Making AI More Accurate: Microscaling on NVIDIA Blackwell

Summary

The script discusses the advancements in machine learning quantization, highlighting the shift towards lower precision formats like FP6 and FP4 to accelerate computation. It emphasizes the challenges and potential of these formats, particularly in inference and low-power devices. The introduction of microscaling by Nvidia is noted as a significant development, allowing for efficient scaling of the number line to maintain accuracy. The need for standardization in these reduced precision formats is also stressed, as the industry awaits clear guidelines for implementation.

Takeaways

• 📈 In machine learning, quantization involves using smaller numbers to increase computational efficiency.
• 🔢 The adoption of reduced precision formats like FP16, BFloat16, and INT8 has become popular for speeding up computations while maintaining accuracy.
• 🌟 Nvidia's Blackwell announcement introduced support for FP6 and FP4 formats, aiming to further accelerate math workloads by using fewer bits.
• 🚀 Despite the limited representation of numbers in FP4 (2 bits for the magnitude), it's believed that these formats can still be sufficient for certain machine learning tasks, especially inference.
• 🔍 Ongoing research is needed to confirm the accuracy of low-precision formats like FP6 and FP4 for everyday use.
• 📊 Microscaling is a technique that allows for better utilization of reduced precision formats by adjusting the scaling factor, which was initially introduced by Microsoft as MSFP12.
• 🔧 Microscaling enables a range of numbers to have their accuracy and range scaled to a specific region of interest, which is crucial for maintaining precision in calculations.
• 🛠️ Nvidia's approach to microscaling supports a larger number of FP4 values with a single 8-bit scaling factor, improving efficiency.
• 📈 The industry is moving towards standardized reduced precision formats, but the rapid pace of machine learning advancements presents challenges for standardization bodies like IEEE.
• 👨‍💻 For programmers working with fundamental mathematical operations, the complexity and rapid evolution of reduced precision formats present a high barrier to entry.
• 🎯 Clear guidelines and industry consensus on the implementation and usage of reduced precision formats are essential for their successful adoption and to maximize their potential benefits.

Q & A

What is the purpose of quantization in machine learning?

Quantization is a process that allows the use of smaller numbers or bits to increase computational efficiency, which can lead to faster computation and reduced memory usage while maintaining accuracy, particularly for machine learning tasks.

What are the benefits of using reduced precision formats like FP16, bfloat16, and INT8?

Reduced precision formats offer substantial speedups in computation while maintaining the same level of accuracy. They enable faster processing of large datasets and models, which is crucial for machine learning applications, especially in resource-constrained environments.

What new formats did Nvidia announce in their latest GTC event?

Nvidia announced support for FP6 and FP4 formats, which are floating-point precision in six bits and four bits, respectively. These formats aim to further increase the number of operations that can be performed, especially for machine learning and inference tasks.

What is the main challenge with using FP4 format for machine learning?

The main challenge with FP4 format is that it has only four bits to represent a number, with one bit for the sign and one for indicating infinity or not a number. This leaves only two bits to cover the entire range of numbers, which limits the precision and the number of operations that can be performed.

How does microscaling help in addressing the limitations of reduced precision formats?

Microscaling involves using an additional set of bits, typically eight bits, as a scaling factor. This allows the representation of a range of numbers with greater precision within a specific interval, effectively expanding the dynamic range and improving the accuracy of computations in reduced precision formats.

What is the significance of the work done by Microsoft in the context of microscaling?

Microsoft introduced the concept of microscaling in their research, which was first implemented in a format called MSFP12. This innovation allows for the scaling factor to be applied to multiple values, reducing the overhead and making reduced precision formats like FP4 more practical and efficient for machine learning tasks.

How do processors like Tesla Dojo and Microsoft's Maya AI 100 chip utilize scaling factors?

These processors support scaling factors that can be applied to a range of values within a machine learning matrix. By doing so, they can perform operations across a large number of values with a single scaling factor, enhancing efficiency and performance in computations.

What is the role of the IEEE standards body in the development of precision formats?

The IEEE standards body is responsible for establishing and maintaining standards for various data formats, including floating-point precision formats like FP64 and FP32. They are also working on standards for 16-bit and 8-bit precision formats to ensure consistency and compatibility across different architectures and applications.

What are the implications of the diversity in reduced precision formats across different architectures?

The diversity in reduced precision formats can lead to inconsistencies in mathematical operations and handling of special cases like infinities and not-a-numbers (NaNs). This can make it difficult to manage and ensure the correctness of computations across different hardware and software platforms.

