Making AI More Accurate: Microscaling on NVIDIA Blackwell
Summary
TLDRThe 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
📈 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.
📚 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.
Mindmap
Keywords
💡Quantization
💡Floating Point Precision
💡Giga flops and GigaOps
💡Nvidia Blackwell
💡Microscaling
💡Machine Learning
💡Inference
💡Reduced Precision Formats
💡Standardization
💡Frameworks
💡Programming Complexity
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
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