Accelerating Large Language Model Inference with Combined Token and Embedding Speculators

Główne pojęcia

Speculative decoding with novel architectures that condition on both context vectors and sampled tokens can effectively predict high-quality n-grams, allowing 2-3x acceleration of highly optimized large language model inference in production settings.

Streszczenie

The authors describe the design and training of novel speculative decoding draft models to accelerate the inference speeds of large language models in a production environment. By conditioning draft predictions on both context vectors and sampled tokens, the speculators can efficiently predict high-quality n-grams, which the base model then accepts or rejects. This allows for predicting multiple tokens per inference forward pass, accelerating wall-clock inference speeds of highly optimized base model implementations by a factor of 2-3x.

The key contributions are:

1. Showing that speculator output quality can be greatly improved by conditioning on sampled tokens, in addition to the base model context vector.
2. Introducing an efficient two-stage training scheme, aligning the speculators first to base model input behavior, then to output behavior.
3. Using this speculator training pipeline, accelerating four highly optimized production large language models by a factor of 2-3x.
4. Exploring the limitations of speculative decoding in a production setting, where the promised speedups diminish as baseline computation and efficiency levels increase.
5. Outlining next steps and further areas of investigation.

The authors open-source their code and release the speculators for their 13B-parameter base models on HuggingFace.

Statystyki

Our baseline Llama2-7B model runs at 94.9 tokens per second, compared to EAGLE's gpt-fast implementation baseline of 55.1 tokens per second.
For prompt length 64 and batch size 1, the speculator achieves almost 2x speedup, generating 2.67 tokens per step.
As prompt length and batch size increase, the speedup from speculative decoding erodes, and can even become slower than non-speculative decoding.
For Llama2-13B, we observe a similar 2x reduction in latency for batch size 1 and 5 candidates, but this improvement gradually disappears for larger batch sizes.
The 7-headed speculator for Codellama-13B-instruct achieves over 3x wall-clock speedup, allowing the 13B parameter model to run at 181.5 tokens per second in fp16 precision.
The 5-headed speculator for the 20B Granite model also achieves around 3x wall-clock speedup in the best case.

Cytaty

"By conditioning draft predictions on both context vectors and sampled tokens, we can train our speculators to efficiently predict high-quality n-grams, which the base model then accepts or rejects."
"As powerful as they are, large language models incur substantial memory and computation overhead. Well-known models such as Llama2-13B contain 13 billion parameters, occupying roughly 24 Gb in memory using typical 16-bit weight representations."
"An obvious way to rectify this imbalance would be to predict multiple tokens at a time. Indeed, classical NLP theory has proven that even a simple 2/3-gram language model has great predictive capability, which tells us that learned language models should be capable of predicting more than one token at a time with a reasonable accuracy."

Kluczowe wnioski z

Accelerating Production LLMs with Combined Token/Embedding Speculators

by Davis Werthe... o arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19124.pdf

Głębsze pytania

How could the speculator architecture and training be further improved to maintain the speedup benefits even as the baseline model becomes more computationally efficient

To maintain the speedup benefits as the baseline model becomes more computationally efficient, several enhancements can be considered for the speculator architecture and training process:

Increased Parallelism: Introducing more heads/stages in the speculator architecture can allow for predicting a higher number of tokens per step, thus leveraging the increased computational efficiency of the base model.

Dynamic Adaptation: Implementing dynamic adjustment of the number of parallel candidates (k) based on the workload and hardware specifics can optimize the speculator's performance in real-time, ensuring efficient utilization of resources.

Weight Tying: While weight tying was initially avoided for speed considerations, selectively tying weights across different stages or between the speculator and the base model can reduce parameter count, leading to better memory management and potentially improved convergence.

Auxiliary Losses: Utilizing the same latent space as the base model can facilitate the introduction of auxiliary losses, enhancing the training process and potentially improving the speculator's accuracy and efficiency.

By incorporating these enhancements, the speculator architecture can adapt to the evolving computational efficiency of the base model, maintaining and even enhancing the speedup benefits in production environments.

What are the potential drawbacks or risks of relying on speculative decoding in a production environment, and how could they be mitigated

Relying on speculative decoding in a production environment presents certain drawbacks and risks that need to be addressed to ensure reliable performance:

Overhead: Speculative decoding introduces additional computational overhead due to evaluating multiple candidate sequences, which can impact overall inference speed and resource utilization.

Accuracy: The accuracy of speculative predictions may not always align perfectly with the base model's outputs, leading to potential discrepancies and errors in the generated text.

Resource Management: Speculative decoding requires careful management of GPU resources, especially in scenarios where GPU bandwidth is already maximized, as additional parallelism may not yield significant speedups.

To mitigate these risks, strategies such as dynamic adjustment of parallel candidates based on workload, continuous monitoring of performance metrics, and thorough validation of speculator outputs against base model behavior can be implemented. Additionally, optimizing the speculator architecture for efficient resource utilization and accuracy can help mitigate potential drawbacks in a production setting.

Could the insights from this work on accelerating large language models be applied to other domains beyond natural language processing, such as code generation or multimodal models

The insights gained from accelerating large language models through speculative decoding can indeed be applied to other domains beyond natural language processing:

Code Generation: As demonstrated with Codellama-13B and Granite-20B models, leveraging speculative decoding for code-related tasks can lead to significant speedups in generating code snippets or completing programming tasks. The structured and predictable nature of code makes it a suitable domain for applying speculative decoding techniques.

Multimodal Models: Extending the concept to multimodal models, which combine text, images, and other modalities, can enhance the efficiency of inference in tasks like image captioning, visual question answering, and multimodal translation. By conditioning predictions on both visual and textual context, speculative decoding can improve the speed and accuracy of multimodal model outputs.

By adapting the principles of speculative decoding to these domains, researchers and practitioners can explore new avenues for accelerating model inference and improving performance across a wide range of applications beyond traditional natural language processing tasks.