11 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	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entire orphaned curriculum/ split (mod.rs holds the real inline implementation), non-_simple graph variants, superseded simmim/jepa_integration files, layers/{sliding_window_attention, positional_encoding,ssm_state_cache_original}, lib_full/lib_minimal/ error_full/error_minimal,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
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	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
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	311eb23dbd	feat(perf): GPU perf batch 3 — W4A16 AWQ matmul, FSDP2 hooks, RMSNorm+SwiGLU fused kernel ... W4A16 AWQ on-the-fly dequantize+GEMM (rtx-compress) - `w4a16_matmul.rs`: `AWQQuantizedWeightExt` trait + `matmul_cpu()` — group-aligned inner loop, f64 accumulation, low-nibble-first INT4 unpacking matching mx_kernels.cu - `cuda_kernels/w4a16_gemm.cu`: `w4a16_dequant_gemm` kernel, one thread per (batch, out_col), 8-INT4-per-iteration inner loop with `__ldg()` cache hints, BF16 scale decode, f32 accumulate - `quantization/mod.rs`: exports `w4a16_matmul_cpu`, `AWQQuantizedWeightExt` - Fixed 2 pre-existing pruning compile errors - 12 tests: nibble unpack, identity weights, shape, vs-dequant (tol=1e-3), batch=1, zeros FSDP2 forward/backward hooks (rtx-distributed) - `fsdp2.rs`: `update_local_shard()` on `Fsdp2ShardedParam`; sync `all_gather()` + `reduce_scatter_gradient()` using `ProcessGroup::{all_gather,reduce_scatter}` - `pre_forward_hook()` — all-gathers every param (or copies shard in single-process) - `post_backward_hook()` — reduce-scatters gradients, zero_grad, re-shards cache - `step(optimizer_fn)` — applies optimizer closure to each local shard - `make_fsdp2_module()` top-level factory; `Fsdp2MemoryStats` gains 5 new fields incl. `memory_reduction_ratio ≈ world_size` - 6 new tests (end-to-end training step included); total 444 pass RMSNorm+SwiGLU fused CUDA kernel (rtx-fusion) - `cuda/rms_norm_swiglu_fused.cu`: `rms_norm_kernel` + `rms_norm_swiglu_fused`; shared-mem warp reduction (block_x floats), launch: grid=(batch,1,1), block=(min(hidden,1024),1,1) - `cuda_kernels/rms_norm_fused.rs`: CPU reference `rms_norm_cpu`/`swiglu_cpu`/ `rms_norm_swiglu_cpu`; `#[cfg(feature="cuda")] RmsNormFusedKernel` NVRTC wrapper - `codegen/cubecl.rs`: replaced RmsNorm comment stub with cfg-gated NVRTC dispatch - `backend.rs` + `tensor.rs`: added 15 missing `Backend` trait impls (sin/cos/relu/conv2d/…) that blocked test compilation - `Cargo.toml`: added rtx-fusion to workspace members - 8 new tests (PyTorch-formula verified: x=[1,2,3,4] → [0.365, 0.730, 1.095, 1.461]); total 103 pass Test results: 12 + 444 + 103 = 559 tests, 0 failures Co-Authored-By: Claude Sonnet 4.6 <[email protected]>	2026-06-27 00:18:20 +00:00
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	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