AdaTape: Foundation model with adaptive computation and dynamic read-and-write

Posted by Fuzhao Xue, Research Intern, and Mostafa Dehghani, Research Scientist, Google Adaptive computation refers to the ability of a machine learning system to adjust its behavior in response to changes in the environment. While conventional neural networks have a fixed function and computation capacity, i.e., they spend the same number of FLOPs for processing different inputs, a model with adaptive and dynamic computation modulates the computational budget it dedicates to processing each input, depending on the complexity of the input. Adaptive computation in neural networks is appealing for two key reasons. First, the mechanism that introduces adaptivity provides an inductive bias that can play a key role in solving some challenging tasks. For instance, enabling different numbers of computational steps for different inputs can be crucial in solving arithmetic problems that require modeling hierarchies of different depths. Second, it gives practitioners the ability to tune the cost of inference through greater flexibility offered by dynamic computation, as these models can be adjusted to spend more FLOPs processing a new input. Neural networks can be made adaptive by using different functions or computation budgets for various inputs. A deep neural network can be thought of as a function that outputs a result based on both the input and its parameters. To implement adaptive function types, a subset of parameters are selectively activated based on the input, a process referred to as conditional computation. Adaptivity based on the function type has been explored in studies on mixture-of-experts, where the sparsely activated parameters for each input sample are determined through routing. Another area of research in adaptive computation involves dynamic computation budgets. Unlike in standard neural networks, such as T5, GPT-3, PaLM, and ViT, whose computation budget is fixed for different samples, recent research has demonstrated that adaptive computation budgets can improve performance on tasks where transformers fall short. Many of these works achieve adaptivity by using dynamic depth to allocate the computation budget. For example, the Adaptive Computation Time (ACT) algorithm was proposed to provide an adaptive computational budget for recurrent neural networks. The Universal Transformer extends the ACT algorithm to transformers by making the computation budget dependent on the number of transformer layers used for each input example or token. Recent studies, like PonderNet, follow a similar approach while improving the dynamic halting mechanisms. In the paper “Adaptive Computation with Elastic Input Sequence”, we introduce a new model that utilizes adaptive computation, called AdaTape. This model is a Transformer-based architecture that uses a dynamic set of tokens to create elastic input sequences, providing a unique perspective on adaptivity in comparison to previous works. AdaTape uses an adaptive tape reading mechanism to determine a varying number of tape tokens that are added to each input based on input’s complexity. AdaTape is very simple to implement, provides an effective knob to increase the accuracy when needed, but is also much more efficient compared to other adaptive baselines because it directly injects adaptivity into the input sequence instead of the model depth. Finally, Adatape offers better performance on standard tasks, like image classification, as well as algorithmic tasks, while maintaining a favorable quality and cost tradeoff. Adaptive computation transformer with elastic input sequence AdaTape uses both the adaptive function types and a dynamic computation budget. Specifically, for a batch of input sequences after tokenization (e.g., a linear projection of non-overlapping patches from an image in the vision transformer), AdaTape uses a vector representing each input to dynamically select a variable-sized sequence of tape tokens. AdaTape uses a bank of tokens, called a “tape bank”, to store all the candidate tape tokens that interact with the model through the adaptive tape reading mechanism. We explore two different methods for creating the tape bank: an input-driven bank and a learnable bank. The general idea of the input-driven bank is to extract a bank of tokens from the input while employing a different approach than the original model tokenizer for mapping the raw input to a sequence of input tokens. This enables dynamic, on-demand access to information from the input that is obtained using a different point of view, e.g., a different image resolution or a different level of abstraction. In some cases, tokenization in a different level of abstraction is not possible, thus an input-driven tape bank is not feasible, such as when it's difficult to further split each node in a graph transformer. To address this issue, AdaTape offers a more general approach for generating the tape bank by using a set of trainable vectors as tape tokens. This approach is referred to as the learnable bank and can be viewed as an embedding