README.md

# Speculative Decoding

!!! warning
    Currently, speculative decoding in vLLM is not compatible with pipeline parallelism.

This document shows how to use [Speculative Decoding](https://x.com/karpathy/status/1697318534555336961) with vLLM.
Speculative decoding is a technique which improves inter-token latency in memory-bound LLM inference.

!!! tip
    To train your own draft models for speculative decoding, see [Speculators](speculators.md), a library for training draft models that integrates seamlessly with vLLM.

## Speculating with a draft model

The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time.

!!! warning
    In vllm v0.10.0, speculative decoding with a draft model is not supported.
    If you use the following code, you will get a `NotImplementedError`.

??? code

    ```python
    from vllm import LLM, SamplingParams

    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(
        model="facebook/opt-6.7b",
        tensor_parallel_size=1,
        speculative_config={
            "model": "facebook/opt-125m",
            "num_speculative_tokens": 5,
        },
    )
    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    ```

To perform the same with an online mode launch the server:

```bash
vllm serve facebook/opt-6.7b \
    --host 0.0.0.0 \
    --port 8000 \
    --seed 42 \
    -tp 1 \
    --gpu_memory_utilization 0.8 \
    --speculative_config '{"model": "facebook/opt-125m", "num_speculative_tokens": 5}'
```

!!! warning
    Note: Please use `--speculative_config` to set all configurations related to speculative decoding. The previous method of specifying the model through `--speculative_model` and adding related parameters (e.g., `--num_speculative_tokens`) separately has been deprecated now.

Then use a client:

??? code

    ```python
    from openai import OpenAI

    # Modify OpenAI's API key and API base to use vLLM's API server.
    openai_api_key = "EMPTY"
    openai_api_base = "http://localhost:8000/v1"

    client = OpenAI(
        # defaults to os.environ.get("OPENAI_API_KEY")
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    models = client.models.list()
    model = models.data[0].id

    # Completion API
    stream = False
    completion = client.completions.create(
        model=model,
        prompt="The future of AI is",
        echo=False,
        n=1,
        stream=stream,
    )

    print("Completion results:")
    if stream:
        for c in completion:
            print(c)
    else:
        print(completion)
    ```

## Speculating by matching n-grams in the prompt

The following code configures vLLM to use speculative decoding where proposals are generated by
matching n-grams in the prompt. For more information read [this thread.](https://x.com/joao_gante/status/1747322413006643259)

??? code

    ```python
    from vllm import LLM, SamplingParams

    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(
        model="facebook/opt-6.7b",
        tensor_parallel_size=1,
        speculative_config={
            "method": "ngram",
            "num_speculative_tokens": 5,
            "prompt_lookup_max": 4,
        },
    )
    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    ```

## Speculating using Suffix Decoding

The following code configures vLLM to use speculative decoding where proposals are generated using Suffix Decoding ([technical report](https://arxiv.org/abs/2411.04975)).

Like n-gram, Suffix Decoding can generate draft tokens by pattern-matching using the last `n` generated tokens. Unlike n-gram, Suffix Decoding (1) can pattern-match against both the prompt and previous generations, (2) uses frequency counts to propose the most likely continuations, and (3) speculates an adaptive number of tokens for each request at each iteration to get better acceptance rates.

Suffix Decoding can achieve better performance for tasks with high repetition, such as code-editing, agentic loops (e.g. self-reflection, self-consistency), and RL rollouts.

!!! tip "Install Arctic Inference"
    Suffix Decoding requires [Arctic Inference](https://github.com/snowflakedb/ArcticInference). You can install it with `pip install arctic-inference`.

!!! tip "Suffix Decoding Speculative Tokens"
    Suffix Decoding will speculate a dynamic number of tokens for each request at each decoding step, so the `num_speculative_tokens` configuration specifies the *maximum* number of speculative tokens. It is suggested to use a high number such as `16` or `32` (default).

??? code

    ```python
    from vllm import LLM, SamplingParams

    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(
        model="facebook/opt-6.7b",
        tensor_parallel_size=1,
        speculative_config={
            "method": "suffix",
            "num_speculative_tokens": 32,
        },
    )
    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    ```

## Speculating using MLP speculators

The following code configures vLLM to use speculative decoding where proposals are generated by
draft models that condition draft predictions on both context vectors and sampled tokens.
For more information see [this blog](https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/) or
[this technical report](https://arxiv.org/abs/2404.19124).

