TorchSpec

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A PyTorch native library for training speculative decoding models

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# TorchSpec

TorchSpec is a torch-native speculative decoding training framework. We introduce a disaggregated way of training speculative decoding draft models where inference and training are fully decoupled and stream hidden states directly from inference engine groups to distributed training workers via [Mooncake](https://github.com/kvcache-ai/Mooncake) store, allowing each side to scale independently.

## Adoption

TorchSpec has been adopted by production inference platforms and the vLLM ecosystem:

- [DigitalOcean](https://www.digitalocean.com/blog/how-we-built-fastest-deepseek-minimax-qwen-on-blackwell-ultra) used TorchSpec to train an EAGLE3 draft model for MiniMax-M2.5 on DigitalOcean Serverless Inference.
- [vLLM](https://vllm.ai/blog/2026-05-11-vllm-tops-artificial-analysis) used TorchSpec and vLLM to train the custom EAGLE3 draft model featured in its Artificial Analysis leaderboard work.
- [CoreWeave](https://www.coreweave.com/blog/kimi-k2-7-code-now-available-on-serverless-inference-with-leading-benchmark-price-performance) used TorchSpec to train a DFlash speculative decoding model for Kimi K2.7 Code and contributed D-PACE support upstream.
- [fal](https://blog.fal.ai/how-we-achieved-1000-tok-s-and-16x-throughput-with-dspark-for-ideogram-v4-prompt-expander/) used TorchSpec to train a DSpark speculative decoding model for its Ideogram V4 prompt expander, reporting 16x throughput gains with DSpark.
- [Tencent Hunyuan](https://x.com/TencentHunyuan/status/2082447023626944936) open-sourced [**AngelSpec**](https://arxiv.org/abs/2607.25852), adopting TorchSpec for training and featuring it in the paper.

## 🤗 Released Models

Draft models trained with TorchSpec, available on the [LightSeek Foundation](https://huggingface.co/lightseekorg) Hugging Face organization:

- [lightseekorg/kimi-k2.5-eagle3](https://huggingface.co/lightseekorg/kimi-k2.5-eagle3)
- [lightseekorg/kimi-k2.5-eagle3-mla](https://huggingface.co/lightseekorg/kimi-k2.5-eagle3-mla)
- [lightseekorg/kimi-k2.6-eagle3](https://huggingface.co/lightseekorg/kimi-k2.6-eagle3)
- [lightseekorg/kimi-k2.6-eagle3-mla](https://huggingface.co/lightseekorg/kimi-k2.6-eagle3-mla)
- [lightseekorg/kimi-k2.6-eagle3.1-mla](https://huggingface.co/lightseekorg/kimi-k2.6-eagle3.1-mla)

Draft models trained with TorchSpec, available from other organizations:

- [Inferact/MiniMax-M3-EAGLE3](https://huggingface.co/Inferact/MiniMax-M3-EAGLE3)
- [Inferact/Kimi-K3-DSpark](https://huggingface.co/Inferact/Kimi-K3-DSpark)

## 🚀 Blogs

## Blogs and Announcements

- [EAGLE 3.1: Advancing Speculative Decoding Through Collaboration Between the EAGLE Team, vLLM, and TorchSpec](https://lightseek.org/blog/eagle-3-1.html) (May 2026)
- [TorchSpec: Speculative Decoding Training at Scale](https://pytorch.org/blog/torchspec-speculative-decoding-training-at-scale/) (PyTorch Blog, March 2026)
- [TorchSpec: Speculative Decoding Training at Scale](https://lightseek.org/blog/torchspec-speculative-decoding-training-at-scale.html) (March 2026)

## Table of Contents

- [Architecture Overview](#architecture-overview)
- [Inference Backend Support](#inference-backend-support)
- [Quick Start](#quick-start)
- [Setup](#setup)
- [Examples](#examples)
- [Training Modes](#training-modes)
- [Checkpoint Conversion](#checkpoint-conversion)
- [Metrics Reporting](#metrics-reporting)
- [Troubleshooting](#troubleshooting)

## Architecture Overview

TorchSpec is built around a disaggregated training pipeline:

- **Inference engines** generate target-model hidden states with inference engines.
- **Mooncake store** transfers tensors between inference and training without materializing them on disk.
- **Training workers** consume streamed hidden states to train speculative decoding draft models.

This separation keeps the training side focused on optimization while letting the inference side scale for hidden-state generation throughput.

