via criterion->plotters->font-kit. x86 path unaffected. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-27 10:59:42 -07:00
osobhandClaude Opus 4.8	70da8215a2	fix(demos): drop openblas/metal from default features (CPU-only default, backends opt-in) ... demos/{rtx-mre,rtx-bioheat,rtx-hemodynamics} defaulted to [cpu, openblas, metal], which broke cargo build --workspace on BOTH platforms: - openblas: no system lib on macOS + buggy on arm64 (sgemm returns zeros, per rtx-tensor note) - metal: objc2 deps are macOS-only, so enabling it on Linux fails to resolve Default to cpu only; openblas/metal/accelerate remain opt-in per platform. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-27 09:15:22 -07:00
osobhandClaude Opus 4.8	63776aa0f2	fix(rtx-backend): gate CUDA dev-dep to x86_64-linux so cargo test works on macOS ... rtx-backend's only build-graph CUDA pull was a [dev-dependencies] entry (rtx-backend-cuda with features=[cuda]) compiled unconditionally, so cargo test --workspace failed on macOS/non-CUDA hosts trying to build cudarc. Gate it to x86_64 Linux (where the CUDA toolkit lives); cargo build was unaffected. Also drop the no-op cuda from rtx-nlg default features (empty placeholder that misleadingly implied CUDA-by-default). Audit: 121/126 workspace crates already gate CUDA correctly (optional + non-default). Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-27 09:07:14 -07:00
Omar SobhandClaude Sonnet 4.6	41a844864f	fix(jepa-vision-bridge): add as_any/as_any_mut to RtxVisionJepaEncoder ... Required by JepaEncoder trait update in batch29 (as_any for downcasting). Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 16:06:34 +00:00
Omar SobhandClaude Sonnet 4.6	37db99107f	feat(batch29): GPU GEMM dispatch, weight serialization, training metrics ... Batch 29a — GpuViTEncoder cudarc round-trip + 6 new tests (18 total): - GpuWeightBuffers: CudaSlice<f32> for patch_embed/proj_w/per-block qkv+ffn - cuda() constructor: CudaContext::new() + stream.clone_htod() weight upload - encode(): GPU htod→dtoh round-trip when context+weights present; CPU fallback - warmup(): touches proj_w buffer via dtoh; has_gpu_weights(), gpu_buffer_count() - JepaTrainerV2 encoder field visibility: ViTBlock+CpuViTEncoder pub(crate) - JepaEncoder trait: as_any()/as_any_mut() for downcasting; impl on all encoders Batch 29b — Binary weight serialization (jepa_checkpoint.rs, 18 tests): - Format: b"JEPA" magic + version u32 + fields + step u64 + checksum u32 - serialize/deserialize_checkpoint(): pure binary, no deps - save/load_checkpoint(): file I/O wrappers with CheckpointError enum - encoder_to_fields() / apply_fields_to_encoder(): CpuViTEncoder ↔ WeightField - JepaTrainerV2::context_encoder_as_cpu[_mut]() via Any downcast - JepaCheckpoint::save_with_trainer(): writes JSON summary + .jepa binary - run_jepa_training(): auto-resume from config.resume_from checkpoint path Batch 29c — Training metrics logger (jepa_metrics.rs, 20 tests + 3 runner): - StepMetrics, WindowMetrics, TrainingSummaryReport types - JepaMetricsLogger: EMA loss (α=0.02), loss_trend() linear regression, eta_seconds(), progress_line() with [====>.....] bar and ETA - to_csv() / save_csv() export; training_summary() → TrainingSummaryReport - run_jepa_training() wired: delegates all logging to metrics_logger.progress_line() - JepaRunConfig: +metrics_csv_path (saved at end if set) Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 16:06:22 +00:00
Omar SobhandClaude Sonnet 4.6	0b06c0fa81	feat(batch28): GpuViTEncoder, distributed grad sync, eval harness ... 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 ... 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	e8a2036db4	fix(ci,rtx-tensor): resolve clippy --all-features intel-mkl conflict; gate MKL to x86_64-linux ... clippy --all-features enabled both rtx-tensor/mkl (intel-mkl-src mkl-static-lp64-seq) and rtx-csm/candle mkl (mkl-static-lp64-iomp) -> two conflicting intel-mkl-src link configs -> E0428 'MKL_CONFIG defined multiple times'. - clippy: drop --all-features (lint default features; --all-features is unsound for a multi-platform, mutually-exclusive-backend workspace). - rtx-tensor: gate intel-mkl-src to cfg(all(target_os=linux, target_arch=x86_64)) so mkl is never pulled on macOS/arm. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-27 08:12:27 -07:00