??? code

    ```python
    from vllm import LLM, SamplingParams

    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(
        model="meta-llama/Meta-Llama-3.1-70B-Instruct",
        tensor_parallel_size=4,
        speculative_config={
            "model": "ibm-ai-platform/llama3-70b-accelerator",
            "draft_tensor_parallel_size": 1,
        },
    )
    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    ```

Note that these speculative models currently need to be run without tensor parallelism, although
it is possible to run the main model using tensor parallelism (see example above). Since the
speculative models are relatively small, we still see significant speedups. However, this
limitation will be fixed in a future release.

A variety of speculative models of this type are available on HF hub:

- [llama-13b-accelerator](https://huggingface.co/ibm-ai-platform/llama-13b-accelerator)
- [llama3-8b-accelerator](https://huggingface.co/ibm-ai-platform/llama3-8b-accelerator)
- [codellama-34b-accelerator](https://huggingface.co/ibm-ai-platform/codellama-34b-accelerator)
- [llama2-70b-accelerator](https://huggingface.co/ibm-ai-platform/llama2-70b-accelerator)
- [llama3-70b-accelerator](https://huggingface.co/ibm-ai-platform/llama3-70b-accelerator)
- [granite-3b-code-instruct-accelerator](https://huggingface.co/ibm-granite/granite-3b-code-instruct-accelerator)
- [granite-8b-code-instruct-accelerator](https://huggingface.co/ibm-granite/granite-8b-code-instruct-accelerator)
- [granite-7b-instruct-accelerator](https://huggingface.co/ibm-granite/granite-7b-instruct-accelerator)
- [granite-20b-code-instruct-accelerator](https://huggingface.co/ibm-granite/granite-20b-code-instruct-accelerator)

## Speculating using EAGLE based draft models

The following code configures vLLM to use speculative decoding where proposals are generated by
an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found in [examples/offline_inference/spec_decode.py](../../../examples/offline_inference/spec_decode.py)

??? code

    ```python
    from vllm import LLM, SamplingParams

    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        tensor_parallel_size=4,
        speculative_config={
            "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
            "draft_tensor_parallel_size": 1,
            "num_speculative_tokens": 2,
            "method": "eagle",
        },
    )

    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    ```

A few important things to consider when using the EAGLE based draft models:

1. The EAGLE draft models available in the [HF repository for EAGLE models](https://huggingface.co/yuhuili) should
   be able to be loaded and used directly by vLLM after <https://github.com/vllm-project/vllm/pull/12304>.
   If you are using vllm version before <https://github.com/vllm-project/vllm/pull/12304>, please use the
   [script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d) to convert the speculative model,
   and specify `"model": "path/to/modified/eagle/model"` in `speculative_config`. If weight-loading problems still occur when using the latest version of vLLM, please leave a comment or raise an issue.

2. The EAGLE based draft models need to be run without tensor parallelism
   (i.e. draft_tensor_parallel_size is set to 1 in `speculative_config`), although
   it is possible to run the main model using tensor parallelism (see example above).

3. When using EAGLE-based speculators with vLLM, the observed speedup is lower than what is
   reported in the reference implementation [here](https://github.com/SafeAILab/EAGLE). This issue is under
   investigation and tracked here: <https://github.com/vllm-project/vllm/issues/9565>.

4. When using EAGLE-3 based draft model, option "method" must be set to "eagle3".
   That is, to specify `"method": "eagle3"` in `speculative_config`.