layer where the model can dynamically retrieve tokens based on the complexity of the input example. The learnable bank enables AdaTape to generate a more flexible tape bank, providing it with the ability to dynamically adjust its computation budget based on the complexity of each input example, e.g., more complex examples retrieve more tokens from the bank, which let the model not only use the knowledge stored in the bank, but also spend more FLOPs processing it, since the input is now larger. Finally, the selected tape tokens are appended to the original input and fed to the following transformer layers. For each transformer layer, the same multi-head attention is used across all input and tape tokens. However, two different feed-forward networks (FFN) are used: one for all tokens from the original input and the other for all tape tokens. We observed slightly better quality by using separate feed-forward networks for input and tape tokens. An overview of AdaTape. For different samples, we pick a variable number of different tokens from the tape bank. The tape bank can be driven from input, e.g., by extracting some extra fine-grained information or it can be a set of trainable vectors. Adaptive tape reading is used to recursively select different sequences of tape tokens, with variable lengths, for different inputs. These tokens are then simply appended to inputs and fed to the transformer encoder. AdaTape provides helpful inductive bias We evaluate AdaTape on parity, a very challenging task for the standard Transformer, to study the effect of inductive biases in AdaTape. With the parity task, given a sequence 1s, 0s, and -1s, the model has to predict the evenness or oddness of the number of 1s in the sequence. Parity is the simplest non-counter-free or periodic regular language, but perhaps surprisingly, the task is unsolvable by the standard Transformer. Evaluation on the parity task. The standard Transformer and Universal Transformer were unable to perform this task, both showing performance at the level of a random guessing baseline. Despite being evaluated on short, simple sequences, both the standard Transformer and Universal Transformers were unable to perform the parity task as they are unable to maintain a counter within the model. However, AdaTape outperforms all baselines, as it incorporates a lightweight recurrence within its input selection mechanism, providing an inductive bias that enables the implicit maintenance of a counter, which is not possible in standard Transformers. Evaluation on image classification We also evaluate AdaTape on the image classification task. To do so, we trained AdaTape on ImageNet-1K from scratch. The figure below shows the accuracy of AdaTape and the baseline methods, including A-ViT, and the Universal Transformer ViT (UViT and U2T) versus their speed (measured as number of images, processed by each code, per second). In terms of quality and cost tradeoff, AdaTape performs much better than the alternative adaptive transformer baselines. In terms of efficiency, larger AdaTape models (in terms of parameter count) are faster than smaller baselines. Such results are consistent with the finding from previous work that shows that the adaptive model depth architectures are not well suited for many accelerators, like the TPU. We evaluate AdaTape by training on ImageNet from scratch. For A-ViT, we not only report their results from the paper but also re-implement A-ViT by training from scratch, i.e., A-ViT(Ours). A study of AdaTape’s behavior In addition to its performance on the parity task and ImageNet-1K, we also evaluated the token selection behavior of AdaTape with an input-driven bank on the JFT-300M validation set. To better understand the model's behavior, we visualized the token selection results on the input-driven bank as heatmaps, where lighter colors mean that position is more frequently selected. The heatmaps reveal that AdaTape more frequently picks the central patches. This aligns with our prior knowledge, as central patches are typically more informative — especially in the context of datasets with natural images, where the main object is in the middle of the image. This result highlights the intelligence of AdaTape, as it can effectively identify and prioritize more informative patches to improve its performance. We visualize the tape token selection heatmap of AdaTape-B/32 (left) and AdaTape-B/16 (right). The hotter / lighter color means the patch at this position is more frequently selected. Conclusion AdaTape is characterized by elastic sequence lengths generated by the adaptive tape reading mechanism. This also introduces a new inductive bias that enables AdaTape to have the potential to solve tasks that are challenging for both standard transformers and existing adaptive transformers. By conducting comprehensive experiments on image recognition benchmarks, we demonstrate that AdaTape outperforms standard transformers and adaptive architecture transformers when computation is held constant. Acknowledgments One of the authors of this post, Mostafa Dehghani, is now at Google DeepMind.