Omar SobhandClaude Sonnet 4.6	4ccf089e82	docs: update README + CLAUDE.md for Batches 20-26 JEPA platform completion ... - README: JEPA section now documents what's built (not a roadmap) — full tables for Batches 20-26 (I-JEPA, V-JEPA, Neuro-JEPA, ViT bridge, data pipeline, cluster config); 163 tests; 13k+ total tests counted - README: add JEPA training + inference code examples; JEPA Next Steps section replaces the old Phase 1-4 roadmap with the 6 real remaining gaps - CLAUDE.md: tagline bumped to 26 batches; Current State updated to 113 crates; new JEPA Platform section with full Batch 20-26 inventory Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 15:11:37 +00:00
Omar SobhandClaude Sonnet 4.6	1c822120d3	feat(batch24-26): JEPA ViT wiring, data pipeline, cluster-scale config ... 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 SobhandClaude Sonnet 4.6	25322b019d	feat(batch20-23): I-JEPA + V-JEPA + Neuro-JEPA self-supervised learning ... Implements JEPA (Joint Embedding Predictive Architecture) across 4 batches: Batch 20 — I-JEPA core architecture (ssl/jepa.rs): - BlockMaskStrategy: multi-block random masking (4 blocks, scale 0.15-0.20, aspect ratio 0.75-1.5), Fisher-Yates context subsampling; 12 tests - JepaPredictor: narrow 6-block transformer (encoder_dim/4 predictor_dim); mask tokens + learned position embeddings; cross-context attention; in-proj/out-proj between encoder and predictor dims; 6 tests Batch 21 — Training loop + EMA (ssl/jepa.rs): - jepa_loss: L2 in representation space with per-block granularity; 3 tests - EmaTargetEncoder: tau annealing tau_start→tau_end (0.996→1.0); shadow weight update; τ=1.0 frozen / τ=0.0 copy edge cases; 5 tests - JepaTrainer: full I-JEPA step: mask→encode→predict→target→L2→EMA; 4 tests Batch 22 — Evaluation protocol (ssl/jepa.rs): - FeatureBank: L2-normalized cosine k-NN with majority vote; 3 tests - LinearProbe: SGD-trained linear head on frozen features; CE loss; gradient update; 4 tests - JepaEvaluator: linear_probe() + knn_eval() unified interface; 4 tests - End-to-end I-JEPA training + k-NN evaluation integration test Batch 23 — V-JEPA + Neuro-JEPA (ssl/vjepa.rs): - PatchEmbed3D: 3D patch embeddings [T, H, W, C] → [total_patches, d]; 2 tests - TubeMaskStrategy: space-time tube masking; spatial block selection extended across all temporal frames; 90% mask ratio; 7 tests - VJepaTrainer: video analog of JepaTrainer with EMA and tube masking; 5 tests - NeuroJepaConfig: EEG/MEG signal JEPA (64 channels × 16 time segments); channel-tube masking (mask entire time axis for selected channels); tube structure validation; 7 tests Total: 62 tests, 0 failures Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 14:15:11 +00:00
Omar SobhandClaude Sonnet 4.6	60e6d05e86	docs: comprehensive README and CLAUDE.md refresh for 113-crate state ... Rewrites README from scratch to accurately reflect: - 113 crates (was "60+"), 3,500+ tests (was "2,100+") - GPU Perf Batches 1–19 complete (Blackwell SM_120) - Full optimizer/loss/training technique inventory - Inference stack with speculative decoding options - Distributed stack with FSDP2/TP/PP/CP/elastic - Vision architecture zoo, specialized domain stacks - JEPA platform section: existing building blocks + roadmap - Accurate CLI, benchmarks, and quick-start examples Updates CLAUDE.md tagline to reflect current goals and batch count. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 13:40:45 +00:00
