使用tf.data、tf.function和tf.distribution在TensorFlow 2中建立高性能、可擴展的模型 (TF World '19) (Performant, scalable models in TensorFlow 2 with tf.data, tf.function & tf.distribute (TF World '19))

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TAYLOR ROBIE: I'm Taylor.
I'm a engineer on the TF Keras team.
PRIYA GUPTA: And I'm Priya.
I'm also an engineer on that in TensorFlow team.
I lead the distribution strategy team.
TAYLOR ROBIE: And today we're going
to be talking talking about writing performant models
in TensorFlow 2.
Just to give a brief outline of the talk,
we're, first, going to talk about the structure
of a training loop in TensorFlow 2.
And then we're going to talk about the different APIs
that you can use to make your training more performant.
The first API that we're be talking about is tf.data.
This is the API for writing high performance pipelines
to avoid various sorts of stalls and make sure
that your training always has data
as it's ready to consume it.
Next we going to be talking about tf.function.
This is how you can formulate your training step
in a way that is efficient so that the runtime can
run with very minimal overhead.
And, finally, we're going to be talking about tf.distribute
which is how to target your training to specific hardware
and also scale out to more hardware.
In order to demonstrate all of these APIs,
we're going to be walking through a case study
starting with the most naive implementation
and then progressively adding more performant APIs
and looking at how that helps our training.
So we're going to be training a cat classifier.
Because we're an internet company, that's what we do.
And we're going to be training it on MobileNet V2.
This task was largely chosen arbitrarily
just as a representative task, so these lessons
should be broadly applicable to your workflows as well.
And there's a notebook.
And I'll put the link back at the end of the slide
so you're encouraged to take a look afterward and compare it
back to the talk.
So let's get started.
A brief overview of training loops in TensorFlow 2.
We're going to be writing a custom training loop.
And the reason is that we want to look
at the mechanics of what the system is actually doing
and how we can make it performant.
If you're used to using a higher level API,
like Keras model fit, these ap-- these lessons are still
broadly applicable it's just that some of this
will be done automatically for you.
So if we want to show why these APIs are important,
it's useful to really dig into the internals of the training
loop.
So because we're training on MobileNet
we've chosen to use the Keras Applications MobileNet
V2 which is just a canned MobileNet V2 implementation.
We're training a classifier.
So we're going to be using sigmoid cross entropy
with logits.
This is a numerically stable cross entropy function.
And then, finally, we're going to be using the Keras SGD
optimizer.
So if we were to call our model on our features,
in this case it's an image classification
task so our features are an image,
we would get logics which is the predicted class.
If we then apply our loss function
comparing the labels and the logits,
this gives us a scalar loss which
we could then use to optimize and update our model.
We want to call all of this under a tf.GradientTape.
What the gradient tape does is it watches the computations
as they're performed, and maintains the metadata,
and keeps the activations alive.
This is what allows us to take gradients
which we can then pass to the optimizer
to update our model weights.
And, finally, if we wrap all of this into a step
and then iterate over our data in many batches,
applying this step in each mini batch,
this comprises the general structure of our training loop.
On the data side, we're going to start out with just a straw man
Python generator to show the different parts of the data
pipeline and then we'll look at its performance a little bit
later.
So what do we need to do?
Well, first, we'll want to shuffle
the data so that we see a different ordering each epoch.
We'll need to load all of the images from disk into memory
so that they can be consumed by the training process.
We'll also want to do some augmentation.
So we'll want to resize the images from their native format
down-- back to the format that the model expects.
And then we'll do some task-specific optimization.
So, in this case, we'll randomly flip the images
and we'll add some pixel level noise
to make our training a little bit more robust.
And, finally, we'll need to batch these.
So the generator that I've shown is
producing examples one at a time,
but we'll need to collect them into mini batches.
And the key operation for that batching operation
is a concatenation.
It's worth noting that different parts of the data pipeline
will stress different parts of the system.
So for loading the disk-- this is an I/O-bound task
and we'll generally want to consume this I/O as fast
as possible so that we're not constantly waiting for images
to arrive from disk one at a time.
