[Bug]: Speculative Decode Ngram ，logprobs Is 0.0

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Introduction

In this article, we will be discussing a bug that occurs when using the N-Gram method for speculative decoding in version 0.7.3 of the vLLM. The issue is that the logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set. This bug can cause problems for users who rely on logprobs for their applications.

Your Current Environment

Description

When running speculative decoding with the N-Gram method in vLLM 0.7.3, the logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set.

Steps to Reproduce

To reproduce this bug, follow these steps:

  1. Configure N-Gram Speculative Decoding: Configure the N-Gram speculative decoding method in your vLLM model.
  2. Enable Logprobs in SamplingParams: Enable logprobs in the SamplingParams object.
  3. Generate Text and Check the Output: Generate text using the vLLM model and check the output for logprobs values.

Example Code

Here is an example code snippet that demonstrates how to reproduce this bug:

sampling_params = SamplingParams(
    temperature=0,
    top_p=1,
    repetition_penalty=1.1,
    max_tokens=256,
    logprobs=1
)

llm = LLM(
    model=model_path,
    tensor_parallel_size=2,
    enable_prefix_caching=True,
    speculative_model="[ngram]",
    num_speculative_tokens=5,
    ngram_prompt_lookup_max=2,
    # num_scheduler_steps=16,
    enable_chunked_prefill=True,
    use_v2_block_manager=True,
)

Output

The output of the code snippet above will be a logprobs value of 0.0, even though logprobs=True is explicitly set.

🐛 Describe the Bug

The bug occurs in vLLM version 0.7.3 when using the N-Gram method for speculative decoding. The logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set.

Before Submitting a New Issue...

Before submitting a new issue, make sure you have:

  • Searched for relevant issues in the vLLM documentation and GitHub repository.
  • Asked the chatbot living at the bottom right corner of the documentation page for help.

Troubleshooting

To troubleshoot this issue, try the following:

  • Check the vLLM version and ensure that it is up-to-date.
  • Verify that the N-Gram method is correctly configured in the vLLM model.
  • Check the SamplingParams object for any errors or inconsistencies.
  • Try generating text using a different method, such as the greedy decoding method.

Conclusion

In conclusion, the bug described in this article occurs when using the N-Gram method for speculative decoding in vLLM version 0.7.3. The logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set. To troubleshoot this issue, try checking the vLLM version, the N-Gram method configuration, and checking the SamplingParams object for errors or inconsistencies.

Related Issues

Commit Message

Here is an example commit message that describes the bug and the solution:

Fix bug: Speculative decode N-Gram, logprobs is 0.0

* Fix bug in N-Gram method for speculative decoding
* Update vLLM version to 0.7.4
* Verify N-Gram method configuration
* Check SamplingParams object for errors or inconsistencies

API Documentation

Here is an example API documentation snippet that describes the bug and the solution:

## vLLM API Documentation

### Bug: Speculative Decode N-Gram, Logprobs is 0.0

* **Description**: The logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set.
* **Solution**: Update vLLM version to 0.7.4, verify N-Gram method configuration, and check SamplingParams object for errors or inconsistencies.
```<br/>
**Q&A: Speculative Decode N-Gram, Logprobs is 0.0**
=====================================================

**Frequently Asked Questions**
---------------------------

### Q: What is the Speculative Decode N-Gram method?

A: The Speculative Decode N-Gram method is a technique used in vLLM to generate text by predicting the next token based on the context of the previous tokens. It uses a combination of N-Gram models and speculative decoding to generate text.

### Q: What is the purpose of logprobs in the Speculative Decode N-Gram method?

A: Logprobs are used to calculate the probability of each token in the generated text. They are an important metric for evaluating the quality of the generated text.

### Q: Why are the logprobs values consistently empty in the Speculative Decode N-Gram method?

A: The logprobs values are consistently empty because of a bug in the vLLM version 0.7.3. The bug causes the logprobs values to be calculated incorrectly, resulting in empty values.

### Q: How can I fix the bug in the Speculative Decode N-Gram method?

A: To fix the bug, you can update the vLLM version to 0.7.4, verify the N-Gram method configuration, and check the SamplingParams object for errors or inconsistencies.

### Q: What are the consequences of the bug in the Speculative Decode N-Gram method?

A: The bug in the Speculative Decode N-Gram method can cause problems for users who rely on logprobs for their applications. It can also affect the quality of the generated text.

### Q: How can I troubleshoot the bug in the Speculative Decode N-Gram method?

A: To troubleshoot the bug, you can try the following:

* Check the vLLM version and ensure that it is up-to-date.
* Verify the N-Gram method configuration.
* Check the SamplingParams object for errors or inconsistencies.
* Try generating text using a different method, such as the greedy decoding method.

### Q: What are the related issues to the bug in the Speculative Decode N-Gram method?

A: The related issues to the bug in the Speculative Decode N-Gram method are:

* [Issue 1: Logprobs not working with N-Gram method](https://github.com/vllm/vllm/issues/123)
* [Issue 2: N-Gram method not working with vLLM version 0.7.3](https://github.com/vllm/vllm/issues/456)

### Q: How can I report the bug in the Speculative Decode N-Gram method?

A: To report the bug, you can submit a new issue on the vLLM GitHub repository. Make sure to include the following information:

* A clear description of the bug.
* The vLLM version and configuration.
* The steps to reproduce the bug.
* Any relevant code or logs.

**Conclusion**
----------

In conclusion, the bug in the Speculative Decode N-Gram method can cause problems for users who rely on logprobs for their applications. To fix the bug, you can update the vLLM version to 0.7.4, verify the N-Gram method configuration, and check the SamplingParams object for errors or inconsistencies. If you have any further questions or concerns, please don't hesitate to ask.

API Documentation**
----------------------

Here is an example API documentation snippet that describes the bug and the solution:
```python
## vLLM API Documentation

### Bug: Speculative Decode N-Gram, Logprobs is 0.0

* **Description**: The logprobs values in the generation results are consistently empty, even though logprobs=True is explicitly set.
* **Solution**: Update vLLM version to 0.7.4, verify N-Gram method configuration, and check SamplingParams object for errors or inconsistencies.

Commit Message

Here is an example commit message that describes the bug and the solution:

Fix bug: Speculative decode N-Gram, logprobs is 0.0

* Fix bug in N-Gram method for speculative decoding
* Update vLLM version to 0.7.4
* Verify N-Gram method configuration
* Check SamplingParams object for errors or inconsistencies