Why is it important for the industry to come together and define clear standards for reduced precision formats?

Clear standards are essential for ensuring compatibility, efficiency, and correctness across different platforms and applications. They help developers and programmers to understand and effectively utilize reduced precision formats, leading to better performance and more reliable machine learning models.

How can programmers and developers overcome the challenges associated with reduced precision formats?

To overcome these challenges, programmers and developers need clear guidelines and documentation on the implementation of reduced precision formats. They may also need to engage with more specialized frameworks and tools beyond common ones like TensorFlow and PyTorch to extract the maximum performance benefits from these formats.

Outlines

00:00

📈 Quantization and Reduced Precision Formats in Machine Learning

This paragraph discusses the concept of quantization in machine learning, which is the process of using smaller numbers with fewer bits to increase computational efficiency. It highlights the shift towards formats like FP16 and BFloat16 for substantial speedups without compromising accuracy. The paragraph also introduces Nvidia's announcement of new formats, FP6 and FP4, which further reduce the precision of floating-point numbers to achieve even greater computational efficiency. The challenge of representing floating-point numbers with limited bits is addressed, emphasizing the need for research to ensure these low-precision formats maintain the required accuracy for everyday use. The concept of microscaling, introduced by Microsoft and now adopted by Nvidia, is explained as a way to scale the accuracy and range of numbers for machine learning tasks, allowing for efficient use of reduced precision formats.

05:00

📚 Standardization and Challenges in Reduced Precision Computing

The second paragraph delves into the challenges and considerations of standardizing reduced precision formats in the industry. It mentions the existence of various versions of FP8 and the need for consistent standards to ensure compatibility and manageability across different architectures. The role of the IEEE standards body in establishing norms for floating-point representations is highlighted, noting the slow pace of standardization compared to the rapid advancements in machine learning. The paragraph emphasizes the importance of clear guidelines and industry collaboration to simplify the implementation and understanding of these formats for programmers. It also touches on the potential for frameworks like TensorFlow and PyTorch to abstract away some of the complexity, but acknowledges that extracting maximum performance may require more specialized knowledge and skill.

Keywords

💡Quantization

Quantization in the context of machine learning refers to the process of reducing the number of bits used to represent a number. This is done to increase computational efficiency, allowing for more operations to be performed in a given time frame. It is particularly relevant for accelerating large-scale computations such as Giga flops and GigaOps. The video discusses how quantization to lower precision formats, like FP16 and INT8, has become popular for offering substantial speedups without compromising accuracy.

💡Floating Point Precision

Floating point precision is a method used in computer systems to represent real numbers, which allows for the efficient handling of a wide range of values. It includes a sign bit, an exponent, and a mantissa (or fraction). The precision refers to the number of bits allocated to each part of the floating point number, with higher precision like double precision (FP64) being more accurate but requiring more bits, and lower precision like FP16 or INT8 requiring fewer bits for faster computation but potentially less accuracy.

💡Giga flops and GigaOps

Giga flops (GFLOPS) and GigaOps are terms used to measure computational performance. One gigaflop is equal to one billion (10^9) floating-point operations per second, while GigaOps refers to one billion operations per second. These metrics are used to gauge the speed and efficiency of processors, especially in the context of machine learning and data processing tasks.

💡Nvidia Blackwell

Nvidia Blackwell is a reference to an announcement made by Nvidia, a leading company in the field of GPUs and AI technology. In the context of the video, it refers to the introduction of new formats for floating point precision, specifically FP6 and FP4, which are aimed at accelerating machine learning workloads while maintaining accuracy.

💡Microscaling

Microscaling is a technique that enhances the efficiency of reduced precision formats like FP4 by using additional bits to scale the range of numbers. This scaling factor allows for a more accurate representation of the data within a specific range of interest, which is crucial for machine learning tasks. It effectively adjusts the 'zero point' of the number line to better fit the data being processed, thus improving the accuracy of computations.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to learn from and make predictions or decisions based on data. It is a rapidly evolving field with a wide range of applications, from image recognition to natural language processing. The video discusses how advancements in hardware, such as the use of quantization and microscaling, are crucial for the deployment of machine learning models, especially on low-power devices.

💡Inference

In the context of machine learning, inference refers to the process of using a trained model to make predictions or decisions based on new input data. It is the application phase where the model is used to understand or predict outcomes. Inference is typically computationally intensive, and优化 techniques like quantization and microscaling can make it more efficient, allowing for real-time or near-real-time predictions on low-power devices.