Secondly, the augmentations tends
to be rather CPU intensive because we're
doing various sorts of math in our augmentation.
And, finally, batching tends to be somewhat memory
intensive task because we're copying examples
from their original location into the memory
buffer of our mini batch.
So now that we have a scaffolding,
let's just go ahead and run this.
We're going to be running on an Nvidia V100 GPU.
By default, TensorFlow will attempt to place ops
on the optimal device.
And because we're doing large matrix multiply operations,
it will place them on the GPU.
However, we find that our training, at the start,
is pretty lackluster.
We're under 100 images a second.
And if we actually look at our device utilization,
it's well under 20%.
So what's going on here?
Well, in order to determine that,
we can actually do some profiling.
And TensorFlow 2 TensorBoard makes this quite easy to do.
You might already be familiar with TensorBoard.
This is the standard way of monitoring training as it runs.
So you might be, for instance, familiar
with the scalars tab which will show things
like losses in metrics as your training continues.
Well, in TensorFlow 2 there's a new tab
and that is the profiler tab.
And what this does is it looks at each op
as it runs in TensorFlow and shows you a timeline of how
you're training is progressing.
And we're going to look in a little bit more detail
about how to read and interpret these timelines.
Before we do, however, let's talk about how to enable it.
So it's quite straightforward.
You simply turn on the profiler.
This will signal to the runtime that it should keep
records of ops as it runs them.
Write your program as normal, and then simply
export this trace, and this will put it in a format
that TensorBoard can consume and display for you.
So this is a timeline.
Moving left to right, we have time
and then each row is a different thread.
And you can see that they're annotated with device.
So the top row is the CPU and then
the bottom ones are the various GPU threads.
And, in this case, we're looking at three different training
steps.
Each vertical line is an individual op
that's been run by TensorFlow.
So you can see that the ops are scheduled from the CPU
and then they appear on the GPU stream.
And the GPU actually executes the ops and this is where
the heavy lifting is done.
But you can also see that we have
these large gaps in between where no ops are running
and our system is essentially stalled.
And this is because we have--
after each step we have to wait for our naive generator
to produce the next batch of data,
so this is obviously quite inefficient.
So Priya's going to tell you how we can improve that.
PRIYA GUPTA: Thanks, Taylor.
So as Taylor just showed us, writing your own input pipeline
in Python to read data and transform
it can be pretty inefficient.
TensorFlow provides the tf.data API
to allow you to easily build performance and scalable input
pipelines.
You can think of the tf.data input pipeline
as an ETL process.
So the first stage is the extract stage
where we read the data from, let's say, network storage
or from your local disk, and then you potentially
are parsing the file format.
The second stage is the transform stage,
which is where you take the input file data and transform
it into a form that's amenable to your ML computation.
So this could be image transformations
specific to your ML task, or there
could be generic transformations like shuffling or batching
that apply to a lot of ML tasks.
Once you transform this data, you
can then load it into your accelerator for your training.
So let's take a look at some code
to see how you can use tf.data to write the same input
pipeline that Taylor showed you before but in a much more
efficient way.
So there's a number of steps here,
and I'll go over them one by one.
The first one is we create a simple data
set consisting of all the filenames in our input.
So in this case, we have a number of image files,
and the PATH_BLOB specifies how to find these filenames.
The second thing we do is we want
to shuffle the filenames because in training typically
you want to shuffle the input data as it's coming in.
And we apply a transformation called the map transformation.
And we provided this get_bytes_and_label custom
function.
And what this function does is that it's going to read
the file one by one using the tf.io.read_file API.
And it uses the filename path to compute the label
and returns both of these.
So these three steps here comprise the extract stage
that I talked about before.
We're reading the files and passing the file data.
Note that this is just one way in which you
can read data using tf.data, and there
are a number of different APIs that you
can use for other situations, which we have listed here.
The most notable one is the TFRecordDataset API,
which you would use if your data is in the TFRecord file format.
And this is potentially the most performant format
to use with tf.data.
If you have your input in memory in a NumPy array--
let's say you can also use the from_tensor_slices API
to convert it into a tf.data data set
and do subsequent transformations like this.
