32 Commits

Include renames

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
osobhandClaude Fable 5	ad6405663f	fix(streaming): sane DynamicBatchingConfig default; worker lifecycle regression tests ... The new lifecycle tests caught that the batch-optimizer worker crashed at spawn: DynamicBatchingConfig derived Default (all zeros), and tokio's interval() panics on a zero period. Default is now a usable config (batch 32 in [1,128], step 4, 1ms latency target, 100-sample window, 1s optimization interval, AIMD adaptation). New regression tests in AdaptiveProcessor, EdgeComputingManager, and MonitoringSystem assert that all workers are still alive shortly after start() (catches workers dying at startup) and that stop() completes via the graceful control-channel path, not the 5s abort backstop (catches shutdown hangs). cargo test -p rtx-streaming: 58 lib + 8 integration + 6 aux, all green. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-10 17:16:04 -07:00
osobhandClaude Fable 5	c83e0fb22d	fix(streaming): wire worker control planes for real graceful shutdown ... Follow-up to a0bf294, which tolerated dead control channels; this makes them functional: - AdaptiveProcessor: mpsc control channel (single consumer behind a mutex, broke on ANY message including Start) replaced with broadcast; all three workers (resource monitor, batch optimizer, pressure monitor) subscribe and exit only on ControlCommand::Stop - EdgeComputingManager / MonitoringSystem: their 7 interval-loop workers now subscribe to the existing broadcast control channels and exit on Stop instead of looping forever - stop() in all three: graceful join with 5s timeout, abort only as a backstop (previously unconditional abort mid-tick) - benches: criterion needs async_tokio for Bencher::to_async — bench target now compiles (clippy --all-targets clean) cargo test -p rtx-streaming: 55 lib + 8 integration + 6 aux, all green. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-10 17:04:35 -07:00
osobhandClaude Fable 5	a0bf29461b	fix(streaming): real inference backend wiring and lifecycle fixes; full suite green ... - token_generator: backend is now an optional real rtx-inference engine (RwLock<Option<Arc<InferenceEngine>>>) with ServingTokenizer support; set_backend/set_tokenizer plumbing through StreamingServer - connection_manager: ConnectionPool::acquire no longer errors when the idle cache is full — creates fresh connections up to max_connections - streaming_server: ServerState::Running on construction; stream_inference generates one token per step (chunk_size semantics) - lifecycle bugs surfaced by the newly-compiling integration tests: * start(): broadcast control-channel send with zero subscribers was treated as fatal ("channel closed") in RealtimePipeline, EdgeComputingManager, MonitoringSystem — now tolerated * stop(): AdaptiveProcessor/EdgeComputingManager/MonitoringSystem awaited worker interval loops that never exit (test hung 5h) — workers are now aborted with cancellation-aware join - integration_tests: removed stale .await on now-synchronous methods cargo test -p rtx-streaming: 55 lib + 8 integration + 6 aux, all passing. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-07-10 15:48:33 -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	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	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	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
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
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
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
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	a670045f46	feat(perf): GPU perf batch 2 — EAGLE-3 dynamic draft trees + GaLore-2 optimizer ... EAGLE-3 dynamic draft trees (rtx-inference) - `eagle3.rs`: `Eagle3Config` (min_depth=1, max_depth=6, expansion_threshold=0.4, beam_width=3, self_consistency=true, prune_threshold=0.05), `DynamicDraftTree` with confidence-gated BFS expansion + iterative bottom-up cascade pruning + `all_paths()` / `accept_path()`, `Eagle3Decoder::build_draft_tree()` with cheap hidden-state proxy for child nodes (parent states scaled by child probability) - `tree.rs`: added `path_probability(leaf)`, `leaves()` (tombstone-safe DFS) - `types.rs`: added `DraftModelType::Eagle3` variant - 10 new unit tests via `FixedProbDraftModel` mock (no GPU required); total 85 pass GaLore-2 low-rank optimizer state (rtx-transformers) - `galore.rs`: `GaLoreConfig` (rank=128, update_proj_gap=200, scale=0.25, min_param_size=4096, momentum_inheritance=true), `GaLoreParamState` (proj_matrix [rows×rank], m_lr/v_lr [rank×cols]), `GaLoreAdamW` implementing `Optimizer` trait - Randomized range-finder sketched SVD: Ω~N(0,1) via LCG, Y=G@Ω, Gram-Schmidt QR - Momentum inheritance: project old m_lr onto new subspace on refresh - Automatic fallback to standard AdamW for params smaller than `min_param_size` - Memory ratio at rank=64, param=256×256: 2×(64×256) vs 2×(256²) = 25% of full state - `mod.rs`: `pub mod galore` + re-exports - 12 unit tests (all CPU); total 102+12 pass Combined: 85 + 114 = 199 lib tests pass across rtx-inference and rtx-transformers Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-26 22:43:09 +00:00
