LLM Basics - Binary Quantization π₯
𧡠A thread - 1/n π
The concept itself isn't new, but what's reignited interest is the recent announcement from @cohere regarding their support for int8 and binary embeddings in their Cohere embed v3.
π First, in essence, embeddings are numerical representations of more complex objects, like text, images, audio, etc. Specifically, the objects are represented as n-dimensional vectors.
After transforming the complex objects, you can determine their similarity by calculating the similarity of the respective embeddings! This is crucial for many use cases: it serves as the backbone for recommendation systems, retrieval, one-shot or few-shot learning, outlier detection, similarity search, paraphrase detection, clustering, classification, and much more.
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π Binary Quantization for embeddings
Unlike quantization in models where you reduce the precision of weights, quantization for embeddings refers to a post-processing step for the embeddings themselves. In particular, binary quantization refers to the conversion of the float32 values in an embedding to 1-bit values, resulting in a 32x reduction in memory and storage usage.
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β¨ Binary quantization example
Vector embeddings are usually generated by embedding models, such as Cohereβs embed v3, and a single vector embeddings will in the following form.
[0.056, -0.128, -0.029, 0.047, β¦, 0.135]
To quantize float32 embeddings to binary, we simply threshold normalized embeddings at 0
That is, because these embeddings have very small absolute numbers close to zero, you can turn them into a binary vector:
1: If the value is greater or equal to 0.
0: If the value is smaller than 0.
So that you get something like this.
[1, 0, 0, β¦, 1]
𧡠A thread - 1/n π
The concept itself isn't new, but what's reignited interest is the recent announcement from @cohere regarding their support for int8 and binary embeddings in their Cohere embed v3.
π First, in essence, embeddings are numerical representations of more complex objects, like text, images, audio, etc. Specifically, the objects are represented as n-dimensional vectors.
After transforming the complex objects, you can determine their similarity by calculating the similarity of the respective embeddings! This is crucial for many use cases: it serves as the backbone for recommendation systems, retrieval, one-shot or few-shot learning, outlier detection, similarity search, paraphrase detection, clustering, classification, and much more.
-------
π Binary Quantization for embeddings
Unlike quantization in models where you reduce the precision of weights, quantization for embeddings refers to a post-processing step for the embeddings themselves. In particular, binary quantization refers to the conversion of the float32 values in an embedding to 1-bit values, resulting in a 32x reduction in memory and storage usage.
--------
β¨ Binary quantization example
Vector embeddings are usually generated by embedding models, such as Cohereβs embed v3, and a single vector embeddings will in the following form.
[0.056, -0.128, -0.029, 0.047, β¦, 0.135]
To quantize float32 embeddings to binary, we simply threshold normalized embeddings at 0
That is, because these embeddings have very small absolute numbers close to zero, you can turn them into a binary vector:
1: If the value is greater or equal to 0.
0: If the value is smaller than 0.
So that you get something like this.
[1, 0, 0, β¦, 1]
𧡠2/n
π So basically why does binary quantization reduce vector embedding size so much?
It's kind of like turning a colored image into a black and white image.
By converting the floating point numbers, which are stored in 32 bits, into a single bit, you only need 1/32nd of memory space to store a binarized vector. This can lead to increased search speed and reduced storage costs.
And because vector embeddings are usually high-dimensional, you can still get meaningful similarity measures for vector search. π€―
β¨ Now the question is how to calculate the similarity of vectors which has been binarized ?
π We can use the Hamming Distance to efficiently perform retrieval with these binary embeddings. This is simply the number of positions at which the bits of two binary embeddings differ. The lower the Hamming Distance, the closer the embeddings, and thus the more relevant the document. A huge advantage of the Hamming Distance is that it can be easily calculated with 2 CPU cycles, allowing for blazingly fast performance.
π So basically why does binary quantization reduce vector embedding size so much?
It's kind of like turning a colored image into a black and white image.
By converting the floating point numbers, which are stored in 32 bits, into a single bit, you only need 1/32nd of memory space to store a binarized vector. This can lead to increased search speed and reduced storage costs.
And because vector embeddings are usually high-dimensional, you can still get meaningful similarity measures for vector search. π€―
β¨ Now the question is how to calculate the similarity of vectors which has been binarized ?
π We can use the Hamming Distance to efficiently perform retrieval with these binary embeddings. This is simply the number of positions at which the bits of two binary embeddings differ. The lower the Hamming Distance, the closer the embeddings, and thus the more relevant the document. A huge advantage of the Hamming Distance is that it can be easily calculated with 2 CPU cycles, allowing for blazingly fast performance.
𧡠3/n
π€ Why Binary Quantization (BQ) is particularly suitable for high-dimensional vectors.
Simply because, in higher dimensional space, even with BQ, the vector can retain a high degree of information.
First, noting the basics, the number of elements in a single vector represents the total dimensionality of that vector. Each element of a vector represents a coordinate in a particular dimension, so a vector with `n` elements is said to inhabit an n-dimensional space.
When we refer to a vector's dimensionality, we are essentially describing how many degrees of freedom or independent directions of information it contains. For example, a 3-dimensional vector might represent a point in 3D space with coordinates along the X, Y, and Z axes.
π In high-dimensional spaces, vectors possess a large number of elements. Despite each element being aggressively quantized to a single bit, the overall vector retains substantial aggregate information. The high dimensionality ensures that, even in binary form, the relationships and structures inherent to the data can be preserved to a useful extent.
π This is on the assumption that the essential information of the vector is distributed across its many dimensions, allowing the binary-reduced vector to approximate the original's informational content in aggregate, despite the severe reduction in pre cision per dimension.
π€ Why Binary Quantization (BQ) is particularly suitable for high-dimensional vectors.
Simply because, in higher dimensional space, even with BQ, the vector can retain a high degree of information.
First, noting the basics, the number of elements in a single vector represents the total dimensionality of that vector. Each element of a vector represents a coordinate in a particular dimension, so a vector with `n` elements is said to inhabit an n-dimensional space.
When we refer to a vector's dimensionality, we are essentially describing how many degrees of freedom or independent directions of information it contains. For example, a 3-dimensional vector might represent a point in 3D space with coordinates along the X, Y, and Z axes.
π In high-dimensional spaces, vectors possess a large number of elements. Despite each element being aggressively quantized to a single bit, the overall vector retains substantial aggregate information. The high dimensionality ensures that, even in binary form, the relationships and structures inherent to the data can be preserved to a useful extent.
π This is on the assumption that the essential information of the vector is distributed across its many dimensions, allowing the binary-reduced vector to approximate the original's informational content in aggregate, despite the severe reduction in pre cision per dimension.
𧡠4/n
β¨ What are the drawbacks of Binary Quantization?
Firstly, the adoption of binary quantization impacts the accuracy and precision of your search results. Although you can still retrieve relevant outcomes, the nuance and detail provided by higher-resolution data can be lost, leading to less precise results.
Furthermore, binary quantization is a one-way streetβonce you've converted your data into binary form, there's no turning back. This process is a form of lossy compression, meaning once the data has undergone quantization, the original, detailed information is irretrievably lost.
β¨ What are the drawbacks of Binary Quantization?
Firstly, the adoption of binary quantization impacts the accuracy and precision of your search results. Although you can still retrieve relevant outcomes, the nuance and detail provided by higher-resolution data can be lost, leading to less precise results.
Furthermore, binary quantization is a one-way streetβonce you've converted your data into binary form, there's no turning back. This process is a form of lossy compression, meaning once the data has undergone quantization, the original, detailed information is irretrievably lost.
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