enginex-mlu370-vllm/vllm-v0.6.2/docs/source/models/spec_decode.rst

.. _spec_decode:

Speculative decoding in vLLM
============================

.. warning::
    Please note that speculative decoding in vLLM is not yet optimized and does
    not usually yield inter-token latency reductions for all prompt datasets or sampling parameters. The work
    to optimize it is ongoing and can be followed in `this issue. <https://github.com/vllm-project/vllm/issues/4630>`_

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.

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.

.. code-block:: 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_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:

.. code-block:: bash

    python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 --model facebook/opt-6.7b \
        --seed 42 -tp 1 --speculative_model facebook/opt-125m --use-v2-block-manager \
        --num_speculative_tokens 5 --gpu_memory_utilization 0.8

Then use a client:

.. code-block:: 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-block:: 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_model="[ngram]",
        num_speculative_tokens=5,
        ngram_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 MLP speculators
---------------------------------

The following code configures vLLM to use speculative decoding where proposals are generated by
draft models that conditioning 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-block:: 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_model="ibm-fms/llama3-70b-accelerator",
        speculative_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-fms/llama-13b-accelerator>`_
* `llama3-8b-accelerator <https://huggingface.co/ibm-fms/llama3-8b-accelerator>`_
* `codellama-34b-accelerator <https://huggingface.co/ibm-fms/codellama-34b-accelerator>`_
* `llama2-70b-accelerator <https://huggingface.co/ibm-fms/llama2-70b-accelerator>`_
* `llama3-70b-accelerator <https://huggingface.co/ibm-fms/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>`_

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 `this directory <https://github.com/vllm-project/vllm/tree/b67ae00cdbbe1a58ffc8ff170f0c8d79044a684a/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 <../serving/faq>`_.


**Conclusion**

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.

**Mitigation Strategies**

For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the `FAQs <../serving/faq>`_.

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>`_