Omar SobhandClaude Sonnet 4.6	082a50e3a0	feat(perf): GPU perf batch 1 — wire GPU execution paths for FP8, FA3, CUDA Graphs, SnapKV ... FP8 GPU FFI (rtx-tensor) - `fp8_cast.rs`: replaced `not_implemented` stubs with real cudarc 0.18.2 PTX launches; `cast_bf16_to_fp8_e4m3` and `cast_fp8_e4m3_to_bf16` now dispatch to NVCC-compiled `fp8_cast.ptx` via `LazyLock` module cache, matching the `inplace_ops` pattern - `build.rs`: `create_dummy_ptx` now emits `fp8_cast.ptx` alongside `element_wise.ptx` so `include_str!` resolves cleanly when NVCC is absent FlashAttention-3 typed kernel launch (rtx-flash-attention) - `flash_v3_forward.rs`: `forward()` now takes typed `CudaSlice<bf16>` Q/K/V/O + `CudaSlice<f32>` LSE buffer; dispatches via `stream.launch_builder` with block_dim=(128,1,1), grid_dim=(ceil(seq_len/64), batch*heads, 1), shared_mem_bytes=0 (PTX metadata-resolved) - `simple.rs`: added `has_flash_v3()` + `flash_attention_v3_forward_raw()` dispatch - `Cargo.toml`: `half` added as optional cuda-gated dependency CUDA Graphs stream threading (rtx-transformers) - `training_loop.rs`: added `cuda_stream: Option<CudaStreamHandle>` field; `set_cuda_backend()` now creates a non-default capture stream; capture step calls real `begin_capture(stream)` + `end_capture(stream)`; added `set_cuda_stream()` override; replay unchanged (no stream needed) SnapKV + prefix cache BatchScheduler wiring (rtx-inference) - `scheduler.rs`: added `prefix_hit_pages: Option<Vec<PageId>>` + `evicted_positions: Vec<usize>` to `SchedulerRequest`; `BatchScheduler` gains `kv_cache` + `snapkv_eviction` fields; `submit_request` does non-blocking `try_lock` prefix lookup; added `notify_prefill_complete` (registers prefix + runs `select_evict_positions`), `set_kv_cache`, `set_snapkv_eviction`, `get_evicted_positions`, `get_prefix_hit_pages` — +5 new integration tests Test results: 22 + 42 + 75 + 102 = 241 tests, 0 failures Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-26 20:39:58 +00:00
Omar SobhandClaude Sonnet 4.6	1eb89c5b2b	feat(perf): GPU perf batch 1 — FP8, FA3 Blackwell, CUDA Graphs, SnapKV, prefix cache ... Item 1 — CUDA Graphs wiring (rtx-transformers) - Added `enable_cuda_graphs: bool` (default false) + `cuda_graph_warmup_iters: usize` (default 3) to `TrainingConfig` - Wired 3-phase state machine into `training_loop.rs` (warmup → capture → replay) gated on `#[cfg(feature = "cuda")]`; stream plumbing stubbed with TODO pending `CudaStreamHandle` threading Item 2 — FP8 E4M3/E5M2 training infrastructure (rtx-tensor, rtx-transformers) - `fp8_cast.cu`: dual-path CUDA kernels — SM_89+ uses `<cuda_fp8.h>` native `__nv_cvt_*` intrinsics; older SM uses software bit-manipulation fallback - `fp8_cast.rs`: host-side CPU casting + `#[cfg(feature = "cuda")]` GPU stubs - `fp8_gemm.rs`: bit-accurate E4M3 decoder/encoder, BF16 round-trip utils, CPU reference matmul with cuBLASLt GPU path documented inline; 12 unit tests - `training_config.rs`: `fp8_training: bool`, `fp8_e4m3_forward: bool` - `linear.rs` (modular): `fp8_mode: bool` field + forward dispatch stub - build.rs: registers `fp8_cast.cu` alongside existing `element_wise.cu` - 22 FP8 unit tests — all pass Item 3 — FlashAttention-3 Blackwell (WGMMA + TMA + warp specialization) - `flash_attention_v3_forward.cu`: SM_90+ warp-specialised producer/consumer kernel (producer TMA-loads K/V tiles, consumers run WMMA as portable WGMMA proxy); SM_89+ FP8 header path; SM<90 standard FA2-style WMMA fallback - `flash_v3_forward.rs`: NVRTC wrapper (`compile_ptx` via `include_str!