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fce6cef262 |
docs: JEPA roadmap — GPU resume re-upload done; remaining items need multi-GPU
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Co-Authored-By: Claude Fable 5 <[email protected]> |
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74d3db7ee7 |
docs: refresh honesty notes for rewired demos, consolidation audit, ServingTokenizer
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Co-Authored-By: Claude Fable 5 <[email protected]> |
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68481ef314 |
docs: JEPA roadmap round-2 done items; queue GPU-resume re-upload and TP/PP
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Co-Authored-By: Claude Fable 5 <[email protected]> |
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f6b2308381 |
docs: JEPA roadmap — 2026-07-10 items done, next tier queued
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Co-Authored-By: Claude Fable 5 <[email protected]> |
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fdc1432072 |
feat(jepa): full GPU-resident ViT block on CUDA
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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]> |
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5f32165184 |
chore(sweep): delete 43 orphaned source files; document SYCL/demo/duplication status
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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]>
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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]> |
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4ccf089e82 |
docs: update README + CLAUDE.md for Batches 20-26 JEPA platform completion
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- 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]> |
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60e6d05e86 |
docs: comprehensive README and CLAUDE.md refresh for 113-crate state
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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]> |
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bc88a14fa1 |
docs: add CLAUDE.md with architecture and integration reference
Co-Authored-By: Claude Sonnet 4.6 <[email protected]> |