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loopyt 8ae7365cbc feat(h20): port the four MTP kernels, so speculative decoding no longer needs TileLang 
`DEEPINFER_USE_NEXTN_DECODE` defaults to 5, so speculative decoding is on unless someone
turns it off, and these four ran on every decode step. On a host without TileLang the first
step failed; on the H20 they were the only kernels still answering from the other backend
during a normal run.

The interesting one is `remap_draft_topk`. Its TileLang form builds a vocabulary-windowed
lookup table in shared memory and reads the logprobs back out of it. Triton has no
equivalent primitive, so the table is gone and each target index is matched against the
draft indices directly: draft indices are unique per token id, so at most one candidate can
match and a max over the block is well defined. That is O(topk^2) instead of
O(vocab/pool_size * topk), which would be the wrong trade at a large `force_topk` -- but
`force_topk` defaults to 0, so this kernel is off the default path and correctness comes
first. Worth revisiting if a deployment turns it on.

The other three adapt rather than change shape:

* `get_accept_result`'s prefix scan becomes a bounded loop over the widest span in the
  block; lanes with a shorter span hold their value on the extra iterations. MTP packs at
  most num_steps+1 rows per request, so the bound stays small.
* `finalize_verification` reduces `batch_confidence_error` within the program and issues one
  atomic instead of one per row, because a scalar pointer with a vector value is not valid
  in this Triton version. Accumulating in a different order changes the last bits, so its
  cases compare with a tolerance and say why.
* `get_fused_dflash_get_input` rounds its column loop up. `padded_kv_len` is a multiple of 64
  but not necessarily of the tile width, and flooring leaves the tail of every row unwritten.

33 new A/B cases across the four, covering each constexpr variant the engine builds. The
full run is green and an end-to-end pass is token-for-token identical to the run before the
change, with the MTP fallback notices gone.
2026-10-22 14:16:33 +08:00
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deep_cluster style(sm90): satisfy ruff and add the missing type annotations 2026-10-21 17:09:08 +08:00
deepinfer feat(h20): port the four MTP kernels, so speculative decoding no longer needs TileLang 2026-10-22 14:16:33 +08:00
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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 test(h20): check what the model actually says, in a file rather than on the command line 2026-10-22 10:53:01 +08:00
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pyproject.toml test(h20): compare two hosts' Triton builds, not just two backends on one host 2026-10-22 10:53:36 +08:00
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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}},
}