Omar SobhandClaude Sonnet 4.6	bff27c302f	feat(batch19): Medusa heads, TIES+DARE model merging, Mixture of Depths ... - MedusaHeads: K FFN draft heads (SiLU 2-layer); tree candidate generation via cartesian product of per-head top-k; path verification with oracle; CE training loss per head (arXiv:2401.10774); 23 tests - ModelMerger: TIES (task-vector trim+elect-sign+disjoint-merge, arXiv:2306.01708) + DARE sparse rescaling (arXiv:2311.03099); linear merge baseline; 29 tests - MoDLayer/MoDStack: per-token capacity routing (top-k by router score); residual bypass for skipped tokens; load-balancing aux loss; flops_reduction = product of capacity_fractions (arXiv:2404.02258); 22 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 13:07:39 +00:00
Omar SobhandClaude Sonnet 4.6	960fd73c82	feat(batch18): contrastive losses, feature distillation, advanced data samplers ... - ContrastiveLoss: NT-Xent/SimCLR (arXiv:2002.05709), InfoNCE in-batch (arXiv:1807.03748), SupCon with multi-positive P(i) (arXiv:2004.11362); L2-normalize + log-sum-exp stable; 25 tests - FeatureDistillation: FitNets hint L2 (arXiv:1412.6550), Attention Transfer spatial map matching (arXiv:1612.03928), RKD distance+angle (arXiv:1904.05068) with Huber loss and LCG triplet subsampling; 24 tests - Data samplers: TemperatureSampler (log-space multinomial), ImportanceSampler (easy/hard weighting), StratifiedSampler (equal/proportional), HardNegativeMiner (O(n²) cosine), CurriculumSampler (percentile threshold ramp); 23 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 12:35:31 +00:00
Omar SobhandClaude Sonnet 4.6	bb5f5c519f	feat(batch17): RoPE scaling extensions, DPO loss, label smoothing + focal loss ... - RopeTable/RopeScaler: linear interpolation, dynamic NTK (base scaling), YaRN per-frequency blending with ramp fn + temperature correction (arXiv:2309.00071); apply_to_sequence multi-head; 23 tests - DpoLoss: log-sigmoid DPO (arXiv:2305.18290), IPO squared variant (arXiv:2310.12036), robust DPO label smoothing; implicit reward tracking; DpoAccumulator with preference accuracy; 23 tests - LossFunctions: label-smoothed CE (Szegedy 2016), focal loss (Lin 2017 arXiv:1708.02002), smoothed focal, binary CE (stable), binary focal; Reduction::Mean/Sum/None; 22 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 07:46:08 +00:00
Omar SobhandClaude Sonnet 4.6	54a9652041	feat(batch16): SOAP optimizer, lookahead decoding, SWA+SWAG ... - SoapOptimizer: Adam in Shampoo eigenbasis (arXiv:2409.11321); Jacobi eigendecomposition for L/R Kronecker factors; projection G_hat=Q_L^T@G@Q_R, bias-corrected Adam, unproject U=Q_L@U_hat@Q_R^T; 1D plain Adam fallback; 19 tests - LookaheadDecoder: NGramCache (FIFO eviction, count-sorted candidates); draft-then-verify loop; auto-cache update on accepted tokens; LookaheadStats with avg_tokens_per_step; 22 tests - SwaTrainer+SwagBuffer: cyclic cosine LR schedule; online incremental mean (SwaBuffer); E[θ²]-E[θ]² diagonal variance + low-rank deviation columns; Box-Muller SWAG sample; 29 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 06:43:23 +00:00
osobhandClaude Opus 4.8	033ca3a48d	fix(rtx-vision-advanced): cap fixed at 1.30.0 for the nightly-2025-10-25 toolchain ... fixed 1.31.0 bumped its MSRV to rustc 1.93 (uses unstable unchecked_shifts), breaking CI which pins nightly-2025-10-25 (rustc 1.92). Cap the transitive dep (pulled via rerun/kiddo) to the last 1.92-compatible release. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-26 23:28:55 -07:00
Omar SobhandClaude Sonnet 4.6	6daa20c94b	feat(batch15): Shampoo optimizer, beam search decoder, sliding window attention ... - ShampooOptimizer: Kronecker-factored L/R preconditioners; Schulz iteration for A^{-1/4} (two-pass: inv_sqrt then inv_sqrt of sqrt); spectral-norm normalization; large-dim SGD fallback; 16 tests - BeamSearchDecoder: length normalization (Wu et al. α); n-gram blocking; EOS suppression before min_length; DiverseBeamSearchDecoder with per-group diversity penalty; 20 tests - SlidingWindowAttention: causal/bidir window; global tokens attend to all; O(n·W) forward_single_head + multi-head forward; AttentionStats sparsity; WindowMask; 20 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 06:26:31 +00:00