The next thing we do is another map transformation
to now take this raw image data but convert it and do
some processing to make it amenable for our training task.
So we have this process_image function here
which we provide to this map transformation.
And if you take a look, this could look something like this.
We do some decoding of the image data,
and then we apply some image-processing transformation
such as resizing, flipping, normalization, and so on.
The key things to note here are these transformations
are actually very similar and correspond one to one to what
you saw before in the Python version,
but the big difference here is that now instead you're
using TensorFlow ops to do those transformations.
The second thing is that tf.data will take the custom function
that you specify to the map transformation and it will
trace it as a TensorFlow graph and run it in the C++ runtime
instead of Python.
And this is what allows it to make it much more efficient
than Python.
And finally we use the batch transformation
to batch the elements of your input into mini batches,
and this is a very common practice for training
efficiency in ML tasks.
So before I hand it off to Taylor
to talk about how using tf.data can improve
the performance in our MobileNet example,
I want to walk through a few more advanced performance
optimization tips for tf.data.
And I'll go through them quickly,
but you can also read about them in much more detail
on the page that's listed here.
The first optimization that I want to talk about
is pipelining.
And this is conceptually very similar to any other software
pipelining that you might be aware of.
The high-level idea here is that when
you're training a single batch of data on your accelerator,
we want to use the CPU resource at the same time
to process and prepare the next batch of data.
What this will do is that when the next training step starts,
we don't have to wait for the next batch of data
to be prepared.
It will automatically be there, and this
can reduce the overall training time significantly.
To use software pipelining in tf.data is very simple.
You can use the prefetch transformation as shown here.
The second optimization that I want to talk about
is paralyzing the transformation stage.
By default, the map transformation
will apply the custom function that you
provide to each element of your input data set in sequence.
But if there is no dependency between these elements,
there's no reason to do this in sequence, right?
So you can parallelize this by passing
the num_parallel_calls argument to the map transformation.
And this indicates to the tf.data runtime
then it should run these map operations
on your elements of the data set in parallel.
And the third optimization that I want to talk about
is parallelizing the extraction stage.
So similar to how transforming your elements in sequence
can be slow, similarly reading files one by one
can be slow as well.
So, of course, you want to parallelize it.
And in this example, since we are using a map transformation
to read our files, the way you do it
is actually very similar to what we just saw.
You add a num_parallel_calls argument to the map function
that you have to read your files.
Note that if you're using one of the built-in file
readers such as the TFRecordDataset,
you can also provide very similar arguments
to that in order to parallelize the file reading there.
So if you've been paying close attention,
you'll notice that we have these magic numbers X, Y,
and Z on the slides, and you might be wondering,
how do you determine the optimal values of these?
And in reality, it's actually not
very straightforward to compute the optimal values
of these parameters because if you set them too low,
you might not be using enough parallelism in your system.
And if you set them too high, it might lead to contention
and actually have the opposite effect of what do you want.
Fortunately, tf.data makes it really easy
for you to specify these.
Instead of specifying specific values
for these X, Y, Z arguments, you can simply
use this constant tf.data experimental AUTOTUNE.
And what this does is it indicates to the tf.data
runtime that it should do the autotuning for you
and determine the optimal values for these arguments
based on your workload, your environment, your setup, and so
on.
So that's all for tf.data.
I'm now going to hand it off to Taylor
to talk about what kind of performance benefits
you can see.
TAYLOR ROBIE: Thanks, Priya.
So on the right here we can see the timeline before and after
we add tf.data.
So before we have this long stall
in between our training steps where we're waiting for data.
Once we've used tf.data, you'll note two things
about this timeline after.
The first is that the training step begins immediately
after the previous one, and that's
because tf.data was actually preparing the upcoming batch
while the previous training step was happening.
The second is that before, it actually took
longer than the time of a batch in order
to prepare the next batch, whereas now we
see no large stalls in the timeline.
The reason is that tf.data, because
of the native parallelism, can now produce batches
much more quickly than the training can consume them.
And you can see this manifest in our throughput with more than a
2x improvement.
