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loopyt 5e2838aa5e feat(triton): reproduce cuRAND's Philox stream, so the sampler can be ported 
The last blocker for `fused_sample` was the random stream: `T.rng_init`/`T.rng_rand` lower to
`curand_init`/`curand` with `curandStatePhilox4_32_10_t`, and the seeding is not documented. Its
mixing function is public but `curand_init` folds the seed, the subsequence and the offset into the
counter in a way that has to be measured rather than derived.

Measured (`scripts/h20_rng_dump.py` dumps the real stream), the layout is:

    key = (seed & 0xFFFFFFFF, seed >> 32)
    ctr = (offset & 0xFFFFFFFF, offset >> 32, subsequence & 0xFFFFFFFF, subsequence >> 32)

with draws taking the block's four words in order before `ctr.x` advances. The subsequence sits in
the third and fourth counter words, not the first -- a cuRAND subsequence spans 2^64 blocks. That
detail is invisible at subsequence 0 and wrong everywhere else, which is why the check covers both.

`scripts/h20_philox_check.py` compares our kernel against the dumped reference over five seeds
(including 2^64-1) and four subsequences (0, 1, 2048, 4097 -- the last two are the actual
`sample_positions` from a run): 20/20 streams bit-identical.

One trap worth noting: the multipliers and the second Weyl constant all exceed int32, so referencing
them as Python ints inside the kernel silently wraps them negative. They are materialised as uint32
tiles instead.

This does not yet change any output -- `fused_sample` is still the TileLang kernel. It removes the
reason it had to be, which is the point.
2026-10-22 08:37:08 +08:00
csrc initial commit 2026-10-20 12:09:42 +08:00
deep_cluster style(sm90): satisfy ruff and add the missing type annotations 2026-10-21 17:09:08 +08:00
deepinfer feat(triton): reproduce cuRAND's Philox stream, so the sampler can be ported 2026-10-22 08:37:08 +08:00
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eval initial commit 2026-10-20 12:09:42 +08:00
examples initial commit 2026-10-20 12:09:42 +08:00
scripts feat(triton): reproduce cuRAND's Philox stream, so the sampler can be ported 2026-10-22 08:37:08 +08:00
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H20-DEBUG-PLAN.md fix(debug): move debug_dump out of h20_ops so other platforms can import it 2026-10-21 22:37:49 +08:00
H20-DRIFT-ANALYSIS.md docs: record that plain-text prompting answers correctly 2026-10-21 22:50:57 +08:00
H20-PORTING-JOURNAL.md docs: record the two-branch split and the upstream PR link 2026-10-21 17:16:46 +08:00
H20-SM90-FINDINGS.md feat: add SM90 (Hopper) support for DeepSeek-V4.1 inference 2026-10-21 15:18:56 +08:00
H20-TRITON-PLAN.md docs: state precisely what end-to-end parity covers 2026-10-22 08:10:20 +08:00
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pyproject.toml feat(ops): add a selectable Triton backend, and measure the real kernel surface 2026-10-22 00:04:27 +08:00
README.md initial commit 2026-10-20 12:09:42 +08:00
requirements-ascend.txt initial commit 2026-10-20 12:09:42 +08:00
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setup.py initial commit 2026-10-20 12:09:42 +08:00

README.md

DeepInfer

DeepInfer is the high-performance inference framework that powers DeepSeek's large-scale production services. This open-source release provides offline inference with the core features needed for serving at scale.

Supported models:

  • DeepSeek-V4.1-Flash

Supported features:

  • Optimized kernels, model and sampler runtime
  • Parallelism: Attention DP / balanced CP, MoE EP + EPLB
  • DSpark and confidence-scheduled verification
  • SWA bounded replay
  • Asynchronous scheduling (zero-overhead batch scheduler)
  • Batch-invariant inference
  • Deterministic sampling
  • Structured outputs
Feature details and configuration
  • Optimized kernels: FlashMLA, DeepGEMM, DeepEP, TileKernels, DeepSelect, and custom TileLang kernels.
  • DSpark and confidence-scheduled verification: accelerate decoding with draft tokens and use confidence estimates to schedule verification. Set DEEPINFER_ENABLE_MTP_VARLEN=1; DEEPINFER_USE_NEXTN_DECODE controls the maximum draft length (default: 5).
  • Decoder SWA bounded replay: reduce decoder prefill work by replaying only a bounded suffix. Set DEEPINFER_YOCO_PREFILL_TRUNCATION_MODE=compact and DEEPINFER_YOCO_PREFILL_KEEP_TOKENS=140 for the default V4.1 settings; the retained suffix must cover the SWA window plus the maximum backtracking length.
  • Asynchronous scheduling: overlap CPU batch scheduling with device execution. Enabled by default.
  • Batch-invariant inference: keep model results identical across different batch compositions. Enable with DEEPINFER_BATCH_INVARIANT=1 (NVIDIA only).
  • Deterministic sampling: produce identical outputs for identical input tokens and sampling parameters. Set DEEPINFER_BATCH_INVARIANT=1 DEEPINFER_ENABLE_MTP_VARLEN=0 and pass a fixed seq_seed to SamplingParams (NVIDIA only).
  • Structured outputs: constrain generation to a regex, JSON schema, or EBNF grammar. Pass so_args={"schema_type": ..., "schema": ...} to SamplingParams; see examples.

Supported hardware:

  • NVIDIA SM100 GPUs
  • Huawei Ascend 950 NPUs

Required for a complete inference service (not included in this release):

  • A KV cache system
  • VE/Prefill/Decode disaggregation (most required engine functionality is already implemented)
  • Load balancing across data-parallel replicas for both prefill and decode

Getting started

Prerequisites

  • Linux
  • GCC >= 13.0
  • Python 3.12
  • CUDA 13 (NVIDIA only)
  • CANN 9.2 (Ascend only)

Build from source

Before building DeepInfer, please create and activate an empty Python 3.12 environment.

# Use `ascend` instead for the Ascend platform
# Keep it exported in the environment where you run DeepInfer
export DEEPINFER_ACCELERATOR=nvidia
./build.sh

Offline inference

The default checkpoint, deepseek-ai/DeepSeek-V4.1-Flash-1017, is downloaded into the Hugging Face cache on the first run; set DEEPINFER_HF_MODEL_DIR=/path/to/DeepSeek-V4.1-Flash-1017 to run from a local copy instead.

python examples/offline_inference.py

See examples for details.

Performance benchmarks

Prefill Decode
Prefill: non-cached input throughput vs. latency Decode: output throughput vs. per-user speed

Measured on 4 SM100 GPUs for prefill and 64 for decode — each within one scale-up domain. To reproduce the benchmarks, see run_sweep.sh.

Evaluation benchmarks

See eval/README.md.

License

DeepInfer is released under the Apache License 2.0. See LICENSE for details.

Acknowledgments

DeepInfer was originally built primarily on vLLM, with additional inspiration from SGLang and TensorRT-LLM. We thank their developers.

Citation

@misc{deepinfer2026,
  title = {DeepInfer: High-performance, large-scale inference framework for DeepSeek models},
  author = {DeepSeek-AI},
  year = {2026},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/deepseek-ai/DeepInfer}},
}