19 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	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	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	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
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
redclawsystems	4d88dc0584	Initial commit	2026-03-04 00:08:42 +00:00