Hi, I’m Joshua Newnham,
an engineer on the Core ML team. Today, I'm excited to introduce you
to some new features in Core ML to help you efficiently deploy
and run your machine learning and AI models on-device. Running models on-device opens up exciting possibilities
for you to create new forms of interactions,
powerful professional tools, and insightful analysis
from health and fitness data, all while keeping personal data
private and secure. Thousands of apps have used Core ML
to create amazing experiences powered by on-device ML, and so can you! In this video,
we’ll start by reviewing Core ML’s role in the model deployment workflow. Next, we’ll dive into
some exciting features, starting with a new type
to simplify model integration. Then, explore using state
to improve model inference efficiency. After that, we’ll introduce multi-function models
for efficient deployment. And finally, review updates
to Core ML performance tools to help you profile and debug your models.

Let’s begin. The machine learning workflow
consists of three phases: model training, preparation,
and integration. In this video, we’ll focus on integrating and running your machine learning
and AI models on-device. To learn about training,
I recommend watching this year’s video on training ML and AI models
on Apple GPUs. And to learn about converting
and optimizing your models, check out this year's video
on bringing your models to Apple silicon. Model integration
begins with an ML package, the artifact created
during the preparation phase. From here, Core ML makes it easy to integrate
and use this model in your app. It is tightly integrated with Xcode and offers a unified API
for performing on-device inference across a wide range of machine learning
and AI model types. Models are executed using Apple silicon’s
powerful compute capabilities, dispatching work across the CPU,
GPU, and Neural Engine. It does this with the help
of MPS Graph and BNNS Graph.

two other machine learning frameworks,
which can also use Core ML models. Most suitable when your use case requires
either tight integration with Metal or real-time inference on the CPU. Check out the relevant videos
to learn more. With significant improvements
across the inference stack, Core ML delivers
even better performance this year. For example, when comparing
the relative prediction time between iOS 17 and 18, you’ll observe that iOS 18
is faster across many of your models. This speed-up comes with the OS and doesn’t require
recompiling your models or changing any of your code. It also applies to other devices, although the speed-up depends
on the model and hardware. With the model converted, we are ready to explore
how to integrate and run it efficiently. Integrating a model into your app can be as simple
as passing in the required input and reading the returned output. However, complexity can quickly grow
for more advanced use cases. For example,
generative AI is often iterative.

and can involve multiple models. In these use cases,
there exists computation outside the model, which serves as the glue or stitching necessary to run an end-to-end pipeline. Supporting this computation often requires
implementing operations from scratch or using various low-level APIs, both leading to lengthy and complex code. That is, until now. Introducing MLTensor,
a new type in Core ML that provides a convenient
and efficient way to support this computation. MLTensor offers many common math and transformation operations,
typical of machine learning frameworks. These operations are executed using Apple silicon’s
powerful compute capabilities, ensuring high-performance execution. And it resembles
popular Python numerical libraries, making it easier
and more intuitive for you to adopt if you’re already familiar
with machine learning. Let’s explore the API
by walking through some examples. To start, we will create
a couple of tensors to play with. There are various ways
of creating a tensor; we’ll present two here.

The first creates a tensor from a MLShapedArray, and the second uses a nested collection of scalars. Like MLShapedArray, MLTensor is a multi-dimensional array defined by its shape and scalar type, where the shape specifies the length of each axis and scalar type indicates the type of elements it holds. Now we know how to create tensors, let’s explore some ways to manipulate them. Starting with some basic math. Tensors support a broad range of operations. In this example, we perform an element-wise addition and multiplication, then compute the result’s mean. Tensors seamlessly work with literals, and the framework automatically broadcasts compatible shapes. A Boolean mask is then created by comparing the result to its mean. This mask is multiplied by the result to create a filtered version, zeroing out any values where the mask is false. Next, we demonstrate how to index and transform a tensor’s shape. Similar to Python numerical libraries, you can slice the tensor by indexing into each dimension.

In this example, we take the first row of the matrix and expand it back out using the reshape method. All tensor operations are dispatched asynchronously. It’s for this reason, a tensor must be explicitly materialized to a MLShapedArray before its underlying data can be accessed. Doing so ensures that all upstream operations are complete and data is available. Now that we’ve seen how to create and manipulate tensors, let’s turn our attention to something more interesting. Let’s explore how MLTensor can simplify integrating large language models. We’ll start by briefly introducing our example model and its outputs. The model is an autoregressive language model, a model trained to predict the next word, or token, based on the context of the preceding words. To generate a sentence, the predicted word is added back to the input, and the process repeats until it detects an end-of-sequence token or reaches a set length. However, language models don’t output a single word.

