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loopyt cbb28a8d65 feat(h20): make the pure-torch kernel references usable without TileLang 
The bring-up plan for a card that cannot build TileLang is to run the model on
`tile_kernels.torch` -- pure-torch implementations of nearly every kernel the forward pass
calls -- and replace the hot ones with Triton afterwards. That plan was blocked at the
import: `import tile_kernels.torch` dragged in the whole compiled stack.

Three eager imports were responsible, and each is now lazy or optional:

* the top-level `__init__` re-exported every compiled kernel; it resolves them on access
* `quant/__init__` did the same for 13 modules, and `import quant.common` runs it first
* `quant/common.py` mixed host-side config dataclasses with `@T.macro` fragments; the
  dataclasses now import without TileLang, the macros are behind a guard, and its three
  dtype-handle members resolve TileLang when called rather than when imported

`config.py` also only needed TileLang for three constants, now literals.

One thing that looks like it should work and does not: a module-wide
`from __future__ import annotations`. It also stringifies the annotations inside `@T.macro`
bodies, which TileLang's macro parser evaluates itself, and it fails there. Only the four
host-side annotations are quoted, individually.

The patches are idempotent and reversible, and `verify_tile_kernels_patch.py` proves the
surface is unchanged where TileLang is installed -- the kernel modules' wildcard imports of
`quant.common` still see the macros, `from pkg import sub` still works, config knobs still
take effect.

The other half of the question, whether the references are numerically trustworthy, is
answered by `compare_with_cuda.py`: on the recipe the engine actually uses on Hopper,
per_token_cast, cast_back, swiglu_forward_and_per_token_cast and engram_hash are bitwise
identical, and apply_rotary differs by one bf16 ULP on one element in 65536. The quantisation
chain being bitwise equal is what makes a torch-first bring-up safe rather than approximate;
a cast that were merely close would change tokens downstream.

Not yet done, and recorded in the doc: MHC and the MoE gate still need their own comparison
before anyone assumes they match, and the routing layer that points deepinfer's 25 call sites
at these references does not exist yet.
2026-10-22 16:08:46 +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(h20): port the four MTP kernels, so speculative decoding no longer needs TileLang 2026-10-22 14:16:33 +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): make the pure-torch kernel references usable without TileLang 2026-10-22 16:08:46 +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 test(sm90): decode FP4 by table lookup in the quant check 2026-10-21 17:15:16 +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 docs(h20): the missing 16 kernels are the small half of the TileLang problem 2026-10-22 15:28:00 +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}},
}