collapse in torch is bit-exact with the hc_collapse kernel and a faster graph (512 rows: 0.15 vs 1.73 ms
standalone). post_torch (the kernel's operation order) is bit-equal standalone but rounds differently fused
in the layer graph, so it is opt-in. Tiny golden 9/9 with collapse. bind_cpu receives the physical core id;
_neuron_bind_cpus indexed CUDA_VISIBLE_DEVICES with it again (broke runs on cores other than 0..n).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Decode runs eagerly and compiles per-layer regions (attention half, MoE) with stage boundaries
(neuron_ops/regions.py, DEEPINFER_NEURON_GRAPH_MODE=regions); layer KV views are sliced from the
roots inside the graph (_LazyKVCache).
- vllm-neuron#71 NKI kernels (Apache-2.0, vendored under neuron_ops/nki/ with attribution), called via
torch_neuronx.nki_hop.wrap_nki: blockquant (fp8/fp4 GEMMs incl. grouped einsum, MoE and shared
expert on bf16 activations), sparse_attn / index_score / compress_pool (nki_attention.py),
hc_mixes / hc_expand / hc_collapse / hc_attn_out / rmsnorm / swiglu (nki_hc.py, nki_norm.py).
- Compile-safe formulations: row-major scales, no dtype bitcasts of graph inputs (uint8 weight
tensors), whole-row KV stores, byte assembly in int32 narrowed once at the store, arithmetic int16
scale packing, plain fma/rsqrt and bf16 F.linear under compile; default backend deepinfer_neuron
(torch fp8 carried as uint8, avoiding NCC_EOCP001 with the kernels' OCP fp8).
- Host: tiny model 9/9 vs golden on all prompts; device: kernel tests pass (13 attention, 88 hc/norm);
region decode graph compiles except the attention projection stage (being reduced).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