But sometimes there are even more stalls.
So if we zoom in on the timeline of one of our training steps,
we'll see that there are a number of these very
small gaps, and these are launch overheads.
So if we further look at different portions of the GPU
stream, near the end, this is what
a healthy profile looks like.
You have one op runs and then finishes,
and immediately the next op can begin,
and this results in an efficient and saturated accelerator.
On the other hand, in some of the earlier parts of the model,
an op is scheduled on the GPU.
The GPU immediately chews through it,
and then it simply waits idle for Python
to enqueue the next op.
And this leads to very poor accelerator utilization
and efficiency.
So what can we do?
Well, if we back to our training step,
we can simply add the tf.function decorator.
What this will do is this will trace the entire training
step into a tf.Graph, which can then
be run very efficiently from the tf runtime.
And this is the only change needed.
And now if we look at our timeline,
we see that all of the scheduling
is done in this single, much faster op on the CPU,
and then the work appears on the GPU as before.
And this also allows us to use the rich library
of graph optimizations that were developed in TensorFlow 1.
And you can see this is again almost
another factor-of-two improvement,
and it's pretty obvious in the timeline on the right why.
Whereas before tf.function when you were running everything
eagerly we had all of these little gaps waiting for ops,
now once it's compiled into a graph and launched from the C++
runtime, it's able to do a much better job keeping up with
the GPU.
So the next optimization that I'm going to talk about
is XLA, which stands for Accelerated Linear Algebra.
To understand how XLA works, we have just this simple example
of a graph with some skip connections.
What XLA will do is it will cluster that graph
into subgraphs and then compile the entire subgraph into a very
efficient fused kernel.
And there are a number of performance gains
from using these fused kernels.
So the first is that you get much more efficient memory
access because the kernel can use data
while it's hot in the cache as opposed to pushing
it all the way down the memory hierarchy
and then bringing it all the way back.
It also reduces the overhead from launch overhead--
this is the C++ executor launch overhead--
by running fewer ops but the same math.
And finally, XLA is heavily optimized to target hardware.
So it does things like use efficient hardware-specific
vector instructions, specialize on shapes,
and choose layouts so that the hardware is able to have
very efficient access patterns.
And it's quite straightforward to enable.
You simply use this tf.config.optimizer.set_jit
flag.
And this will cause every tf.function
that runs to attempt to compile and run with XLA.
And you can see that, in our example,
it's a very stark improvements.
It's about a 2 and 1/2 x improvement in throughput.
The one caveat with XLA is that a lot
of the optimizations that it uses
are based on specializing on shapes.
So XLA needs to recompile every time it sees new shapes.
So if you have an extremely dynamic model-- for instance,
the shapes are different each batch--
you might actually wind up spending more time on the XLA
compile than you gain back from the more efficient computation.
So you should definitely try out XLA,
but if you see performance regression rather than
performance gain, it's likely that that's the reason.
So next we're going to talk about mixed precision.
If we want to go even faster, then we
can give up some of our numeric stability
in order to obtain faster training performance.
So here I have the IEEE float32, which
is the default numeric representation in TensorFlow,
but there are also two half-precision formats
that are relevant.
The first is bfloat16 where we keep all of the exponent
and simply chop off 16 bits of mantissa.
And the other is float16 where we give up both some exponent
in exchange for keeping a little bit more mantissa.
And what's important about these formats
is that they actually have native hardware support.
So TPU has hardware support for very efficient
bfloat16 operations, and GPUs have
support for very efficient float16 operations.
So if we can formulate our computation
in these reduced-precision formats,
we can potentially get very high speed-ups from the hardware.
In order to enable this, we need to do a couple of things.
First, we need to choose a loss_scale.
So what is a loss_scale?
A loss_scale is a constant that's
inserted into the computation which doesn't change
the mathematics of the computation,
but it does change the numerics.
And so this is a knob where the runtime can
adjust the computation to keep it
in a numerically stable range.
The next thing we'll want to do is
we want to set a mixed_precision policy.
And this will tell Keras that it should cast tensors
as they flow through the model in order
to make sure that the computation is actually
happening in the correct floating-point representation.