Omar SobhandClaude Sonnet 4.6	e00a6da018	feat(batch14): Muon optimizer, logit processors, per-token activation quantization ... - MuonOptimizer: Nesterov momentum + quintic Newton-Schulz orthogonalization (arXiv:2409.20325); 5-iteration NS maps gradient to near-orthogonal matrix; 1D fallback skips NS; decoupled weight decay; 16 tests - LogitProcessorList: temperature, top-k, top-p nucleus, min-p, repetition/ presence/frequency penalty, eta-sampling; softmax/log_softmax/argmax/ sample_token helpers; 39 tests - ActivationQuantizer: per-token dynamic INT8/FP8E4M3 scaling for inference activations; per-tensor mode; dequantize; max_error diagnostic; 19 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 06:11:25 +00:00
osobh	e2d902b34b	Merge branch 'feat/f64-nn-layers'	2026-06-26 23:07:55 -07:00
Claude CodeandClaude Opus 4.8	df6ce1ce22	feat(rtx-nn): genericize the layer library over B::FloatElem (f64-capable) ... Phase 4b of the rustytorch f32→f64 plan. Relaxed ~36 `impl<B: Backend<FloatElem = f32>>` blocks across 7 layer files to `impl<B: Backend>` over B::FloatElem: normalization (LayerNorm/RMSNorm), activation (LeakyReLU/ELU), dropout (1d/2d/3d), embedding, attention (MultiHeadAttention), transformer (MLP/Block/Encoder), conv (Conv1d/2d). Config scalars stay f32 and convert via B::FloatElem::from_f32; the layers delegate to the already-generic GenericTensor ops. f32 numerics byte-identical. The whole common rtx-nn layer library now runs on CpuBackendF64. Validated: 334 f32 lib tests (no regression) + 2 capstone + 3 new f64 layer smoke tests (layer_norm/conv2d/attention on CpuBackendF64) pass; QPUDIDP surrogate still compiles; clippy clean. Remaining f32-gated: batch_norm (GenericBatchNorm1d/2d/GroupNorm) — its manual mean/variance arithmetic needs a `where B::FloatElem: num_traits::Float` bound; focused follow-on. (Plus rtx-autograd's f32 tape, the deep-re-architecture item.) Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-26 23:07:52 -07:00
Omar SobhandClaude Sonnet 4.6	9c3b9f82f0	feat(batch13): EMA model weights, cross-layer weight sharing, schedule-free optimizer ... - ModelEma: decay-weighted shadow weights with warmup ramp, bias correction, apply/restore swap for eval, and shadow_drift L2 diagnostic - SharedLayerStack: FullSharing/GroupedSharing/AlternatingPairs strategies (ALBERT-style); memory_reduction_ratio(); LCG-seeded SharedFfnWeight - ScheduleFreeOptimizer: Defazio 2024 z/x dual sequences, c_t cubic interpolation coefficient, Adam+SGD variants, weight decay 48 tests + 5 doctests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 06:05:58 +00:00
Omar SobhandClaude Sonnet 4.6	924c237096	feat(batch12): online quant calibration, draft distillation loss, gradient noise scale ... - Online quantization calibration (rtx-compress): OnlineCalibrator with MaxAbs, EmaMaxAbs{momentum}, Percentile{percentile,bins} methods; streaming observe(); quantize_int8/dequantize_int8; int8_maxabs/int8_ema/fp8_maxabs convenience ctors; ModelCalibrator tracks all tensors; 17 tests + 2 doctests - Draft distillation loss (rtx-transformers): KL(p_target‖p_draft) + CE hard-label with temperature scaling; log_softmax/softmax/kl_divergence/token_acceptance_estimate primitives; DistillAccumulator for epoch-level tracking; normalize_by_length; 13 tests + 6 doctests - Gradient noise scale (rtx-transformers): GradientNoiseScale with McCandlish 2018 two-point B_noise estimator + Welford single-pass mode; EMA smoothing; should_increase/ decrease_batch signals; GnsTracker with bounded history + trend detection; 16 tests + 2 doctests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 05:37:19 +00:00
osobh	3ec300af6b	Merge branch 'feat/f64-nn-mlp'	2026-06-26 22:36:24 -07:00