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Posted by Fuzhao Xue, Research Intern, and Mostafa Dehghani, Research Scientist, Google

Adaptive computation refers to the ability of a machine learning system to adjust its behavior in response to changes in the environment. While conventional neural networks have a fixed function and computation capacity, i.e., they spend the same number of FLOPs for processing different inputs, a model with adaptive and dynamic computation modulates the computational budget it dedicates to processing each input, depending on the complexity of the input.

Adaptive computation in neural networks is appealing for two key reasons. First, the mechanism that introduces adaptivity provides an inductive bias that can play a key role in solving some challenging tasks. For instance, enabling different numbers of computational steps for different inputs can be crucial in solving arithmetic problems that require modeling hierarchies of different depths. Second, it gives practitioners the ability to tune the cost of inference through greater flexibility offered by dynamic computation, as these models can be adjusted to spend more FLOPs processing a new input.

Neural networks can be made adaptive by using different functions or computation budgets for various inputs. A deep neural network can be thought of as a function that outputs a result based on both the input and its parameters. To implement adaptive function types, a subset of parameters are selectively activated based on the input, a process referred to as conditional computation. Adaptivity based on the function type has been explored in studies on mixture-of-experts, where the sparsely activated parameters for each input sample are determined through routing.

Another area of research in adaptive computation involves dynamic computation budgets. Unlike in standard neural networks, such as T5, GPT-3, PaLM, and ViT, whose computation budget is fixed for different samples, recent research has demonstrated that adaptive computation budgets can improve performance on tasks where transformers fall short. Many of these works achieve adaptivity by using dynamic depth to allocate the computation budget. For example, the Adaptive Computation Time (ACT) algorithm was proposed to provide an adaptive computational budget for recurrent neural networks. The Universal Transformer extends the ACT algorithm to transformers by making the computation budget dependent on the number of transformer layers used for each input example or token. Recent studies, like PonderNet, follow a similar approach while improving the dynamic halting mechanisms.

In the paper “Adaptive Computation with Elastic Input Sequence”, we introduce a new model that utilizes adaptive computation, called AdaTape. This model is a Transformer-based architecture that uses a dynamic set of tokens to create elastic input sequences, providing a unique perspective on adaptivity in comparison to previous works. AdaTape uses an adaptive tape reading mechanism to determine a varying number of tape tokens that are added to each input based on input’s complexity. AdaTape is very simple to implement, provides an effective knob to increase the accuracy when needed, but is also much more efficient compared to other adaptive baselines because it directly injects adaptivity into the input sequence instead of the model depth. Finally, Adatape offers better performance on standard tasks, like image classification, as well as algorithmic tasks, while maintaining a favorable quality and cost tradeoff.

Adaptive computation transformer with elastic input sequence

AdaTape uses both the adaptive function types and a dynamic computation budget. Specifically, for a batch of input sequences after tokenization (e.g., a linear projection of non-overlapping patches from an image in the vision transformer), AdaTape uses a vector representing each input to dynamically select a variable-sized sequence of tape tokens.

AdaTape uses a bank of tokens, called a “tape bank”, to store all the candidate tape tokens that interact with the model through the adaptive tape reading mechanism. We explore two different methods for creating the tape bank: an input-driven bank and a learnable bank.

The general idea of the input-driven bank is to extract a bank of tokens from the input while employing a different approach than the original model tokenizer for mapping the raw input to a sequence of input tokens. This enables dynamic, on-demand access to information from the input that is obtained using a different point of view, e.g., a different image resolution or a different level of abstraction.

In some cases, tokenization in a different level of abstraction is not possible, thus an input-driven tape bank is not feasible, such as when it’s difficult to further split each node in a graph transformer. To address this issue, AdaTape offers a more general approach for generating the tape bank by using a set of trainable vectors as tape tokens. This approach is referred to as the learnable bank and can be viewed as an embedding layer where the model can dynamically retrieve tokens based on the complexity of the input example. The learnable bank enables AdaTape to generate a more flexible tape bank, providing it with the ability to dynamically adjust its computation budget based on the complexity of each input example, e.g., more complex examples retrieve more tokens from the bank, which let the model not only use the knowledge stored in the bank, but also spend more FLOPs processing it, since the input is now larger.

Finally, the selected tape tokens are appended to the original input and fed to the following transformer layers. For each transformer layer, the same multi-head attention is used across all input and tape tokens. However, two different feed-forward networks (FFN) are used: one for all tokens from the original input and the other for all tape tokens. We observed slightly better quality by using separate feed-forward networks for input and tape tokens.