## Inference Backend Support

TorchSpec streams hidden states from inference engines into training workers.

| Backend | Support Tier | Status |
|---------|--------------|--------|
| [vLLM](https://github.com/vllm-project/vllm) | First-class | Available |
| [TokenSpeed](https://github.com/lightseekorg/tokenspeed) | First-class | In progress |
| [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) | First-class | Available |
| [SGLang](https://github.com/sgl-project/sglang) | Best community effort | Available |
| [HuggingFace Transformers](https://github.com/huggingface/transformers) | Best community effort | Available |

## Quick Start

Train an Eagle3 draft model for Qwen3-8B on a single node with 4 GPUs (2 for training and 2 for inference):

```bash
./examples/qwen3-8b-single-node/run.sh
```

Override config values directly from the CLI:

```bash
./examples/qwen3-8b-single-node/run.sh training.learning_rate=5e-5 training.num_train_steps=500
```

## Setup

### Quick Setup

```bash
# Install with vLLM
./tools/build_conda.sh 1 vllm
micromamba activate torchspec

# Or install with SGLang
./tools/build_conda.sh
micromamba activate torchspec

# Or install TokenSpeed from an editable source checkout
./tools/build_conda.sh 1 tokenspeed
micromamba activate torchspec
```

To install into your current environment instead:

```bash
./tools/build_conda.sh current tokenspeed  # or 'sglang', 'vllm', or 'both'
```

The TokenSpeed backend currently requires a Python 3.12 environment because
its native kernel dependency wheels do not support Python 3.14.

Optional: install Flash Attention support:

```bash
pip install -e ".[fa]"
```

### Backend-Specific Usage

**vLLM**

```bash
./examples/qwen3-8b-single-node/run.sh configs/vllm_qwen3_8b.yaml
```

**SGLang**

```bash
./examples/qwen3-8b-single-node/run.sh
```

**TensorRT-LLM**

Run inside the TensorRT-LLM image (`docker/trtllm/v1.3.0rc18/Dockerfile`), which ships `tensorrt_llm` pre-patched for Mooncake hidden-state capture:

```bash
./examples/qwen3-8b-single-node/run.sh configs/trtllm_qwen3_8b.yaml
```

Single-node tensor parallelism only for now (multi-node TP is not yet wired up).

TorchSpec uses vLLM's **Worker Extension** mechanism to hook into the model forward pass and capture hidden states directly inside worker processes, which avoids RPC serialization overhead during extraction. For SGLang, TorchSpec applies a patch to the existing codebase to enable hidden-state extraction. For TensorRT-LLM, TorchSpec builds on its native **SaveHiddenStates** speculative mode and applies a small patch that redirects the captured aux + final hidden states to Mooncake instead of writing them to disk.

### Updating the SGLang patch

Commit your changes in the `_sglang` checkout, then regenerate `patches/sglang/<version>/sglang.patch` from the pinned base commit with `./tools/update_sglang_patch.sh`.

## Examples

| Example | Backend | Model |
|---------|---------|-------|
| [hf-quickstart](examples/hf-quickstart/) | HuggingFace | Qwen3-8B |
| [qwen3-8b-single-node](examples/qwen3-8b-single-node/) | Inference engine | Qwen3-8B |
| [kimi-k25-2node-h200](examples/kimi-k25-2node-h200/) | Inference engine | Kimi-K2.5 |
| [kimi-k25-3node-h100](examples/kimi-k25-3node-h100/) | Inference engine | Kimi-K2.5 |
| [minimax-m25-5node-h200](examples/minimax-m25-5node-h200/) | Inference engine | MiniMax-M2.5 |

See [examples/README.md](examples/README.md) for more details about each example.

## Training Modes

### Offline Replay Training

Offline replay ([Docs](docs/offline_training.md)) is a training mode that separates target-output generation from draft-model training by reading hidden states from disk. Recommended for testing and development on 1 GPU.

