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DeepInfer/examples/neuron
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whn09 3629d113fb Neuron: prompt chunks through the decode region graphs (issue #26) 
- 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>
2026-10-23 03:19:56 +08:00
..
golden Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
check_tiny_checkpoint.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
compare_golden.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
device_diff.py Neuron: tiny model on a NeuronCore matches the reference (issues #1, #2, #4) 2026-10-21 13:22:59 +08:00
make_tiny_checkpoint.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
offline_inference_neuron.py Neuron: prompt chunks through the decode region graphs (issue #26) 2026-10-23 03:19:56 +08:00
probe_layer0.py Neuron: tiny model on a NeuronCore matches the reference (issues #1, #2, #4) 2026-10-21 13:22:59 +08:00
README.md Neuron flash_mla: round attention P to bf16 like the CUDA kernels 2026-10-20 20:48:36 +08:00
ref_kernels_torch.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
reference_golden.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00
run_deepinfer_tiny.py Neuron: EP > 1 with real neuron collectives (issue #6) 2026-10-21 11:46:59 +08:00
test_ref_kernels_torch.py Neuron basic mode — part 3: end-to-end check vs the official reference 2026-10-20 20:20:02 +08:00

README.md

DeepInfer on AWS Trainium — end-to-end check with a tiny checkpoint

Design and conventions of the port: deepinfer/neuron_ops/NEURON_PORT.md.

The ground truth is DeepSeek's official reference implementation (inference/ in the deepseek-ai/DeepSeek-V4.1-Flash-1017 HF repo) running on CPU, with its TileLang kernels replaced by the pure-torch versions in ref_kernels_torch.py.

Script Purpose
make_tiny_checkpoint.py Random tiny V4.1 checkpoint in the real HF layout (FP8 e4m3 + ue8m0 32x32 scales, FP4 e2m1 experts with 1x32 scales, engram tables)
check_tiny_checkpoint.py DeepInfer loader coverage / shape / dtype checks on the meta device
ref_kernels_torch.py, test_ref_kernels_torch.py Torch replacements for the reference kernel.py
reference_golden.py Reference prefill + greedy decode; saves logits, tokens and per-layer hidden states
run_deepinfer_tiny.py Same prompts through the DeepInfer engine (LLM offline API), teacher-forced
compare_golden.py Candidate vs golden: argmax match, logits rel / cos / KL, per-layer hidden error

Tiny config: 10 layers + 3 nextn, hidden 1024, 8 heads x 512 (MLA, rope 64), 16 experts / top-4, indexer 4 x 128 with top-64, engram on layers 1 and 4, real tokenizer (vocab 129280).

export DEEPINFER_ACCELERATOR=neuron DEEPINFER_NEURON_DEVICE=cpu   # or =neuron for the NeuronCore
python examples/neuron/run_deepinfer_tiny.py --threads 32 --hidden --compare

Results so far (CPU mode, basic mode: eager, no DSpark/MTP, EP=DP=CP=1)

Teacher-forced, prefill + 8 decode steps per prompt; "floor" is the reference compared with itself under a different CPU thread count (FP8 activation quantization amplifies sub-ulp differences).

prompt (tokens) argmax match max logits rel (floor) min cos (floor)
short (11) 9/9 0.054 (0.032) 0.9985 (0.9995)
medium (172, wraps the 128 SWA window) 8/9 * 0.041 (0.039) 0.9992 (0.9993)
long (715, candidate-block filter active) 9/9 0.059 (0.060) 0.9983 (0.9982)

* medium step 6 has a top-1/top-2 margin of 0.0096; the reference flips it against itself too.

probe_layer0.py compares ~35 named ops per layer between the reference and DeepInfer. Layer 0 is bit-exact once three rounding points are aligned:

  • attention P rounded to bf16 before P@V (online softmax, 64-key blocks) — the CUDA FlashMLA kernels and the reference do this; the Neuron flash_mla shim now does too (was fp32, a port bug);
  • SwiGLU output quantized to FP8 straight from fp32 (the reference rounds to bf16 first) and
  • the MoE combine rounding the routed sum to bf16 before adding the shared expert (the reference adds in fp32) — both are DeepInfer's own GPU/Ascend behaviour, kept as is.

The remaining logits difference comes from those two, amplified by FP8 activation quantization layer by layer. Per-step / per-layer numbers before the attention fix: golden/deepinfer_cpu_vs_golden.json.