A variety of EAGLE draft models are available on the Hugging Face hub:

| Base Model                                                           | EAGLE on Hugging Face                     | # EAGLE Parameters |
|---------------------------------------------------------------------|-------------------------------------------|--------------------|
| Vicuna-7B-v1.3                                                       | yuhuili/EAGLE-Vicuna-7B-v1.3             | 0.24B              |
| Vicuna-13B-v1.3                                                      | yuhuili/EAGLE-Vicuna-13B-v1.3            | 0.37B              |
| Vicuna-33B-v1.3                                                      | yuhuili/EAGLE-Vicuna-33B-v1.3            | 0.56B              |
| LLaMA2-Chat 7B                                                       | yuhuili/EAGLE-llama2-chat-7B             | 0.24B              |
| LLaMA2-Chat 13B                                                      | yuhuili/EAGLE-llama2-chat-13B            | 0.37B              |
| LLaMA2-Chat 70B                                                      | yuhuili/EAGLE-llama2-chat-70B            | 0.99B              |
| Mixtral-8x7B-Instruct-v0.1                                           | yuhuili/EAGLE-mixtral-instruct-8x7B      | 0.28B              |
| LLaMA3-Instruct 8B                                                   | yuhuili/EAGLE-LLaMA3-Instruct-8B         | 0.25B              |
| LLaMA3-Instruct 70B                                                  | yuhuili/EAGLE-LLaMA3-Instruct-70B        | 0.99B              |
| Qwen2-7B-Instruct                                                    | yuhuili/EAGLE-Qwen2-7B-Instruct          | 0.26B              |
| Qwen2-72B-Instruct                                                   | yuhuili/EAGLE-Qwen2-72B-Instruct         | 1.05B              |

## Lossless guarantees of Speculative Decoding

In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of
speculative decoding, breaking down the guarantees into three key areas:

1. **Theoretical Losslessness**
   \- Speculative decoding sampling is theoretically lossless up to the precision limits of hardware numerics. Floating-point errors might
   cause slight variations in output distributions, as discussed
   in [Accelerating Large Language Model Decoding with Speculative Sampling](https://arxiv.org/pdf/2302.01318)

2. **Algorithmic Losslessness**
   \- vLLM’s implementation of speculative decoding is algorithmically validated to be lossless. Key validation tests include:

    > - **Rejection Sampler Convergence**: Ensures that samples from vLLM’s rejection sampler align with the target
    >   distribution. [View Test Code](https://github.com/vllm-project/vllm/blob/47b65a550866c7ffbd076ecb74106714838ce7da/tests/samplers/test_rejection_sampler.py#L252)
    > - **Greedy Sampling Equality**: Confirms that greedy sampling with speculative decoding matches greedy sampling
    >   without it. This verifies that vLLM's speculative decoding framework, when integrated with the vLLM forward pass and the vLLM rejection sampler,
    >   provides a lossless guarantee. Almost all of the tests in [tests/spec_decode/e2e](../../tests/spec_decode/e2e).
    >   verify this property using [this assertion implementation](https://github.com/vllm-project/vllm/blob/b67ae00cdbbe1a58ffc8ff170f0c8d79044a684a/tests/spec_decode/e2e/conftest.py#L291)

3. **vLLM Logprob Stability**
   \- vLLM does not currently guarantee stable token log probabilities (logprobs). This can result in different outputs for the
   same request across runs. For more details, see the FAQ section
   titled *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](../../usage/faq.md).

While vLLM strives to ensure losslessness in speculative decoding, variations in generated outputs with and without speculative decoding
can occur due to following factors:

- **Floating-Point Precision**: Differences in hardware numerical precision may lead to slight discrepancies in the output distribution.
- **Batch Size and Numerical Stability**: Changes in batch size may cause variations in logprobs and output probabilities, potentially
  due to non-deterministic behavior in batched operations or numerical instability.

For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](../../usage/faq.md).

## Resources for vLLM contributors

- [A Hacker's Guide to Speculative Decoding in vLLM](https://www.youtube.com/watch?v=9wNAgpX6z_4)
- [What is Lookahead Scheduling in vLLM?](https://docs.google.com/document/d/1Z9TvqzzBPnh5WHcRwjvK2UEeFeq5zMZb5mFE8jR0HCs/edit#heading=h.1fjfb0donq5a)
- [Information on batch expansion](https://docs.google.com/document/d/1T-JaS2T1NRfdP51qzqpyakoCXxSXTtORppiwaj5asxA/edit#heading=h.kk7dq05lc6q8)
- [Dynamic speculative decoding](https://github.com/vllm-project/vllm/issues/4565)