`), `FlashV3ForwardKernel::new/is_supported/forward`; 6 unit tests - `backend_selector.rs`: `SdpaBackend::FlashAttentionV3`, `for_compute_capability`, `supports_flash_v3` (major >= 9), FA3 scoring (0.98/0.90/0.70), 2× speedup estimate - `kernels/simple.rs`: `v3_kernel: Option<FlashV3ForwardKernel>` in `FlashCudaKernels` - Fixed pre-existing `Device::Cpu` cfg-gate bug in `tensor/creation.rs` - 8 new FA3 backend tests + 6 kernel unit tests; 50 total pass Item 4 — SnapKV attention-score eviction + prefix caching (rtx-inference, rtx-serving-api) - `prefix_index.rs`: `PrefixIndex` with 8MB Zobrist hash table (Knuth MMIX LCG seed), `compute_hash/lookup/insert/remove/remove_page`; 10 unit tests - `eviction.rs`: `AttentionScoreEviction` struct — `accumulate_scores` + `select_evict_positions` (retain top keep_ratio + last recent_window); 7 unit tests - `types.rs`: `EvictionPolicy::AttentionScore { keep_ratio, recent_window }` + `KvCacheConfig::enable_prefix_caching` - `paged_kv_cache.rs`: `prefix_index: Option<PrefixIndex>` + `lookup_prefix / register_prefix / unregister_prefix_page / prefix_caching_enabled` methods - `config.rs` (serving-api): `enable_prefix_sharing: true` (was false), `snapkv_keep_ratio: 0.6`, `snapkv_recent_window: 32` - Fixed 12 pre-existing test errors (spurious `.await` on sync constructors) - 17 SnapKV/prefix tests pass Total: 918 lib tests pass across rtx-tensor, rtx-flash-attention, rtx-transformers, rtx-inference. Zero new failures. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-26 19:56:39 +00:00
Omar SobhandClaude Sonnet 4.6	b60caf042c	fix(gaps): G7/CI — ONNX constant codegen, DLPack exhaustiveness, 3 new CI jobs ... G7 (Low): - rtx-onnx-codegen: replace todo!("Constant tensor") with real inline constant handling; supports Floats+Ints shape attributes, Float scalar, and compile_error! for unknown cases - rtx-bindings/dlpack: fix non-exhaustive match arms for Device::Cpu (no #[cfg] gate needed) and new DType variants (FP8E4M3/E5M2, MX formats → OpaqueHandle); both features compile clean CI (Sprint 10): Add 3 missing jobs to .gitea/workflows/ci.yml - python-bindings: maturin develop --features python on ubuntu + macos - wasm-build: cargo build --target wasm32-unknown-unknown + <5MB size check - distributed-tests: cargo test -p rtx-distributed - ci-success gate now requires all 8 jobs to pass Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-26 16:10:22 +00:00
Omar Sobh	d05e28a449	chore (#19) ... Co-authored-by: Omar Sobh <[email protected]> Co-committed-by: Omar Sobh <[email protected]>	2026-05-16 04:46:41 +00:00
Omar Sobh	ff81df62ce	chore: commit local changes before node reformat	2026-05-10 14:11:34 -07:00
osobhandClaude Opus 4.7	a5cedfb46a	rtx-csm: emotional_speech_guide — CREMA-D vs RAVDESS firdhokk verdict ... 8-gen bench (4 emotions × 2 corpora) at seed=42 against firdhokk Whisper-LV3: target RAVDESS CREMA-D happy happy (0.999) ✓ happy (0.999) ✓ angry neutral (0.92) sad (0.99) fearful happy (0.998) fearful (0.984) ✓ sad angry (0.99) fearful (0.99) CREMA-D 2/4 vs RAVDESS 1/4. Larger / more naturalistic corpus produces more class-pure fearful direction. Neither corpus solves angry or sad — recipe shifts into 'vague expressivity' rather than class-specific corners. Practical: prefer CREMA-D when available; A/B both per emotion if class precision matters. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>	2026-04-30 00:01:02 -07:00
Omar Sobh	16161bb9df	deps: align all 56 per-crate Cargo.toml files to thiserror v2 ... 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
Omar Sobh	a88d254518	rust-scan: edition 2024 clippy clean, workspace lint fixes 2026-04-25	2026-04-25 22:25:49 -07:00
osobhandClaude Opus 4.6	5fe8c67b04	Fix rtx-serving-api binary: create main.rs entry point ... The binary path pointed to non-existent src/bin/server.rs. Created proper src/main.rs with tokio async entry point that: - Initializes tracing - Loads config from RTX_HOST/RTX_PORT/RTX_TIMEOUT env vars - Instantiates ServingServer and calls serve() Fixed Cargo.toml: path = "src/bin/server.rs" → path = "src/main.rs" Validated: binary builds, starts, health endpoint returns healthy, inference endpoint returns mock completions. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>	2026-04-12 20:16:38 -07:00
osobhandClaude Opus 4.6	02d382d5f6	style: apply rustfmt across all crates and demos ... 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
osobh	d52d359f52	Fix 3 compile errors: rtx-metal (Linux cfg), rtx-onnx (ort API), rtx-fusion (edition) ... - rtx-metal: fix MetalError import in sparse/conversion.rs non-macOS stub - rtx-onnx: update session.rs and tensor_bridge.rs for ort 2.x API changes - rtx-fusion: fix Cargo.toml package name - rtx-hub: fix discovery.rs type mismatch - Full workspace (80+ crates) now compiles clean on Linux	2026-03-15 17:33:54 -07:00
redclawsystems	4d88dc0584	Initial commit	2026-03-04 00:08:42 +00:00