### Resume vs. Continual Training

Both modes use `training.load_path`, but they restore different states:

| Goal | `training.load_path` | `training.continual_training` | What gets restored |
|------|----------------------|-------------------------------|--------------------|
| Resume an interrupted run | Required | `false` (default) | Model, optimizer, LR scheduler, RNG, and step metadata |
| Start a new run from existing weights | Required | `true` | Model weights only |

Resume the same run:

```yaml
training:
  load_path: /path/to/old_run/checkpoints

output_dir: /path/to/old_run
```

Start a new run from existing weights:

```yaml
training:
  load_path: /path/to/old_run/checkpoints
  continual_training: true
  learning_rate: 1e-5
  warmup_ratio: 0.01
  num_epochs: 1

output_dir: /path/to/new_run
```

Start a new run from an existing Hugging Face checkpoint. `--input` accepts a Hugging Face Hub repo id (downloaded automatically), a local HF/safetensors directory or `.safetensors` file, or a TorchSpec DCP checkpoint dir:

```bash
python tools/convert_to_torchspec.py \
    --input org/dflash-checkpoint \
    --config torchspec/config/dspark_draft_config_qwen36_35b.json \
    --output ./outputs/dspark_init
```

Then warm-start from the generated init:

```yaml
training:
  load_path: ./outputs/dspark_init
  continual_training: true
```

Using this technique, you can also warm-start DSpark training runs from a pre-trained DFlash model for faster convergence.

## Checkpoint Conversion

Convert an FSDP checkpoint to HuggingFace format:

```bash
python tools/convert_to_hf.py --input-dir ./outputs/my_experiment/iter_0010000/
```

Vocabulary pruning, which reduces the draft model `lm_head` to a smaller token set and emits `d2t` and `t2d` mappings, can be applied either during training or at conversion time.

- **Pre-pruning**: set `draft_vocab_size` in your training config. The checkpoint already contains the pruned `lm_head` and `d2t`/`t2d` buffers, so the basic conversion command is enough.
- **Post-pruning**: train with the full vocabulary, then pass `--prune-vocab` at conversion time together with a representative dataset to compute token frequencies.

```bash
python tools/convert_to_hf.py \
    --input-dir ./outputs/my_experiment/iter_0010000/ \
    --prune-vocab \
    --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
    --draft-vocab-size 32000 \
    --tokenizer Qwen/Qwen3-8B \
    --chat-template qwen \
    --prompt-key conversations
```

Pass `--cache-dir ./cache` to reuse the tokenized dataset cache from training.

## Metrics Reporting

W&B logging is disabled by default with `report_to: none`. To enable it, set `report_to: wandb` in your config and provide your API key.

## Troubleshooting

Set `TORCHSPEC_LOG_LEVEL=DEBUG` for more verbose logging when diagnosing issues:

```bash
TORCHSPEC_LOG_LEVEL=DEBUG ./examples/qwen3-8b-single-node/run.sh
```

### Mooncake SEGFAULT

Current Mooncake version has a bug with TCP-only hosts causing a SEGFAULT error. Set `MC_STORE_MEMCPY=0` until the [upstream issue](https://github.com/kvcache-ai/Mooncake/issues/1986) is fixed.

### RDMA failure

If you get the error `... libcudart symbols not found globally. Make sure PyTorch with CUDA is installed before using TileLang`, reinstall mooncake to match your system CUDA version, i.e. if you run on CUDA 13, mooncake is probably installed for cu12 wheels, re-install it using `uv pip uninstall mooncake-transfer-engine && uv pip install mooncake-transfer-engine-cuda13`.

### Per-Rank File Logging

Set `TORCHSPEC_LOG_DIR` to an absolute path on a shared filesystem (NFS) to enable per-rank log files for every Ray actor on both training and inference:

```bash
export TORCHSPEC_LOG_DIR=/my_project/running_logs
```

This creates a structured directory with one file per actor, organized by role and node:

```text
running_logs/
  training/
    10.0.0.1/
      training_g0_rank0_20260301_080012.log
      training_g0_rank1_20260301_080012.log
    10.0.0.2/
      training_g0_rank2_20260301_080013.log
  inference/
    10.0.0.1/
      inference_g0_rank0_20260301_080014.log
    10.0.0.2/
      inference_g0_rank1_20260301_080015.log
```

The path must be absolute and writable from all nodes. If `TORCHSPEC_LOG_DIR` is unset or not writable, per-rank file logging stays disabled and Ray falls back to stdout/stderr capture.

| Issue | Reference |
|-------|-----------|
| Stuck or failing distributed runs, Ray actor errors | [docs/debugging_ray_jobs.md](docs/debugging_ray_jobs.md) |
| Ray cluster setup, actor hierarchy, placement groups | [docs/ray.md](docs/ray.md) |
| Pipeline bottlenecks, slow steps, throughput analysis | [docs/performance_metrics.md](docs/performance_metrics.md) |

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