Its periodic load-statistics all-gather spans the EP group; the driver waited
in it forever (allgather seq=10 timeout after the warmup request at cp=64).
Also bench_neuron.py --cp.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Followers claim core cp_rank (DEEPINFER_NEURON_LOCAL_RANK at spawn) and get
the graph's host metadata through shm (no in-graph broadcast on Neuron),
including the prompt-as-decode bucket and the context limit; they skip the
sampler. max_num_prefilling_tokens is not divided by cp on Neuron (prompt
chunks run replicated).
- DEEPINFER_NEURON_NUM_KV_BLOCKS: the fixed KV pool as an env var, so spawned
CP followers take it too.
- static MoE: when the EP group is the CP group the tokens are already
replicated: no all-gather, all-reduce instead of reduce-scatter (64x fewer
expert rows a rank at cp=ep=64).
- run_deepinfer_tiny.py --cp. Host tiny: cp=2 and cp=4 match the cp=1 tokens
(short 8/9 at a near tie, long 9/9).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
DEEPINFER_NEURON_CTX_BUCKETS: the model runner sets the step's context limit
to the smallest bucket that holds every sequence, and the dense kv_ca_index /
indexer logits are sized to it instead of max_model_len (one more set of
attention graphs a bucket). Prefill cost scaled with the limit: a 128-token
chunk ~9 s at 16384 against ~5.3 s at 4352. Host tiny check: same tokens as
without buckets up to near ties.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The op census of a decode step (DEEPINFER_NEURON_REGION_TIMING) found 85 eager
aten ops in the forward outside the region graphs but 1905 in sampling (the
top-p/top-k bisection and the int64 philox draw), ~0.6 s of NEFF launches.
- The log-softmax / top-p / top-k part runs as one compiled graph: 1.4 ms
instead of 46.7 ms on one core for the [1, 4096] force_topk row, logprobs
within 1e-9.
- Greedy rows (temperature clamped to SAMPLING_EPS) have one-hot weights, so
the two-level draw returns their argmax; an all-greedy batch takes the argmax
from the graph and skips the draw. The philox draw stays eager for sampled
rows: neuronx-cc graphs reject 64-bit constants outside the int32 range
(NCC_ESFH001), an int32 philox is #3.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
torch.remainder ran ~140 ms a call eagerly on the device and was 554 of the
640 ms a decode step spent sampling. a - (a // b) * b gives the same result for
the non-negative operands (checked on 100k random values).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- PrepareArgs.prompt_as_decode: decode-layout metadata for a prompt chunk;
input_ids are the prompt's own tokens instead of last token + MTP drafts.
- DEEPINFER_NEURON_PREFILL_BUCKET adds one graph size; the model runner pads
a prompt chunk to it with the decode placeholders (every EP rank takes the
same bucket, so the static collectives match). Sampling still sees a prompt
step.
- nki_attention.paged_mqa_logits: one gather + one kernel call for a chunk
(one sequence) instead of a T-way loop.
- bf16 linear for the LM head eagerly on a NeuronCore (b.float() was a
2.6 GiB copy a call); DEEPINFER_NEURON_REGION_TIMING; --num_prompts.
Host mode, tiny V4.1, chunk 128: same tokens as eager prefill (short 9/9,
long 715 tokens in 6 chunks 8/9 against golden).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- NeuronGraphRunner: the CudaGraphRunner contract over torch.compile(dynamic=False), one graph per
batch size (DEEPINFER_ENFORCE_EAGER=0, backend DEEPINFER_NEURON_COMPILE_BACKEND). Layer cache views are
re-sliced from their root allocations inside the graph, so AOT never regenerates aliased mutated
inputs with as_strided.
- Decode MoE with fixed shapes: packed all-gather, dense local experts, fp32 reduce-scatter
(neuron_ops/static_moe.py, deep_gemm.m_grouped_fp8_fp4_gemm_nt_shared_a).
- Graph-break and compile fixes: hide torch.cuda during compile, torch.device equality for FX codegen,
hash-based force_random routing, Engram hash/fetch on the host (exact int64), env vars resolved before
tracing, Movable annotation, no .item()/nonzero/as_strided writes on the decode path, KV stores route
padding slots to a scratch block kept past the pool, native-dtype gather under compile.
- fp8: trn2 reads float8_e4m3fn as IEEE e4m3 (max 240). Activations/KV quantize to 224
(DEEPINFER_NEURON_FP8_MAX), checkpoint fp8 weights are rescaled at load (bytes / 2, ue8m0 + 1), and
neuronx-cc gets --experimental-unsafe-fp8e4m3fn-as-fp8e4m3 via --internal-hlo2tensorizer-options.
The uint8-carrier backend (neuron_ops/neuron_compile.py, "deepinfer_neuron") stays as a fallback.
- Tests: tests/neuron/compile; run_all.sh runs kernel unit tests at the e4m3fn range (448) and the
end-to-end stages at 224. Docs: vllm-neuron PR #71 analysis and compile notes.
Host: tiny model 9/9 vs golden (all prompts) eager, compiled path (aot_eager) matches eager; CPU suite green.
Device: the whole decode graph still crashes neuronx-cc (NCC_IINL902, being bisected).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