In a custom training loop, we'll want
to wrap our optimizer in this loss_scale optimizer,
and this is the hook by which the loss_scale is inserted.
So as training happens, TensorFlow
will do a dynamic adjustment of this loss_scale
to balance vanishing gradients from FP16 under flow
while still preventing NaNs from FP16 overflow.
If you're using the Model.fit workflow,
this will be done for you.
And finally, we generally need to increase our batch size
when doing this mixed-precision training.
The reason is that mixed precision
makes computing a single example much less expensive.
So if we just turn on mixed precision,
we can go from a saturated accelerator in float32
to an underutilized accelerator in float16.
So by increasing the batch size, we
can go back to filling all of the hardware registers.
And we can see this in our example.
If we just turn on float16, there's actually
no improvement in performance.
But if we then increase our batch size,
then we see a very substantial improvement in performance.
It is worth noting that because we've both
reduced the numeric precision and changed the batch size,
this can require that you retune the hyperparameters.
And then finally if we look at what
are the remaining bits of performance, so about 60%
of what's left is actually the copy
from the host CPU to the GPU.
Now in a little bit Priya's going
to talk about distribution, and one
of the things that the distribution-aware code
in TensorFlow will do is it will automatically
pipeline this prefetch.
So you'll actually get that 60% for free.
And then finally you have hand tuning.
So you can give up some of the numeric stability.
You can mess with thread pools.
You can manually try and optimize layouts.
This is included largely for completeness in case
you're curious what real low-level hand tuning
looks like.
But for most cases, given that we
can get the vast majority of the performance with just simple,
idiomatic, easy-to-use APIs, this very fine hand tuning
is not recommended.
But once we've actually saturated a single device,
we need to do something else to get
another order-of-magnitude improvement.
And so Priya's going to talk to you about that.
Thanks,
PRIYA GUPTA: Taylor.
All right, so Taylor talked about a number
of different things you can do to get the maximum performance
out of a single machine with a single GPU.
But if you want your training to go even faster,
you need to start scaling out.
So maybe you want to add more GPUs to your machine,
or perhaps you want to train on multiple machines in a cluster,
or maybe you want to use specialized
hardware such as cloud TPUs.
TensorFlow provides distribution strategy API
to allow you to do just that.
We built this API with three key goals in mind.
The first goal is ease of use.
We want users to be able to use the distribute.Strategy API
with very little changes to their code.
The second goal is to give great performance out of the box.
We don't want users to have to change their training code
or tune a lot of knobs to get the maximum efficiency out
of their hardware resources.
And finally, we want this API to work
in a variety of different situations.
So for instance, if you want to scale out
to multiple GPUs or multiple machines or TPUs,
or if you want just different distributed-training
architectures such as synchronous or asynchronous
training, or you're using different types of APIs--
so maybe you're using high-level Keras APIs like Model.fit,
or you have a custom training loop as in our example earlier.
We want distribution strategy to work in all
of these potential cases.
There are a number of different ways
in which you can use this API, and we've listed them here
in the order of increasing complexity.
The first one is if you're using Keras high-level API
model.fit for your training loop and you
want to distribute your training in that setup.
The second use case is what we've
been talking about in this talk so far where you have a custom
training loop and you want to scale out your training.
The third use case is maybe you don't have a specific training
program, but maybe you're writing a custom
layer or a custom library and you want
to make it distribution aware.
And finally, maybe you're experimenting
with a new distributed-training architecture
and you want to create a new strategy.
In the talk here, I'm only going to talk about the first two
cases.
So let's begin.
The first use case we'll talk about
is if you have a single machine with multiple GPUs
and you want to scale up your training to this situation.
For this setup, we provide MirroredStrategy.
MirroredStrategy implements synchronous training
across multiple GPUs on a single machine.
The entire computation of your model
would be replicated on each GPU.
All the variables of your model would be replicated on each GPU
as well, and they will be kept in sync using all-reduce.
Let me go through step by step to talk
about what the synchronous training looks like.