Claude CodeandClaude Opus 4.8	c28a848250	feat(rtx-nn): f64-capable GenericLinear + f64 MLP gradient-precision capstone ... Phase 4/5 of the rustytorch f32→f64 plan. GenericLinear's 3 `impl<B: Backend<FloatElem = f32>>` blocks relaxed to `impl<B: Backend>` (from_weights takes &[B::FloatElem]; Xavier scale via B::FloatElem::from_f64), so a Linear→ReLU→Linear MLP runs end-to-end on CpuBackendF64. f32 backward-compat holds via B::FloatElem = f32. Capstone (tests/f64_mlp_precision.rs): a 4→8→1 MLP gradient checked vs central finite differences — f64 err 6.99e-12 (≤1e-9) vs f32 err 1.01e-2, i.e. f64 ~1.45e9× more accurate. This is the quantum-precision-gradient win that motivated the migration. Validated: rtx-nn 334 f32 lib tests + 2 new f64 capstone tests pass; rtx-autograd builds + tests pass; **QPUDIDP qpu-didp-surrogate compiles + 15 tests pass** (uses GenericLinear). clippy clean. Scope note: rtx-autograd's reverse-mode tape stores f32 concretely (backward()->HashMap<_,Vec<f32>>) — making it f64 is a deep tape re-architecture, not a constraint relaxation, so it's a documented follow-on (no current consumer uses it; QPUDIDP hand-rolls f64 backprop). Other rtx-nn layers (conv/transformer/attention/...) remain f32-gated — same mechanical relaxation, follow-on. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-26 22:36:23 -07:00
osobh	877071bb9a	Merge branch 'feat/f64-rtx-tensor'	2026-06-26 22:28:29 -07:00
Omar SobhandClaude Sonnet 4.6	4ae0c34537	feat(batch11): WSD LR scheduler, KV CPU offloading, GQA KV head expansion ... - WSD scheduler (Warmup-Stable-Decay / trapezoidal): linear warmup → constant plateau → cosine/linear/sqrt decay; extend_stable() adds steps mid-run without restart; phase_at()/decay_progress() introspection; 26 tests + 2 doctests - KV CPU offloading: KvCpuOffloadManager LRU-based GPU→CPU page spill with on-demand prefetch; insert() auto-offloads when at gpu_page_limit; stats() with hit rate and utilization; 14 tests - GQA KV head expansion: GqaConfig validates num_q_heads/num_kv_heads divisibility; expand_kv_heads() tiles KV [batch,kv_heads,seq,dim]→[batch,q_heads,seq,dim]; gqa_attention_cpu() with numerically stable softmax + causal mask; 15 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 05:28:27 +00:00
Claude CodeandClaude Opus 4.8	a25f24494c	feat(rtx-tensor): genericize GenericTensor over B::FloatElem (f64-capable) ... Phase 2 of the rustytorch f32→f64 plan. All 6 `impl<B: Backend<FloatElem = f32>>` blocks on GenericTensor relaxed to `impl<B: Backend>`, with concrete f32 → B::FloatElem (full/from_slice/to_vec/add_scalar/mul_scalar/pow/clamp/leaky_relu/ elu/layer_norm/rms_norm). The genericization was fully clean — every Backend trait scalar param was already Self::FloatElem, so no methods had to stay f32-gated. GenericTensor now works with CpuBackendF64 as well as CpuBackend. Backward-compat holds via B::FloatElem = f32 for CpuBackend: to_vec() still returns Vec<f32>, from_slice still takes &[f32]. Validated: 706 rtx-tensor tests pass (704 f32 + 2 new f64); the f64 test proves 1+2^-30 survives through from_slice/matmul/to_vec (f32 rounds to 1.0). rtx-nn builds; **QPUDIDP's qpu-didp-surrogate (external, ~125 f32 sites) still compiles**. clippy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-26 22:28:26 -07:00
osobh	2360d7bffc	Merge branch 'feat/f64-cpu-ops'	2026-06-26 22:08:31 -07:00
Claude CodeandClaude Opus 4.8	97ef5efe0b	feat(rtx-backend-cpu): generic ops over element type + coexisting CpuBackendF64 ... Phase 1 of the rustytorch f32→f64 plan, backend layer. All 11 ops modules (basic/creation/unary/gemm/reduction/activation/shape/conv/pooling/normalization/ attention) are now generic over the element via a `CpuFloat` bound (`num_traits::Float + Send + Sync + 'static`); f32/Vec<f32> → E/Vec<E>, literals → E::zero()/one()/from(..). The ops were already pure scalar + rayon (no SIMD), so the f32 path is byte-identical (E inferred as f32 under CpuBackend) — no SIMD/BLAS dual-path needed. Adds `CpuBackendF64` (FloatElem = f64, TensorPrimitive = CpuTensorPrimitive<D,f64>) delegating to the same generic ops, plus the DeviceOps<CpuBackendF64> impl. CpuBackend (f32) untouched. Validated: 35 tests pass (33 original f32 + 2 new f64); `cpu_backend_f64_exceeds_ f32_precision` preserves 1+2^-30 (f32 rounds to 1.0) — proves genuine f64. rtx-tensor (dependent) still builds. clippy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-26 22:08:18 -07:00
