coffee/DeepInfer
1
0
Fork 0
mirror of https://github.com/deepseek-ai/DeepInfer.git synced 2026-10-31 00:36:20 +08:00
Code Issues Packages Projects Releases Wiki Activity
11 Commits 2 Branches 0 Tags Apache-2.0 15 MiB
Python 89.1%
C++ 10.6%
Shell 0.3%
Go to file
loopyt 21d051c4ad debug: per-step decode dumps, and prove the flip is decode-only 
Adds `record_step` so every decode step is kept instead of just the first, a `--force_ids` mode
to `ref_next_token.py` that mirrors `generate.py`'s incremental loop, and four fixes to the dump
path without which the data was unusable:

- filenames carry `_rank<R>_pid<P>`: with data parallelism every engine is its own
  single-process group and reports RANK=0, so eight processes wrote the same file and the last
  one won;
- decode reads row 0, not the last row: the batch is padded and the padding rows hold logits for
  placeholder tokens, so the last row was a constant `top1=69, margin=0.0528`;
- only the driver rank records: a follower only joins the embedding all-gather, so its logits are
  placeholders that got recorded dozens of times and buried the real steps;
- `DEEPINFER_DUMP_TOKENS` gating plus `profiling()` now cover both profile paths.

The conclusion itself comes from a black-box four-way comparison rather than the dumps, because
per-step dumps still do not line up with the emitted token sequence (`max_tokens=7` produced 75
sampler calls). At the same 14-token context:

    reference prefill   6127 (北京)   correct
    reference decode    6127 (北京)   correct
    our prefill         6127 (北京)   correct
    our decode           666 (**)     wrong

So prefill is right and the remaining flip is introduced by decode alone. Note the reference's
own prefill and decode margins differ (3.112 vs 1.638), so the two paths are not expected to be
identical -- KV cache goes through a quantized round trip -- but they should not disagree on the
direction.

Remaining suspects are therefore narrowed to decode-only state: the KV cache round trip (P4.3)
and the indexer scored in torch versus the TileLang prefill kernel (P4.4), which have never
been diffed against each other.
2026-10-21 19:07:49 +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 debug: per-step decode dumps, and prove the flip is decode-only 2026-10-21 19:07:49 +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 debug: per-step decode dumps, and prove the flip is decode-only 2026-10-21 19:07:49 +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 debug: per-step decode dumps, and prove the flip is decode-only 2026-10-21 19:07:49 +08:00
H20-DRIFT-ANALYSIS.md debug: capture final logits, and keep the profile forward out of the dumps 2026-10-21 17:44:21 +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
LICENSE initial commit 2026-10-20 12:09:42 +08:00
Makefile initial commit 2026-10-20 12:09:42 +08:00
pyproject.toml initial commit 2026-10-20 12:09:42 +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}},
}