4 Commits

Include renames

Author	SHA1	Message	Date
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
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
redclawsystems	4d88dc0584	Initial commit	2026-03-04 00:08:42 +00:00