they output scores
for all words in a vocabulary, where each score represents
the model’s confidence in that word coming next. A decoder uses these scores
to select the next word using various strategies, such as choosing the word
with the greatest score, or randomly sampling
over an adjusted probability distribution. The ability to change the decoding method means this step is typically kept separate
from the model, making it an ideal candidate for MLTensor. Let’s see just how
by comparing implementations for a couple of decoding methods before and after
the introduction of MLTensor. In this demo,
I will use a modified version of HuggingFace’s Swift Transformer package
and Chat app, along with the Mistral 7 billion model converted and optimized
in the preparation video. Before we compare
the decoder implementations, let’s see the model in action by prompting it
to generate some title ideas for a fictional children’s story
about a superhero Corgi. It’s a great start,
but I would like something more creative.

The application defaults to selecting the most probable word, a method known as greedy decoding. Let’s explore another decoding method. By enabling top-k sampling, the application will select the next word by randomly sampling over the top-k most probable words, instead of always selecting the word with the greatest score. In addition to randomly sampling the most probable words, we can also influence the probability distribution by adjusting the temperature. A high temperature flattens the distribution, resulting in more creative responses, while a lower temperature has an opposite effect, leading to more predictable outputs. Let’s re-run the model using top-k sampling with a temperature of 1.8. That’s a little more interesting. Now we have seen the results of two decoding methods, let’s compare their implementations with and without MLTensor. When comparing the implementations, one striking difference between the two is how little code is required to achieve the same functionality with MLTensor. This is not to say the original version is wrong.

or that low-level APIs are unnecessary. On the contrary,
this is well-written and performant code, and there are many instances
where low-level APIs are required. But for many common
machine learning tasks, MLTensor can offer a concise alternative, allowing you to focus more
on creating great experiences and less on low-level details. In the previous section,
we learned how MLTensor simplifies decoding outputs
from a language model. In this section, we’ll introduce state and explore how it can be used
to reduce the time it takes to generate each word
from the language model. Let’s begin
by first describing what we mean by state. It’s likely that most of the models
you’ve interacted with are stateless. That is,
they process each input independently, without retaining any history. For example, an image classifier
based on a Convolutional Neural Network is stateless as each input is processed
in isolation from the previous one. This contrasts with stateful models which

as you may have guessed,
retain history from previous inputs. Architectures like Recurrent
Neural Networks used for sequence data are one example of a stateful model. Today, stateful models can be supported
by managing state manually, where the state is passed in as an input, and the updated version
retrieved from the output ready for the next prediction. However, loading and unloading
the data used for state at each time-step incurs some overhead. This overhead can become noticeable
as the size of the state increases. This year, Core ML has improved
its support for stateful models. Rather than maintaining state manually,
Core ML now can do this for you and works to reduce some of the overhead
previously mentioned. Let’s now turn our attention
to one model type where state can be beneficial. In the previous section,
we learned that language models output scores for all words
in a vocabulary, where the score indicates the confidence
the model assigns

for that word coming next. In addition to scores, the model used also output key
and value vectors for the given word. These vectors are computed for each word and used by the attention mechanism
embedded throughout the network to improve the model's ability to generate natural
and contextually relevant outputs. To avoid recomputing
previous word vectors at each step, they are often stored and reused. This is referred to
as the key-value cache, or KV cache for short. This cache sounds like an ideal candidate
for our new feature. Let’s see how. Instead of handling the KV cache
using the model inputs and outputs, you can now manage it
using Core ML states. This can reduce the overhead
and improve inference efficiency, leading to faster prediction times. It should be noted that support for states must be explicitly added to the model
during the preparation phase. To learn how, check out the video
on bringing your models to Apple silicon. You can easily verify
if a model has states.

by inspecting the model in Xcode. When states are available, they will appear
just above the model inputs in the predictions tab
of the model preview. Now, let’s walk through updating the code used for next word prediction
to support state. We’ll begin by reviewing
the stateless version to highlight the necessary changes. Focusing just on the relevant parts, we start by creating an empty cache
to store the key and value vectors. This cache is then provided to the model
as an input, and updated using the values
returned by the model. Let’s now walk through
the necessary changes to support state. Most of the code will look familiar
with some minor adjustments. Instead of manually preallocating
each state, the model instance is used to create them. In this example,
Core ML preallocates buffers to store the key and value vectors
and returns a handle to the state. With this handle,
you can access these buffers and control state’s lifetime.

