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Problem fixes - #16
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Willy-Chan wants to merge 5 commits into
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Willy-Chan
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Jul 27, 2026
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- Triton integration - allowing for models to write triton + UVA.
- Expanded credits
- Fable 5 fixes - TODO a deep review of the model's critiques and changes.
Every problem keeps its slot; references and input generators are fixed so each one tests a genuinely different multi-GPU kernel skill: - 19/20 (blocked fp8 quant/dequant): references were already Triton kernels, contradicting the PyTorch+NCCL-reference premise; rewritten in pure PyTorch. 20's generator now produces genuinely quantized inputs (positive per-block absmax scales) instead of randn scales. - 26 (moe_token_preprocess): split metadata stays on-device instead of being returned via .tolist()/CPU copies. - 30 (moe_epgroupgemm_lora_backward): was three bare all_reduces (problem 1 x3, misnamed); now computes shared LoRA adapter grads via ragged grouped GEMMs over local experts + EP all-reduce, without mutating inputs. - 34 (ulysses all-gather primitive): was byte-identical to problem 2; now gathers along dim 1 (Ulysses sequence dim), forcing gather+reorder fusion. - 41/42: header comments making the ZeRO-1 vs ZeRO-2 comm-pattern pair explicit. - 44/52/53 (quantized comms): previously quantize->dequantize locally then bf16 collective, so the named technique was untestable; now int8/fp8 payloads and scales actually cross the wire (two-phase quantized all-reduce, fp8 all-to-all reduce-scatter, fp8 all-gather). - 49 (moe_ep_balanced): was byte-identical code to 31/50; now the static capacity-based balanced dispatch (fixed equal splits, zero host-side size exchange, drop/pad to capacity). - 50 (moe_ep_wide): now hosts genuinely distinct per-local-expert weights (stacked tensors) with a ragged grouped-GEMM expert stage. - 63 (deepmd kalman): fp32 instead of f64, no all_gather_object, no in-place mutation of inputs (which drifted across timed iterations), weight exchange via one fixed-size all_gather_into_tensor. - 64 (gnn_neighbor_sampling): reference was a per-node host loop with .item() syncs and np.unique on CPU over a perfectly regular synthetic graph; now fully vectorized on-device (deterministic first-k selection) over an irregular random graph with random per-rank seed frontiers. - 78 (tile-parallel VAE decode): all_gather_object shape exchange replaced with tensor-based exchange, payload duplication (repeat(world_size) + all_to_all) replaced with a padded all-gather, Python blend loops vectorized with broadcast ramps. - 82: trailing-whitespace cleanup. - 83/84 (chunked log-prob top-k): longer sequences and larger vocab so the chunked pipeline (the point of these variants vs 82) processes many chunks. - 24 (load_balancing_loss_fn): generator now feeds multi-layer gate-logit tuples plus a real attention mask so the masked normalization path is exercised and there is meaningful device compute ahead of the scalar all-reduce. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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