An overview of AdaTape. For different samples, we pick a variable number of different tokens from the tape bank. The tape bank can be driven from input, e.g., by extracting some extra fine-grained information or it can be a set of trainable vectors. Adaptive tape reading is used to recursively select different sequences of tape tokens, with variable lengths, for different inputs. These tokens are then simply appended to inputs and fed to the transformer encoder.

AdaTape provides helpful inductive bias

We evaluate AdaTape on parity, a very challenging task for the standard Transformer, to study the effect of inductive biases in AdaTape. With the parity task, given a sequence 1s, 0s, and -1s, the model has to predict the evenness or oddness of the number of 1s in the sequence. Parity is the simplest non-counter-free or periodic regular language, but perhaps surprisingly, the task is unsolvable by the standard Transformer.

Evaluation on the parity task. The standard Transformer and Universal Transformer were unable to perform this task, both showing performance at the level of a random guessing baseline.

Despite being evaluated on short, simple sequences, both the standard Transformer and Universal Transformers were unable to perform the parity task as they are unable to maintain a counter within the model. However, AdaTape outperforms all baselines, as it incorporates a lightweight recurrence within its input selection mechanism, providing an inductive bias that enables the implicit maintenance of a counter, which is not possible in standard Transformers.

Evaluation on image classification

We also evaluate AdaTape on the image classification task. To do so, we trained AdaTape on ImageNet-1K from scratch. The figure below shows the accuracy of AdaTape and the baseline methods, including A-ViT, and the Universal Transformer ViT (UViT and U2T) versus their speed (measured as number of images, processed by each code, per second). In terms of quality and cost tradeoff, AdaTape performs much better than the alternative adaptive transformer baselines. In terms of efficiency, larger AdaTape models (in terms of parameter count) are faster than smaller baselines. Such results are consistent with the finding from previous work that shows that the adaptive model depth architectures are not well suited for many accelerators, like the TPU.

We evaluate AdaTape by training on ImageNet from scratch. For A-ViT, we not only report their results from the paper but also re-implement A-ViT by training from scratch, i.e., A-ViT(Ours).

A study of AdaTape’s behavior

In addition to its performance on the parity task and ImageNet-1K, we also evaluated the token selection behavior of AdaTape with an input-driven bank on the JFT-300M validation set. To better understand the model’s behavior, we visualized the token selection results on the input-driven bank as heatmaps, where lighter colors mean that position is more frequently selected. The heatmaps reveal that AdaTape more frequently picks the central patches. This aligns with our prior knowledge, as central patches are typically more informative — especially in the context of datasets with natural images, where the main object is in the middle of the image. This result highlights the intelligence of AdaTape, as it can effectively identify and prioritize more informative patches to improve its performance.

We visualize the tape token selection heatmap of AdaTape-B/32 (left) and AdaTape-B/16 (right). The hotter / lighter color means the patch at this position is more frequently selected.

Conclusion

AdaTape is characterized by elastic sequence lengths generated by the adaptive tape reading mechanism. This also introduces a new inductive bias that enables AdaTape to have the potential to solve tasks that are challenging for both standard transformers and existing adaptive transformers. By conducting comprehensive experiments on image recognition benchmarks, we demonstrate that AdaTape outperforms standard transformers and adaptive architecture transformers when computation is held constant.

Acknowledgments

One of the authors of this post, Mostafa Dehghani, is now at Google DeepMind.

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1) input, (2) rationale, and (3) output. Given the prompt, an LLM is able to mimic the triplet demonstration to generate the rationale for any new input. For instance, in a commonsense question answering task, given the input question “Sammy wanted to go to where the people are. Where might he go? Answer Choices: (a) populated areas, (b) race track, (c) desert, (d) apartment, (e) roadblock”, distilling step-by-step provides the correct answer to the question, “(a) populated areas”, paired with the rationale that provides better connection from the question to the answer, “The answer must be a place with a lot of people. Of the above choices, only populated areas have a lot of people.” By providing CoT examples paired with rationales in the prompt, the in-context learning ability allows LLMs to output corresponding rationales for future unseen inputs.

We use the few-shot CoT prompting, which contains both an example rationale (highlighted in green) and a label (highlighted in blue), to elicit rationales from an LLM on new input examples. The example is from a commonsense question answering task.