💡Reduced Precision Formats

Reduced precision formats are numerical representations that use fewer bits than traditional full precision formats. They are employed to optimize the trade-off between computational efficiency and accuracy. By reducing the number of bits used to represent numbers, these formats can increase the speed of computations, which is particularly beneficial for high-performance computing tasks in machine learning.

💡Standardization

Standardization in the context of computing and machine learning refers to the establishment of common rules, guidelines, or specifications that are followed by hardware and software developers. This ensures compatibility, consistency, and interoperability among different systems and components. The video discusses the need for standardization in the rapidly evolving field of machine learning, especially with the introduction of new reduced precision formats.

💡Frameworks

In machine learning, frameworks are software libraries that provide an environment for developers to build and train models with relative ease. They often abstract away the complexities of low-level operations, allowing users to focus on higher-level tasks. Examples of popular frameworks include TensorFlow and PyTorch. The video touches on the fact that while these frameworks are useful for learning and understanding, extracting the maximum performance may require more specialized and complex tools.

💡Programming Complexity

Programming complexity refers to the difficulty involved in writing, understanding, and maintaining software code. In the context of the video, it highlights the challenges that programmers face when dealing with the fundamental aspects of mathematics and the intricacies of new reduced precision formats. The need for clear guidelines and industry standards is emphasized to simplify these complexities and make them more accessible.

Highlights

The introduction of quantization in machine learning, which uses smaller numbers and bits to increase computational efficiency.

The concept of Giga flops, GigaOps, TerraOps, and PetaOps in computing, which are measures of computational performance.

The shift towards reduced precision formats like FP16, bfloat16, and INT8 in machine learning for speed improvements without compromising accuracy.

Nvidia's announcement of two new formats, FP6 and FP4, for further accelerating math workloads in machine learning.

The challenge of representing floating-point numbers with limited bits, such as the constraints and possibilities of using only two bits for a range of numbers.

The potential of FP6 and FP4 formats in enabling machine learning inference on low-power devices while maintaining accuracy.

The ongoing research to determine the accuracy and practicality of low-precision formats like FP6 and FP4 for everyday use.

The introduction of microscaling, a technique to enhance the representation of numbers in reduced precision formats, allowing for better accuracy within a specific range.

The concept of microscaling, which involves using additional bits as a scaling factor to adjust the range of numbers for more accurate computations.

Nvidia's approach to supporting 32, 16, 32, and 64 FP4 values with a single 8-bit scaling factor, improving efficiency in machine learning computations.

The demonstration of microscaling by Microsoft Research in their MSFP12 format, which influenced the development of similar techniques in the industry.

The industry's need for standards in reduced precision formats to ensure consistency and compatibility across different architectures and processors.

The role of the IEEE standards body in establishing norms for floating-point representations, including work on FP16 and FP8 formats.

The challenge of maintaining mathematical consistency when different manufacturers implement reduced precision formats in various ways.

The potential for the industry to come together to define clear guidelines and best practices for the implementation and use of reduced precision formats.

The necessity for simplified explanations and standards to help programmers understand and effectively use reduced precision formats in their work.

The impact of reduced precision formats on the performance and cost-effectiveness of machine learning applications, with potential savings in millions of dollars.

