MAMBA from Scratch: Neural Nets Better and Faster than Transformers
TLDRMamba is a groundbreaking neural network architecture that has surpassed transformers in language modeling. Despite being tested on smaller models, Mamba's results are promising and it operates more efficiently, using O(nlog(n)) compute as opposed to transformers' O(n^2). This allows for larger context sizes. Mamba is also presented as an extension of recurrent neural networks (RNNs), which are easier to understand than state-space models. The architecture addresses the issue of long-range dependencies in language text, which convolutional neural nets struggle with. Mamba's design includes the use of different weights depending on the input, enabling selective forgetting of information, and it expands output vector sizes to store more information. However, the Mamba paper faced controversy after being rejected from ICLR 2024, despite its significant potential and community support.
Takeaways
- {"🌟":"Mamba is a new neural net architecture that may surpass transformers in language modelling."}
- {"📉":"Mamba has shown promising results in tests, despite only being evaluated at small model sizes."}
- {"⚙️":"Mamba operates with a computational complexity of O(nlog(n)), compared to the O(n^2) of transformers."}
- {"🔍":"Mamba allows for larger context sizes in language models due to its lower compute requirements."}
- {"🔄":"Mamba can be understood as an extension of Recurrent Neural Networks (RNNs), which are more straightforward than state-space models."}
- {"🚀":"Linear RNNs, a type of Mamba, have shown to be effective for long sequence models and avoid the typical RNN training problems."}
- {"🤖":"Mamba introduces the concept of using varying weights for each step based on the input, allowing the model to selectively forget information."}
- {"📊":"Mamba expands the size of the output vectors to store more information, then projects them back to the original size."}
- {"":"Mamba's architecture has been a topic of controversy within the machine learning community, with debates on its peer review process."}
- {"🏆":"Despite its rejection from ICLR 2024, Mamba has garnered attention for potentially outperforming transformers in language tasks."}
- {"🧐":"The Mamba paper faced criticism for not being tested on the long range arena benchmark and for its perceived limitations in evaluating reasoning ability."}
Q & A
What is Mamba in the context of the video script?
-Mamba is a new neural net architecture that is claimed to be better than transformers at language modeling. It has shown promising results in tests, particularly with smaller model sizes, and uses less computational power than transformers.
How does Mamba's compute complexity compare to that of transformers?
-Mamba has a compute complexity of O(nlog(n)), whereas transformers have a higher complexity of O(n^2) for an input sequence of n words. This makes Mamba more efficient in terms of computation.
What is a recurrent neural network (RNN) and how does it differ from a convolutional neural network (CNN)?
-A recurrent neural network (RNN) is a type of sequence model that processes input vectors sequentially by using the output from the previous step as part of the next step's input, allowing it to incorporate information from all previous vectors. This differs from a convolutional neural network (CNN), which applies a neural net to small groups of vectors at a time and struggles with long-range dependencies due to its limited view of the input sequence.
Why are RNNs difficult to train and what are the two main problems associated with them?
-RNNs are difficult to train primarily due to the vanishing and exploding gradients problem. This occurs because the same weight is used in each step, leading to gradients that can become too small or too large to effectively update the weights. Additionally, RNNs cannot be parallelized as easily as CNNs, making them slower in practice.
How does the linear RNN approach solve the problem of parallelization in RNNs?
-Linear RNNs solve the parallelization problem by using a linear function instead of a neural net for the recurrent operation. This allows the computation of the linear recurrence to be performed in parallel in just O(log(n)) time using a technique similar to a cumulative sum algorithm.
What is the significance of matrix diagonalization in the context of linear RNNs?
-Matrix diagonalization is used to avoid the high computational cost of performing a O(d^3) matrix multiplication at each step of the linear recurrence. By representing the recurrent weight matrix in diagonalized form, the computation is reduced to element-wise multiplication between vectors, which is much faster.
How does Mamba architecture address the issue of forgetting information in linear RNNs?
-Mamba addresses the issue by using different weights for each step that depend on the input. It applies a linear function to each input vector to generate a separate weight vector for that input, allowing the model to selectively forget information based on the input.
What is the controversy surrounding the Mamba paper's rejection from ICLR 2024?
-The controversy stems from the fact that despite Mamba's demonstrated performance improvements over transformers in language modeling and its ability to use less computation, the paper was rejected by peer reviewers. The community had expected the paper to be accepted due to its significant potential impact, and the rejection raised debates about the peer review process and its effectiveness.
Why does Mamba expand the size of the output vectors?
-Mamba expands the size of the output vectors by a factor of 16 to store much more information from previous inputs. This allows the model to have a larger context window, which is then projected back down to the original size before being passed to the next layer.
How does Mamba's memory requirement compare to that of transformers?
-Mamba, like transformers, does not have a quadratic memory requirement. Both have a linear memory cost, contrary to a misconception mentioned in the ICLR peer review.
What was the reviewers' concern regarding Mamba's evaluation on downstream tasks?
-The reviewers were concerned that Mamba was only evaluated on language modeling and not on downstream tasks that measure a model's reasoning ability. However, the Mamba paper did include zero-shot prompting on standard downstream benchmark tasks, where Mamba outperformed other language models.
What is the main advantage of Mamba in terms of computational efficiency?