After the rationales are extracted, in the second stage, we incorporate the rationales in training small models by framing the training process as a multi-task problem. Specifically, we train the small model with a novel rationale generation task in addition to the standard label prediction task. The rationale generation task enables the model to learn to generate the intermediate reasoning steps for the prediction, and guides the model to better predict the resultant label. We prepend task prefixes (i.e., [label] and [rationale] for label prediction and rationale generation, respectively) to the input examples for the model to differentiate the two tasks.

Experimental setup

In the experiments, we consider a 540B PaLM model as the LLM. For task-specific downstream models, we use T5 models. For CoT prompting, we use the original CoT prompts when available and curate our own examples for new datasets. We conduct the experiments on four benchmark datasets across three different NLP tasks: e-SNLI and ANLI for natural language inference; CQA for commonsense question answering; and SVAMP for arithmetic math word problems. We include two sets of baseline methods. For comparison to few-shot prompted LLMs, we compare to few-shot CoT prompting with a 540B PaLM model. In the paper, we also compare standard task-specific model training to both standard fine-tuning and standard distillation. In this blogpost, we will focus on the comparisons to standard fine-tuning for illustration purposes.

Less training data

Compared to standard fine-tuning, the distilling step-by-step method achieves better performance using much less training data. For instance, on the e-SNLI dataset, we achieve better performance than standard fine-tuning when using only 12.5% of the full dataset (shown in the upper left quadrant below). Similarly, we achieve a dataset size reduction of 75%, 25% and 20% on ANLI, CQA, and SVAMP.

Distilling step-by-step compared to standard fine-tuning using 220M T5 models on varying sizes of human-labeled datasets. On all datasets, distilling step-by-step is able to outperform standard fine-tuning, trained on the full dataset, by using much less training examples.

Smaller deployed model size

Compared to few-shot CoT prompted LLMs, distilling step-by-step achieves better performance using much smaller model sizes. For instance, on the e-SNLI dataset, we achieve better performance than 540B PaLM by using a 220M T5 model. On ANLI, we achieve better performance than 540B PaLM by using a 770M T5 model, which is over 700X smaller. Note that on ANLI, the same 770M T5 model struggles to match PaLM’s performance using standard fine-tuning.

We perform distilling step-by-step and standard fine-tuning on varying sizes of T5 models and compare their performance to LLM baselines, i.e., Few-shot CoT and PINTO Tuning. Distilling step-by-step is able to outperform LLM baselines by using much smaller models, e.g., over 700× smaller models on ANLI. Standard fine-tuning fails to match LLM’s performance using the same model size.

Distilling step-by-step outperforms few-shot LLMs with smaller models using less data

Finally, we explore the smallest model sizes and the least amount of data for distilling step-by-step to outperform PaLM’s few-shot performance. For instance, on ANLI, we surpass the performance of the 540B PaLM using a 770M T5 model. This smaller model only uses 80% of the full dataset. Meanwhile, we observe that standard fine-tuning cannot catch up with PaLM’s performance even using 100% of the full dataset. This suggests that distilling step-by-step simultaneously reduces the model size as well as the amount of data required to outperform LLMs.

We show the minimum size of T5 models and the least amount of human-labeled examples required for distilling step-by-step to outperform LLM’s few-shot CoT by a coarse-grained search. Distilling step-by-step is able to outperform few-shot CoT using not only much smaller models, but it also achieves so with much less training examples compared to standard fine-tuning.

Conclusion

We propose distilling step-by-step, a novel mechanism that extracts rationales from LLMs as informative supervision in training small, task-specific models. We show that distilling step-by-step reduces both the training dataset required to curate task-specific smaller models and the model size required to achieve, and even surpass, a few-shot prompted LLM’s performance. Overall, distilling step-by-step presents a resource-efficient paradigm that tackles the trade-off between model size and training data required.

Availability on Google Cloud Platform

Distilling step-by-step is available for private preview on Vertex AI. If you are interested in trying it out, please contact vertex-llm-tuning-preview@google.com with your Google Cloud Project number and a summary of your use case.

Acknowledgements

This research was conducted by Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Thanks to Xiang Zhang and Sergey Ioffe for their valuable feedback.

douglas.campbell@wellstar360.com September 21, 2023

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