Rather than passing in each cache
as an input, we now simply pass in the state
created by the model instance. And since the update
is performed in-place, we can omit the last step
of updating the cache. And that’s it! We’ve updated the code to support states and taken advantage of the speed-up
in predicting the next word. Here is a quick comparison
between the KV cache implementations using the Mistral 7 billion model
running on a MacBook Pro with an M3 Max. The left shows KV cache implemented
without state, and the right with state. We see the right one
finishes around 5 seconds, while the left took
approximately 8 seconds. That’s a 1.6x speedup when using state. Performance will, of course, vary
depending on your model and hardware, but this gives you a general sense of the gains you can expect
when using state. In this next section,
we’ll explore a new feature in Core ML.

that provides a flexible and efficient way to deploy models
with multiple functionalities. When we think of a machine learning model, we usually think of something
that takes in an input and produces some output,
much like a function. Functions are, in fact, how neural networks are represented
in Core ML. They usually consist of a single function
containing a block of operations. A natural extension to this
is to support multiple functions, a feature now available in Core ML. Let’s walk through a concrete example,
to demonstrate how we can use this feature to efficiently deploy a model
with multiple adapters. In case you’re unfamiliar with adapters, they are small modules
embedded into an existing network and trained with its knowledge
for another task. They offer an efficient way to extend the functionality
of a large pre-trained model without adjusting its weights. This allows a single base model
to be shared across multiple adapters. For our example, we’ll use an adapter
to influence the style of the images.

generated by a latent diffusion model. But what if we wanted to deploy
more than one style? Today, we would deploy
two or more specialized models, one for each adapter,
or pass in the adapter weights as inputs. Neither approach is ideal. With support for multiple functions,
we now have another, more efficient option. We can now merge multiple adapters
with a shared base into a single model, exposing a function for each. Check out the video
on bringing your models to Apple silicon to learn how to export a model
with multiple functions. Let’s review the code required
to load a model with a specific function. Loading a multi-function model is simply a matter
of specifying the function name. Once loaded,
calling prediction on the model will invoke the specified function, or its default if no function is given. Let’s see this in action. In this demo, I will use the open-source
Stable Diffusion XL model and modified Diffusers app
from HuggingFace.

to generate images from text. The pipeline consists of multiple models, seamlessly stitched together
using MLTensor. This includes a Unet model
with two functions, each using different adapters that influence the style
of the generated image. We can preview
the available functions for a model by opening it in Xcode. This model has two functions,
sticker and storybook, and since they have
identical inputs and outputs, we can use the same pipeline for both. But this is not required, and it's possible that each function
has different inputs and outputs. Let’s return to the app and generate a sticker
for our superhero Corgi. This is great! Let’s now switch to the other function
to get a different style of our superhero. Nice! In this demo, I have shown you
how to deploy a single model with multiple adapters, each accessed
using their respective function. This feature is versatile and can be used
for many other scenarios.

I’m excited to see how you use it. In this final section, we will briefly look at some enhancements and new tools to help you better profile and debug your models. Starting with updates to the Core ML performance report. Performance reports can be generated for any connected device without having to write any code. Simply open a model in Xcode, select the performance tab, click the + button to create a new report, select the device you want to profile, and compute unit to run on before hitting the Run Test button. Once complete, the report displays a summary of load and prediction times along with a breakdown of compute unit usage. This year, the performance report now offers even more information, specifically estimated time and compute device support for each operation. Estimated time shows the time spent on each operation. It is calculated using the median prediction time multiplied by the estimated relative cost for each operation. It is helpful to identify bottlenecks in your network.

Which can be easily done
by sorting operations based on their estimated time. In addition, you can now hover over
unsupported operations to reveal hints
for why it wasn’t able to run on a specific compute device. In this example, the hint suggests
the data type is not supported, allowing us to return
to the preparation stage to make any necessary changes to ensure our model is compatible
with all compute devices. For this model, it was simply a matter
of updating the precision to Float16. Along with more information,
performance reports can now be exported and compared against other runs. Comparing runs makes it easy to review
the impact of changes to your model, all without having to write
a single line of code in your application. Talking of code, sometimes it’s just more convenient
to work with it. With the introduction
of the MLComputePlan API, you can do just that. Like the performance report,
the MLComputePlan offers model debugging.

and profiling information for Core ML. The API surfaces the model structure and runtime information
for each operation, including the supported
and preferred compute devices, operation support status, and estimated relative cost,
as described earlier. And with that, we can now wrap up this video
to summarize. New features in Core ML
can help you efficiently deploy and run machine learning and AI models on-device. You can now adopt MLTensor
to simplify model integration. Take advantage of state
to improve inference efficiency. Use multi-function models
to efficiently deploy models with multiple functionality. And utilize the new information
available in the performance tools to profile and debug your models. I look forward to seeing
what new experiences you make by leveraging powerful
on-device machine learning.

WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple

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WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple

WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple

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