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loopyt a6c3e69e67 feat(triton): port eight P1 kernels, all A/B identical to TileLang 
gather, gather_input_ids_zos, gather_engram_tokens_zos, expand_swa_index, kv_index_cat,
convert_kv_index, load_kv_index, store_mtp_info. 45 cases, every one bitwise identical to the
TileLang baseline through the shared interface function.

`scripts/h20_ops_ab.py` swaps `ops.<name>_tl` between the two backends and runs the public call
once per side, so the comparison exercises the interface layer's shape handling too, not just the
kernel. `scripts/ab_cases.py` holds the cases: real engine widths and dtypes, negative indices
for gather, -1 padding for the index kernels, page-boundary lengths, the slot-0 no-write path,
and the engram image-sentinel rule.

Five Triton semantics traps worth writing down, all documented in the plan:

- `tl.where` evaluates both arms, so `tl.where(i == 0, 0, LOAD(i - 1))` really reads `LOAD[-1]`;
  that was an illegal memory access, fixed by clamping the index instead.
- A scalar pointer cannot carry a block-shaped mask; it has to be broadcast with
  `+ tl.zeros((BLOCK,), tl.int32)` first.
- Two arms of one kernel cannot use different tile shapes, which is why `kv_index_cat` became two
  launches rather than one fused kernel with a column-block branch.
- Triton's `data_ptr()` already includes a view's storage offset, so passing `out[:, w:w+k]` and
  adding `w` again double-counts -- that showed up as 0x01010101 in the second half.
- Test-case capacity matters: `store_mtp_info` addresses `slot * nextn + step`, so the pool needs
  `(max_slot + 1) * nextn` rows; too few rows let both backends write out of bounds and disagree.
2026-10-22 00:11:45 +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): port eight P1 kernels, all A/B identical to TileLang 2026-10-22 00:11:45 +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
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.gitignore test(sm90): decode FP4 by table lookup in the quant check 2026-10-21 17:15:16 +08:00
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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 feat(triton): port eight P1 kernels, all A/B identical to TileLang 2026-10-22 00:11:45 +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 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
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

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}},
}