-The main advantage of Mamba in terms of computational efficiency is its use of O(nlog(n)) compute, which is significantly less than the O(n^2) compute required by transformers, allowing for the use of much greater context sizes.
Outlines
🚀 Introduction to Mamba: A New Language Modelling Architecture
The video introduces Mamba, a novel neural net architecture that surpasses transformers in language modelling tasks. Mamba has shown promising results in tests, despite being evaluated at small model sizes. It is also more computationally efficient, using O(nlog(n)) compute as opposed to the O(n^2) required by transformers. Mamba is presented as an extension of state-space models, which are complex, but the video opts to explain it through the lens of recurrent neural networks (RNNs), which are more accessible. The script outlines the limitations of convolutional layers and how RNNs address the issue of long-range dependencies, despite their own challenges with parallelization and training difficulties.
🔍 Linear RNNs: A Solution to RNNs' Training and Speed Issues
The video discusses the shift from traditional RNNs to linear RNNs, which are more effective for long sequence models. Linear RNNs replace the neural net with a linear function, and while this might seem limiting, it is compensated by applying a full neural net to each output vector. The video explains how linear recurrence can be computed in parallel in O(log(n)) time, addressing the speed issue. It also covers the training issues of RNNs, such as vanishing and exploding gradients, and how they can be mitigated in linear RNNs through careful initialization and the use of complex numbers.
🧮 Matrix Diagonalization: Optimizing Linear RNNs for Parallel Computing
The video delves into the optimization of linear RNNs for parallel computing. It explains the computational inefficiency that arises from performing matrix multiplications in each step of a linear recurrence and how matrix diagonalization can be used to circumvent this issue. By representing the recurrent weight matrix in diagonalized form, the video shows how the computation can be reduced to element-wise multiplication, significantly speeding up the process. The parameters of the model are detailed, along with the practical approach of using two independent complex matrices to avoid the slow computation of matrix inverses.
🧠 Training Challenges and Solutions for Linear RNNs
The video addresses the training difficulties associated with RNNs, particularly the problem of vanishing and exploding gradients. It explains the concept of gradient and how it is affected by the recurrent weights and inputs. The video then describes the solution proposed in the linear RNN paper, which involves initializing the model in a stable state to allow it to learn long contexts effectively. The weights are parameterized in complex polar form, and the magnitude is constrained to be less than 1 to maintain stability. The video also discusses the initialization of the angle and the inputs to ensure the model starts in a state that is close to the identity function.
🏆 Mamba's Performance and Its Impact on Language Modelling
The video evaluates Mamba's performance on the long-range arena benchmark and compares it with transformers. It explains that while linear RNNs perform exceptionally well on this benchmark, they do not necessarily make for good language models. Mamba, however, is designed specifically for language modelling and is shown to outperform transformers in this domain. The video also describes Mamba's architecture, which includes the use of varying weights based on input and expanding the size of output vectors to store more information, then projecting them back to the original size.
📈 Mamba's Computational Efficiency and Controversy in the ML Community
The video discusses Mamba's computational efficiency, noting its O(nlog(n)) compute requirement compared to the O(n^2) of transformers. It also touches on the controversy surrounding Mamba's rejection from the ICLR 2024 conference, despite its promising results and the successful reproduction of its results by other groups. The video critiques the peer review process, highlighting what it perceives as flawed reasoning behind the rejection, such as the focus on the long-range arena benchmark and the misunderstanding of Mamba's memory requirements. The video concludes by inviting the audience to share their thoughts on the peer review process and the Mamba architecture.
Mindmap
Keywords
💡Mamba
💡Transformers
💡State-space model
💡Recurrent Neural Networks (RNNs)
💡Long-range dependencies
💡Compute
💡Convolutional layers
💡Vanishing and exploding gradients
💡Zero-shot prompting
💡ICLR 2024
💡Memory cost
Highlights
Mamba is a new neural net architecture that is better than transformers at language modelling.
Mamba has been tested at small model sizes up to a few billion parameters with promising results.
Mamba uses less compute than transformers, with O(nlog(n)) compute compared to O(n^2).
Mamba based language models allow for much greater context sizes to be used.
Mamba is presented as an extension of state-space models and recurrent neural networks (RNNs).
State-space models are gaining popularity but are more complicated than RNNs.
Recurrent neural networks (RNNs) address the long-range dependencies issue of convolutional neural nets.
RNNs have two main problems: non-parallelizable compute and difficulty in training.
Linear RNNs can avoid the problems of traditional RNNs and are effective for long sequence models.
Linear recurrence can be computed in parallel in just O(log(n)) time.
Matrix diagonalization is used to speed up the computation of linear recurrences.
The training issues of RNNs can be fixed by initializing the model in a stable state.
Mamba proposes using different weights depending on the input to selectively forget information.
Mamba expands the size of the output vectors by a factor of 16 to store more information.
Mamba transfers data to high-performance memory and computes the whole operation in a single block for efficiency.
Mamba performs better than transformers at language modelling while using only O(nlog(n)) compute.
The Mamba paper caused controversy in the machine learning community after being rejected from ICLR 2024.
Despite the rejection, Mamba has been reproduced and validated by several groups, showing it performs better than transformers.
Critics argue that the Mamba paper should have included evaluations on downstream tasks, not just language modelling.