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causal_conv1d: Causal depthwise 1D convolution with static per-shape configs and a stable fused convolution path; this is the highest-confidence officially validated variant from my set. fp8_quant: Group-128 FP8 E4M3 quantization with exact-shape dispatch across the benchmark regimes; this was my fastest locally correct quantization variant. gated_deltanet_chunk_fwd_h: Inter-chunk state recurrence with a faster multi-chunk state pass while preserving the official-safe execution path; it improved the larger official benchmark cases substantially. gated_deltanet_chunk_fwd_o: Output kernel with a vectorized fast path that remained locally correct while recovering most of the speed lost in earlier safe rewrites. gated_deltanet_recompute_w_u: WY-transform forward kernel with a merged-matmul hot path for the dominant 64x64, H=3 cases plus shape dispatch for correctness elsewhere.

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More from PyTorch Helion Hackathon

We implemented 4 GPU kernels using Helion DSL targeting NVIDIA B200 (Blackwell) GPUs: 1. causal_conv1d: Depthwise causal 1D convolution (core Mamba/Mamba-2…

Axiom

Here's how each kernel works: 1. FP8 Quantization (fp8_quant) Memory-bound, fully parallel. Takes float32 activations, groups them (64-128…

WarpDrive

using helion with NVIDIA ComputeIQ and TileIR.

Shiprail

fully autokernel generated

cob-farmers

GPU modders

GPU modders

causal_conv1d - brief summary reading, it's functionality not known and performance not known fp8_quant_py - brief summary reading, it's functionality not…

vectorblock.io