There was some gray boxes around these which are not
really visible, but let's say in our example
we have two devices or two GPUs, Device 0 and 1,
and we have a very simple model with two simple layers, Layer
A and B. Each layer has a single variable.
As you can see, the variables are mirrored or replicated
on these two devices.
So in our forward pass, we'll give
a different slice of our input data to each of these devices.
And in the forward pass, they will
compute the logits using the local copy of the variables
on these devices.
In the backward pass, each device
will then compute the gradients, again using the local copy.
Once each device has computer the gradients,
they'll communicate with each other
to aggregate these gradients.
And this is where all-reduce that I
mentioned before comes in.
All-reduce is a special class of algorithms
that can be used to efficiently aggregate tensors
such as gradients across different devices,
and it can reduce the overhead of such synchronization
by quite a bit.
There are a number of different such algorithms
available, and some hardware vendors such as Nvidia
also provide specialist implementations of all-reduce
for their hardware, such as the NCCL algorithm.
So once these gradients have been aggregated,
the aggregated result would be available on each device,
and each device can update its local copy of the variable
using this aggregated tensors.
So in this way, both the devices are kept in sync,
and the next forward pass doesn't
begin until all the variables have been updated.
So now let's take a look at some code
to see how you can use MirroredStrategy to scale up
your training.
As I mentioned, we'll talk about two types of use cases.
The first one is if you're using the Keras high-level API,
and then we'll come back to the custom-training-loop example
after this.
So the code here is some skeleton code
to train the same MobileNetV2 model but this time using
the model.compile and fit API in Keras.
In order to change this code to use MirroredStrategy,
all you need to do is add these two lines of code.
The first thing you do is create a MirroredStrategy object,
and the second thing is you move the rest of your training
code inside the scope of this strategy.
Putting things inside the scope lets
it take control of things like variable creation.
So any variables that you create under the scope of the strategy
will now be mirrored variables.
You don't need to make any other changes
to your code in this case because we've already
made components of TensorFlow distribution aware.
So for instance, in this case we have
the optimizer which is distribution aware
as well as compile and fit.
The case we just saw was the simplest way
in which you can create a MirroredStrategy,
but you can also customize it.
So let's say by default it will use
all the GPUs available on your machine for training,
but if you want to only use specific ones,
you can specify them using the devices argument.
You can also customize what all-reduce algorithm
you want to use by using the cross_device_ops argument.
So now we've seen how to use MirroredStrategy
when using the high-level Keras model.fit API.
Now let's go back to our custom-training-loop example
from before and see how you can modify
that to use MirroredStrategy.
And there's a little bit more code in this example
because when you have a custom training loop,
you have more control over what exactly you want to distribute,
and so you need to do a little bit more work
to distribute it as well.
So here, this is the skeleton of the custom training loop
from before.
We have the model, the loss function, the optimizer,
and you have your training step.
And then you have your outer loop
which iterates over your data and calls the training step.
The first thing you need to do is the same as before.
You create the MirroredStrategy object,
and you move the creation of the model, the optimizer, and so
on inside the scope of this strategy.
And the purpose of this is the same as before.
But as I mentioned, you need to do a few more things,
so this is not sufficient in this case.
And let's go over each of them one by one.
The first thing you need to do is
to distribute your data typically.
And if you're using tf.data data sets as your input,
this is straightforward.
All you need to do is call strategy.experimental
distribute dataset on your data set,
and this returns a distributed data
set which you can then iterate over
in a very similar manner as before.
The second thing you need to do is
to scale your loss by the global_batch_size,
and this is very important so that
the convergent characteristics of your model do not change.
And we've provided a helper method in the nn library
to do so.
And the third thing you need to do
is to specify which specific computation
you want to replicate.
So in this case, we want to replicate our training
step on each replica.
So you can use strategy.experimental_run_v2
API to provide the training-step function as well
as your distributed input, and you wrap this whole thing
in another tf.function because we want all of this to run
as a tf.Graph as well.
So those are all the code changes
you need to make in order to take the custom training
loop from before and now run it in a distributed fashion using
distribution strategy API.
So let's go back and take a look at what kind of scaling
you can see.