Transcripts

so in the world of machine learning

there's a process of quantization this

is the ability to use smaller numbers

that use smaller bits to increase the uh

the amount you can compute in any given

uh you know set of time so when we're

talking about Giga flops and Giga Ops

and Terra Ops and Peta Ops this is the

ability to take reduced Precision format

math and just accelerate it multiple

times than it would do if it was the

full double Precision that we're all

used to in Pro programming now in

machine learning numbers like fp16 brain

flow 16 in8 have been all the rage of

late because they've offered substantial

speedups while giving the same accuracy

well Nvidia in their latest announcement

of Blackwell have showcased two new

formats coming that can be used to help

accelerate some of those math workloads

however there's a

Twist what Jensen hang announced at the

GTC event is support for fp6 an fp4

formats this means floating Point

Precision in six bits and 4 bits with a

goal that you can just get many more

operations if you're using fewer bits

however there's a problem in a floating

Point number format you have four bits

to play with one of those is a sign bit

is it positive is it negative then you

have another bit to basically say if

you're an infinity or not and then that

leaves you with two bits to go around

the whole range of numbers and this is a

floating Point format so you got to

support decimals with only two bits in

this format 1 +

0.5 equal 2 I'll put a list on the

screen there is literally only about six

operations you can do with this format

but the goal here is that that's enough

to do some machine learning and a

particularly inference the ability to

take these large models and uh be able

to use them on devices with low power

with a low amount of math and still be

accurate now research is still being

done to see whether fp6 or fp4 these low

Precision formats are as accurate as

they need to be for everyday use however

what the key thing that Nvidia have

announced with this chip is micros

scaling micros scaling is important and

it's something that we saw come out of

Microsoft research a few years ago this

is instead of having the four bits to

represent your number you also use

another eight bits as a scaling Factor

the way I like to describe this is say

you're doing a bunch of math and your

accuracy needs to be between 0o and 10

um that's fine because you're you know

at the root of the number line you're

standing you your numbers start from

zero and they spread out from there

however if your numbers are between 3,00

and

3010 you have no accuracy you have no

range and your math is isn't going to

work what if you could take your region

of interest on the number line over to

start essentially start at the number

3,000 so that 3,00 310 contains all your

math and you've essentially scaled your

accuracy and your range to that number

this is the point of this micros scaling

feature now it was again as I said

demonstrated by Microsoft thought of by

Microsoft at least that's where I found

it first in this format called msfp 12

so you have an fp4 format and you have

this 8 bit scaling but that scaling

factor in those 8 Bits actually applied

to 12 different fp4 values that means

you only have to pay the penalty of

those eight bits once now what Nvidia is

doing here is something similar however

you can Support 32 or 16 32 and 64 fp4

values if I remember correctly with one

of these 8 bit scaling features and the

point is if if you have 32 64 128 or

10,000 operations in this scaled region

of interest that apply to all the

numbers in that machine learning Matrix

you only pay that scaling penalty once

this makes features like fp4 and fp6

able to scale up and down the number

line where the accuracy is needed we've

seen it on two other processors one of

them is Tesla dojo and they had a really

good slide I'll throw it up on the

screen showcasing that they can support

ranges from 2 to the 64 all the way up

to 2 to the 64 with this uh scaling

format and also Microsoft on the Maya AI

100 chip um exact details we still need

to learn about but they support this

sort of scaling Factor as well it's

becoming one of the requirements one of

the standards in the industry as part of

reduced Precision formats the only

difficulty here is kind of like with fp8

if you're familiar with the with the

industry and all these different formats

you'll know that there's about eight

different versions of fp8 and what I

mean by this is when you have a number

format even a standard number format

like a standard fp64 double Precision

you need to know where your infinities

are you need to know where your not

numbers are you need to understand what

happens when you do division by Zero D

normals there are some nor formats that

have a positive zero and a negative zero

you know get around that for a second

when you have start playing with these

reduced Precision format everybody's

doing something slightly different which

makes some of the consistency in the

mathematics also very difficult to

manage between architectures we have a

standards body called i e that deals

with these standards we have i e uh

stand I think it's i e 754 for fp64 and

fp32 they're working on 16 bit and I

believe they're also working on 8bit

it's a slow standards body to catch up

and machine learning is a very very fast

moving industry which means that

standards like this fp4 and this micro

scaling I do think we need to come up

with better names to describe what we're

doing here uh but this is going to be

some of the standards moving forward as

AMD launches you know their next Rd uh

cdna 4 or we have uh more from Intel in

the in the galdi processor line we're

also going to see a large number of

these uh cut down uh quantized reduced

Precision format and some of these

scaling formats um it's going to be

really interesting to see where

everybody everybody ends up when the

dice stop moving my minimum station here

is this needs to be simplified for the

programmers who are in the weeds who are

dealing with the math on a very

fundamental basis it's very difficult

and very complex very quickly one could

argue that some of this is abstracted

away through the framework such as

tensor flow and py T however in speaking

with a lot of companies dealing with

these large models while tensorflow and

Pie torch are great for Learning and

they're great for understanding if you

need to extract every jeel of

performance you may be using something a

bit more complicated there's a barrier

to entry with that in terms of uh skill

and talent and knowledge but the

benefits out of it are millions of

dollars so with these reduced Precision

formats we need clearcut guidelines how

they're being implemented and what it

means I'll show up that graph again of

just the six six or so operations you

can do with this fp4 format so as long

as those are well defined and everybody

understands them we need this industry

to come together and find the right way

to explain how these work and why these

work