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loopyt 39b086d781 feat(h20): route tile_kernels calls through a compat layer, so the model runs without TileLang 
The references were importable and numerically validated, but nothing pointed the model at them:
the forward pass still called the compiled kernels, which raise ModuleNotFoundError on a host
without TileLang. This adds the routing layer and swaps the call sites.

`deepinfer/tk_compat/` exposes the *compiled* signatures -- out-buffers, in-place conventions,
argument order -- because that is what the 25 call sites are written against, and adapts to the
reference's shape internally. Selection is `DEEPINFER_KERNELS=auto|cuda|ref`; `auto` follows
whether TileLang imports.

The adapters are not pass-throughs. Each difference was found by running both backends:

* `per_token_cast` takes `num_per_channels=n`, the reference takes `block_size=(1, n)`.
* `apply_rotary` takes a concatenated cos|sin cache and rotates in place returning None; the
  reference takes cos and sin separately and returns a new tensor. The adapter splits, calls, and
  copies back, because the call site relies on the in-place contract.
* `engram_gate` takes the pre-multiplied weight and `(eps, clamp_value)`; the reference needs the
  two weights separately and `(clamp_value, eps)`. Since a product cannot be un-multiplied,
  `fused_weight` returns the pair on the reference path and the gate unpacks it.
* `mhc_pre_big_fuse_fwd` has no single reference: its five steps are composed from the reference's
  own components.
* `mhc_reduce_partials_and_rmsnorm_fwd` likewise; rebuilt from the reference's formula.

`moe_topk_gate(force_random=...)` raises rather than silently ignoring the flag. The reference has
no equivalent, and dropping it would route tokens differently while looking healthy.

Verification, all on the real call signatures: 18 cases compared between `cuda` and `ref` modes,
`ALL_MATCH`, 12 bitwise identical including every quantised byte and every routing index; plus a
smoke run that executes all 23 APIs with TileLang blocked, `ALL_CALLS_OK`.

End to end still has not completed, and the reason is not this change: six GPUs on the host are
held by other containers (82 GiB of 139 each), so the 8-way EP run OOMs in allocate_userbuffer and
the surviving ranks wait at a barrier. Recorded in the doc along with the command to rerun it.
2026-10-22 18:10:14 +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): route tile_kernels calls through a compat layer, so the model runs without TileLang 2026-10-22 18:10:14 +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 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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requirements-ascend.txt initial commit 2026-10-20 12:09:42 +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}},
}