So just out of the box adding this MirroredStrategy
to the MobileNet V2 example from before,
we're able to get 80% when going from one
GPU to eight GPUs on a single machine.
It's possible to get even more scaling out of this
by doing some manual optimizations which
we won't be going into today.
To give another example of the scaling,
we ran multi-GPU training for ResNet50,
which is a very popular image-classification benchmark.
And in this example, we also used the FP16 and XLA
techniques that Taylor talked about before.
And here you can see going from one
to eight GPUs we were able to get 85% scaling.
And this example was using the Keras model.fit API instead
of the custom-training example.
If you're interested, you can look at the link in the bottom
and you can try out this model yourself.
Another example is using the transformer language model
to show that this is not just for images.
We're able to scale up other types of models as well.
And in this case, we're able to get more than 90%
scaling when running from one to eight GPUs.
So, so far we've been talking about scaling
up to multiple GPUs on a single machine,
but most likely you would want to scale even further
to multiple machines, maybe even multiple GPUs,
or perhaps just with CPUs.
For these use cases, we provide the
MultiWorkerMirroredStrategy.
As the name suggests, this is very
similar to the MirroredStrategy that we've been talking about.
It also implements synchronous training
but this time across all the different machines
in your cluster.
In order to do the altered use, it
uses a new type of op in TensorFlow
called collective ops.
Collective op is a single op in the TensorFlow graph
which can determine the best altered use to use based
on a number of different factors such as the network
topology, the type of communication
available between the different machines,
as well as tensor sizes.
It can also implement optimization
such as tensor fusion.
So for instance if you have a lot of small tensors
that you want to aggregate, it may batch them up
into a single tensor in order to reduce the load on the network.
So how can you use MultiWorkerMirroredStrategy?
It's actually very similar to MirroredStrategy.
You create a strategy object like so,
and the rest of your training code
actually remains the same, so I've omitted it here.
Once you have the strategy object,
you can put the rest of your code in strategy.scope,
and you're good to go.
One more thing you need to do, however,
in the multi-worker case is to give us
information about your cluster.
And one way to do that for multi-worker strategy
is using the TF_CONFIG environment variable.
TF_CONFIG, you might be familiar with this environment variable
if you've used distributor training using estimator
in TensorFlow 1.
So it basically consists of two components.
The first is the cluster which gives us information
about your entire cluster.
So here we have three different workers
at these hosts and port.
And the second piece is information
about the specific worker.
So this is saying this is worker one.
Once you provide this TF_CONFIG, the task part
would be different on each worker,
but their training code can remain the same.
And distribution strategy will read this TF_CONFIG environment
variable and figured out how to communicate
to the different workers in your cluster.
So we've been talking about multiple machines,
multiple GPUs.
What about TPUs?
You've probably heard about TPUs in this conference somewhere
else before.
TPUs are custom hardware built by Google
to accelerate machine-learning workloads.
You can use them through cloud TPUs,
or you can even try them out in Codelab.
Distribution strategy provides TPUStrategy for you
to be able to scale up your training to TPUs.
It's very similar to MirroredStrategy
in that it implements synchronous training,
and it uses the cross_replicate_sum API
to do the all reduce across the TPU cores.
You can use this API to scale your training
to a single TPU or a slice of a pod or a full pod as well.
And if you want to try out TPUStrategy,
you can try it with TensorFlow nightlies,
or you can wait for the 2.1 stable release for this
as well.
Let's look at the code to see how you can use TPUStrategy.
The first thing you need to do is to provide information
about the TPU cluster.
So the way you do that is create a TPUClusterResolver
and give it the name or address of your TPU.
Once you have a ClusterResolver, you
can use the experimental_connect_to_cluster
API to connect to this cluster and also initialize
the TPU system.
Once you've done these three things,
you can then create the strategy object
and pass the ClusterResolver object to it.
Once you have the strategy object, the rest of the code
remains the same.
You create the strategy to scope,
and you put your training code inside that.
So I won't be going into that here.
So looking at this code, you can see that distribution strategy
makes it really easy to switch from training
onto multiple GPUs or multiple machines
to specialized hardware like TPUs.