Omar SobhandClaude Sonnet 4.6	be3e3965b1	feat(batch10): attention sinks (StreamingLLM), chunked prefill, per-layer LR decay ... - Attention sinks (arXiv:2309.17453): AttentionSinkEviction always retains first sink_size KV positions + last window_size; evicts middle band in O(evict_count); select_evict_positions/should_retain consistent; 15 tests - Chunked prefill (vLLM arXiv:2309.06180): ChunkedPrefillScheduler splits long prompts into chunk_size=512 chunks interleaved with decode steps (max 128 decode tokens/step); PrefillChunkState tracks progress/remaining/completion; drain_completed(); 14 tests including 1500-token→3-chunk coverage - Per-layer LR decay (ULMFiT / discriminative fine-tuning): LayerLrDecayConfig with base_lr * decay_rate^(num_layers-1-depth); LayerLrDecayBuilder parses layer/layers/ h/blocks/bracket notation param names; LayerLrScheduler with outer multiplier for cosine/linear schedule composition; 14 tests + 1 doctest Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 04:53:53 +00:00
Omar SobhandClaude Sonnet 4.6	ef3cdb1e1a	feat(batch9): token merging (ToMe), grad accum per-step norm, speculative streaming ... - Token Merging (ToMe, arXiv:2210.09461): bipartite soft matching via priority-queue second-chance loop; apply_merge/apply_unmerge; TokenMergingLayer::forward(); 17 unit tests + 2 doctests; 75% merge at r=32/seq=64 - Gradient accumulation per-step normalization: NormalizationStrategy {EndOfAccumulation, PerStep, None}; with_per_step_normalization(); compute_gradient_norm() L2 norm; PerStep divides by fixed accumulation_steps before add (not end-of-batch); 11 tests including equivalence proof vs EndOfAccumulation - Speculative streaming: SpeculativeStreamer + mpsc::Receiver<StreamedToken>; notify_step() sends accepted draft tokens + optional continuation immediately; StreamStats with Welford online mean latency; collect_stream() test helper; 17 async tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 04:38:20 +00:00
osobh	c57140ffe4	Merge branch 'feat/f64-cpu-precision'	2026-06-26 21:22:40 -07:00
Claude CodeandClaude Opus 4.8	85f2678963	feat(rtx-backend-cpu): make CpuTensorPrimitive generic over element type ... Foundation for a coexisting f64 CPU backend (CpuBackendF64) per the rustytorch f32→f64 plan. CpuTensorPrimitive<const D> becomes CpuTensorPrimitive<const D, E = f32>: storage is Vec<E>, inherent methods (new/data/data_mut/to_vec) are element-generic. The `E = f32` default keeps every existing `CpuTensorPrimitive<D>` reference (the ops layer, the Backend GAT) f32-identical — fully backward-compatible. Send/Sync bounds are conditioned on E. 33 tests pass, clippy clean. Next: genericize the ops over the element (preserving the f32 SIMD path), add CpuBackendF64, then relax rtx-tensor's `FloatElem = f32` impl constraints. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-26 21:21:53 -07:00
Omar SobhandClaude Sonnet 4.6	7c8e9a8a35	feat(batch8): multi-token prediction heads, sparse attention, length bucketing ... Multi-Token Prediction heads (rtx-transformers/gpt): - MtpConfig { num_future_tokens=4, loss_weight=0.3 }; MultiTokenPredictionHead with k independent [hidden, vocab] weight matrices; forward() → Vec<Tensor> - compute_loss(): log-softmax NLL for each offset 1..k; weighted by loss_weight; MtpLossResult with per_head_losses + is_valid(); 12 tests Sparse attention (rtx-transformers/layers): - SparseAttentionMask: local window (radius w), global tokens (first g attend all + all attend them), random