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loopyt c6a65fe6ad feat(h20): FlagGems' Triton sparse MLA matches the TileLang kernel at V4.1's shape 
The remaining blocker for a TileLang-free path was sparse MLA: `h20_ops/sparse_mla_sm90.py` is a
231-line TileLang program with no Triton counterpart in this repo. FlagGems already has one, and it
turns out to be drop-in.

`flagos-ai/FlagGems` -> `src/flag_gems/fused/flashmla_sparse.py` -> `flash_mla_sparse_fwd`. Pure
Triton. At V4.1's real dims (h_q=64, h_kv=1, d_qk=512, topk=512) it compiles and runs on H20, and
on 7 shapes it agrees with the TileLang kernel within 2 bf16 ULP -- including `topk_length` and
`attn_sink`, the two optional arguments.

The pairing is closer than expected, which is why it needed no adaptation:

    prefill_attn_varlen_sparse(query, kv, index_topk, softmax_scale, attn_sink, index_length)
    flash_mla_sparse_fwd(q,         kv, indices,    sm_scale,     attn_sink, topk_length)

Causality matches too, and for the same reason. `attn_sm90.py` passes `is_causal=False` because the
indexer only scores positions before each query, so the indices already carry causality -- bounding
on the row index instead collapses the layer to NaN, which already happened once and is documented
at that call site. The FlagGems kernel has no causal parameter either.

Recorded as *not* the answer: `DSA/sparse_mla.py` in the same repo. It hardcodes causal and needs
the V3.2 576-wide split layout; V4.1 folds RoPE inside a single 512-wide latent, so TD = 0 and it
fails to compile on `tl.arange(0, 0)`. Worth writing down because the name looks like the better
match.

What remains, all much smaller than writing a kernel: wire it in behind the existing
`patch_imports` hook; test the decode path separately (it gathers to bf16 with identity indices, a
different call shape than the 7 cases); and convert the return, since the kernel gives
(out, max_logits, lse) with lse in natural log where DeepInfer expects (out, lse) with lse in log2
-- a 1.4427x scale error if dropped.
2026-10-25 08:52:03 +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 fix(h20): let the reference backend sync, and stop the reference raising under the guard 2026-10-24 19:10:49 +08:00
doc update Q&A documentation 2026-10-20 17:06:57 +08:00
eval initial commit 2026-10-20 12:09:42 +08:00
examples initial commit 2026-10-20 12:09:42 +08:00
scripts feat(h20): FlagGems' Triton sparse MLA matches the TileLang kernel at V4.1's shape 2026-10-25 08:52:03 +08:00
tests initial commit 2026-10-20 12:09:42 +08:00
vendor initial commit 2026-10-20 12:09:42 +08:00
.clang-format initial commit 2026-10-20 12:09:42 +08:00
.gitignore feat(h20): route tile_kernels calls through a compat layer, so the model runs without TileLang 2026-10-22 18:10:14 +08:00
.gitmodules initial commit 2026-10-20 12:09:42 +08:00
build.sh initial commit 2026-10-20 12:09:42 +08:00
CONTRIBUTING.md initial commit 2026-10-20 12:09:42 +08:00
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 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
LICENSE initial commit 2026-10-20 12:09:42 +08:00
Makefile initial commit 2026-10-20 12:09:42 +08:00
pyproject.toml test(h20): compare two hosts' Triton builds, not just two backends on one host 2026-10-22 10:53:36 +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
requirements-dev.txt initial commit 2026-10-20 12:09:42 +08:00
requirements-nvidia.txt initial commit 2026-10-20 12:09:42 +08:00
requirements.txt initial commit 2026-10-20 12:09:42 +08:00
setup.py initial commit 2026-10-20 12:09:42 +08:00
TODO.md feat(h20): FlagGems' Triton sparse MLA matches the TileLang kernel at V4.1's shape 2026-10-25 08:52:03 +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}},
}