So that's all I was going to talk
about distribution strategy.
To recap today, we talked about a few different things.
We talked about tf.data to build simple and performant input
pipelines.
We talked about how you can use tf.function
to run your training as a TensorFlow graph.
We talked about how you can use XLA and mixed precision
to improve your training speed even further.
And finally, we talked about the DistrbuteStrategy API
to scale out your training to more hardware.
We have a number of resources on our website, a number of guides
and tutorials, so we encourage you to check those out.
And if you have any questions or you want to report bugs,
please reach out to us on GitHub.
We'll also be available in the expo hall after the talk
if you have more questions.
And finally, the notebook that we've been using in the talk
is listed here.
So if you want to take a picture,
you can check it out later.
That's all.
Thank you so much for listening.
[APPLAUSE]
I guess we have some time, so we can take some questions.
AUDIENCE: Hi Thanks for the nice presentation.
So I have two questions, both regarding inference.
So you mentioned how we can use TensorBoard
for profiling to see how well our CPUs and GPUs are utilized.
Can we do the same to see how well our inferences are
working?
TAYLOR ROBIE: Yes.
So the profiler has no concept of training or inference.
It just looks at ops as they execute.
So if you're just running the forward pass,
they'll show up all the same.
PRIYA GUPTA: It also kind of depends on
how you're doing inference.
Are you doing it using Python APIs or are you--
AUDIENCE: SavedModel.
So let's say I have a SavedModel format,
and I want to use that in TensorFlow.
PRIYA GUPTA: So you're not really
using any of the Python APIs, right?
AUDIENCE: So I use Python APIs, but basically to--
PRIYA GUPTA: So you're doing a SavedModel in Python,
and then-- yeah, then you can use TensorFlow as well.
AUDIENCE: Regarding mixed precision,
will it help in inference as well and how to we enable that?
TAYLOR ROBIE: It can potentially.
Typically for inference, the integer low-precision formats
tend to be more important than the floating-point ones.
If you really care about very fast
inference with low precision, I would
suggest that you check out TF Lite because they have
a lot of quantization support.
AUDIENCE: Thank you.
AUDIENCE: Hello.
I had just one question about how
to save your model when you are training
in a DistributeStrategy because your code is replicated
in all the nodes.
What's the official way to do this?
PRIYA GUPTA: Yes, so you want to save a full SavedModel.
AUDIENCE: Yes.
PRIYA GUPTA: Yeah, so you can use the standard APIs,
just the tf.saved_model.save.
And what it will do is it will not save the replicated model.
It'll save a single copy of the model.
So then it's like any other SavedModel,
and you can then reload it.
If you load it inside another strategy again,
then you can then--
it will get replicated at that point,
or you can use it for inference as another SavedModel.
AUDIENCE: Because what I experienced
was that all the nodes tried to save
the model at the same time, and then it
was an issue between a race condition with the placement
where the models were saved.
PRIYA GUPTA: And so you got an error?
AUDIENCE: Yes, it crashed.
PRIYA GUPTA: Maybe we can talk offline.
It's supposed to work, but there could be bugs.
AUDIENCE: Thanks for the talk.
I have a quick question about mixed position and the XLA.
Will the API automatically use the hardware-related features?
Like for example, in mixed precision
if you have Volta machine, it will
be able to use Volta machine's [INAUDIBLE]??
TAYLOR ROBIE: Yes.
For instance, on Volta, it will automatically
try to use the Tensor Core.
And in fact XLA and mixed precision
synergize very well because XLA-- or sorry, mixed precision
inserts a bunch of casts, and then XLA will go along
and it will actually optimize a lot of those away.
It will try and make the layout more amenable
to mixed precision.
So XLA tends to talk to the hardware at a very low level.
AUDIENCE: So basically from user point of view it's transparent.
If I run the same code on the CPU, on the old GPU, new GPU,
it will automatically try to use the hardware ability?
TAYLOR ROBIE: Yes.
SPEAKER: I'm really sorry.
We kind of have to stop this now for the livestreaming.
PRIYA GUPTA: We can take more questions outside the hall.