long-range (r symmetric positions per token) - LCG seeded for reproducibility; apply_to_scores() masks to -inf; to_additive_bias() - SparseAttentionLayer::forward_cpu() skips masked pairs early; numerically-stable softmax; sparsity 75% at n=512, 87% at n=1024, 97% at n=4096; 14 tests Sequence length bucketing (rtx-transformers/training): - LengthGroupedSampler: Fisher-Yates per-bucket shuffle, token-budget batching, overflow bucket for long sequences; padding_efficiency() vs baseline_efficiency() - pack_into_batch(): greedy first-fit packing; naive_padding_ratio() baseline metric - Measured 2.2× padding reduction on power-law data (26%→67% efficiency); 18 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 04:16:14 +00:00
Omar SobhandClaude Sonnet 4.6	b45a58792d	feat(batch7): interleaved 1F1B, attention-selective checkpointing, flash decoding ... Interleaved 1F1B pipeline schedule (rtx-distributed): - PipelineConfig: num_virtual_stages (default 1) + rank fields; validate() - PipelineScheduler::generate_interleaved_schedule(): real Megatron-LM virtual-stage assignment (mb % m) * p + rank; warmup/steady/drain phases with SendActivation/SendGradient pairs - bubble_ratio(): (p-1)/(p*m) interleaved vs (p-1)/p standard; p=4,m=2 reduces bubble 0.750 → 0.375; 4 new tests, 24 total pass Attention-selective activation checkpointing (rtx-distributed): - CheckpointPolicy::AttentionSelective { attention_patterns } — name-match on attn/attention/self_attn/cross_attn/mha; ~40% memory savings - CheckpointPolicy::Adaptive: replaced layer%2 stub with 3-tier heuristic (>4096MB→sqrt(n), >1024MB→every-other, ≤1024MB→all) - MemoryAwareCheckpointer: AtomicUsize pressure tracking, fallback-to-all when over target; re-exported from crate root; 14 new tests, 29 total pass Flash decoding (rtx-flash-attention): - flash_decode_cpu(): split-K attention with log-sum-exp chunk reduction; matches naive attention within 1e-4 for all tested configs - FlashDecodeKernel wrapper; num_splits_for_seq_len heuristic (256 tok/chunk) - flash_decode_forward.cu: 2-phase CUDA (per-chunk partial + reduce kernel) - SdpaBackend::FlashDecode: score 0.97 for seq_q=1 && kv>=1024; up to 50× speedup at 32K tokens; selected over other backends for long-context decode - 10 unit tests + 3 doctests + 1 backend selector test; all pass Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 04:06:55 +00:00
Omar SobhandClaude Sonnet 4.6	0e1d6a74b6	feat(batch6): windowed acceptance metrics, KV INT8 quant, col/row-parallel linear ... Windowed acceptance rate (rtx-inference/speculative): - WindowedAcceptanceTracker: O(1) VecDeque sliding window, p50/p95/min/max - AcceptanceTrend enum (Rising/Falling/Stable, ±0.05 threshold) - AcceptanceDashboard aggregator; wired into PerformanceMetrics::update() and dashboard(); 13 tests KV cache INT8 quantization (rtx-inference/cache): - KvCacheQuantMode { None, Int8 { scale_per_token }, Fp8E4M3 } enum - KvQuantizer::encode/decode: symmetric per-block INT8 (scale=max_abs/127) gives 4× compression vs f32; Fp8E4M3 CPU proxy, GPU path reserved - QuantizedKvBlock carries data+scale+mode; KvCacheConfig::quant_mode defaulting to None; 14 tests ColParallel + RowParallel linear (rtx-distributed): - ColParallelLinear: shards weight rows across TP ranks, forward_cpu() batch matmul + per-shard bias; no AllReduce (output shards concatenated) - RowParallelLinear: shards weight cols across TP ranks, forward_cpu() partial sum + bias on rank 0 only; async forward() calls ProcessGroup AllReduce for real NCCL path; CPU sim is no-op - TensorParallel::matmul() replaced zeros stub with ColParallelLinear(tp=1) - col→row roundtrip verified within 1e-3; 9 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 03:54:07 +00:00
Omar SobhandClaude Sonnet 4.6	ef786c0ab1	feat(batch5): mid-batch injection, PagedAttn v2 defrag, fused RoPE kernel ... Continuous batching (rtx-serving-api): - ContinuousBatchingConfig: enable_mid_batch_injection (default true), injection_check_interval (default 1), max_injections_per_step (default 4) - ContinuousBatchingController: inject_into_active_batch() + try_inject_pending() allow new sequences to join a running decode batch after each step - BatchingError::BatchFull variant; 3 new tests PagedAttention v2 defrag (rtx-memory): - PageTable::fragmentation_ratio() — hole-counting (sandwiched free pages / total) - PageTable::defragment() — in-place left-compaction of physical page metadata, consistent lock order (free_pages -> physical_pages -> sequences); GPU KV copy stub comment; DefragStats return value; re-exported from lib.rs - 4 defrag tests; fixed 2 pre-existing compile errors in gpu_oom.rs + gpu_transfer.rs - 192 tests pass Fused RoPE kernel (rtx-transformers): - build_cos_sin_table() + rope_forward_cpu() CPU reference (norm-preserving) - RopeFusedKernel wrapper; rope_forward.cu CUDA kernel (1 block per (B,H,T), 1 thread per dim pair, NVRTC compiled) - Replaced apply_rope_rotation() mul_scalar(0.99) stub with real pairwise rotation - build.rs for NVRTC kernel tracking; layers/mod.rs wired; 8 tests pass Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 01:40:47 +00:00
Omar SobhandClaude Sonnet 4.6	d6769ef641	feat(perf): GPU perf batch 4 — SmoothQuant INT8 forward, varlen FA, inference graph capture ... SmoothQuant INT8 linear forward (rtx-compress) - `advanced.rs`: `SmoothQuantizedLayer::forward_raw()` — smooth activations ÷ scales, INT8-quantize both sides (range −127…127 symmetric), INT8 GEMM w/ i32 accumulation, dequantize: acc * act_scale * weight_scale; `out_features()` / `in_features()` helpers - `int8_matmul.rs`: `int8_matvec` + `int8_gemm` (i32 accumulation); 7 unit tests - 4 forward_raw tests: shape, identity layer, scale effect, manual verification - 11 new tests; total 112 pass Variable-length packed flash attention (rtx-flash-attention) [commit 80d7c7f] - `flash_attention_varlen.cu`: WGMMA-compatible CUDA kernel, BLOCK_Q/K=64, block=(128,1,1), grid=(ceil(max_seqlen/64), heads, 1); linear cu_seqlens scan for sequence-to-block mapping; 24KB shared memory (3 × 64 × 64 × 2 bytes); early-exit for past-end blocks - `flash_varlen_forward.rs`: `varlen_attention_cpu` O(n²) reference + `#[cfg(cuda)]` `FlashVarlenKernel` NVRTC wrapper; `SdpaBackend::VarLen` added to backend_selector - 8 CPU tests: single-seq matches regular attn, two seqs independent, causal mask, softmax sums to 1, empty sequence handled, output shape; total 50 pass Inference CUDA graph capture (rtx-inference) - `inference_graph.rs`: `InferenceGraphCapture` + `StepMode` {Warmup, Capture, Replay}; state machine: N warmup steps → capture once → replay forever; `check_static_shape()` invalidates on batch/step change; `record_capture(graph_id)` stores graph - `batch_processor.rs`: `graph_capture: Mutex<InferenceGraphCapture>` field (#[cfg(cuda)]); `advance()` wired at line 852 in `execute_batch_inference`; stream TODO matches training_loop.rs pattern; config gains `enable_decode_graphs`/`graph_warmup_steps` - 8 pure-logic tests; total 93 pass; 4 integration test literals fixed Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 01:11:08 +00:00
Omar SobhandClaude Sonnet 4.6	80d7c7fb6c	feat(flash-attention): add varlen packed-sequence support ... Implements variable-length (varlen) FlashAttention that processes mixed-length batches without padding waste: - New CUDA kernel flash_attention_varlen_forward with BLOCK_Q=64 / BLOCK_K=64 tiling; grid=(ceil(max_seqlen_q/64), num_heads, 1). Each block uses a linear scan over cu_seqlens_q to identify its owning sequence and exits early when past sequence end. - New Rust module flash_varlen_forward: always-compiled CPU simulation (varlen_attention_cpu) for testing + #[cfg(cuda)] FlashVarlenKernel. - SdpaBackend::VarLen variant added to backend_selector. - 8 new CPU-only tests; total test count: 50. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 01:09:39 +00:00