dlaccelerate
Description
Use dlaccelerate
to speed up deep learning function
evaluation for custom training loops.
The returned AcceleratedFunction
object caches the
traces of calls to the underlying function and reuses the cached
result when the same input pattern reoccurs.
Try using dlaccelerate
for function calls that:
are long-running
have
dlarray
objects, structures ofdlarray
objects, ordlnetwork
objects as inputsdo not have side effects like writing to files or displaying output
Invoke the accelerated function as you would invoke the underlying function. Note that the accelerated function is not a function handle.
Note
When using the dlfeval
function, the software automatically
accelerates the forward
and predict
functions for
dlnetwork
input. If you accelerate a deep learning function where the
majority of the computation takes place in calls to the forward
or
predict
functions for dlnetwork
input, then you might
not see an improvement in training time.
For more information, see Deep Learning Function Acceleration for Custom Training Loops.
creates an accfun
= dlaccelerate(fun
)AcceleratedFunction
object that retains the underlying traces of
the specified function handle fun
.
Caution
An AcceleratedFunction
object is not aware of updates to the underlying
function. If you modify the function associated with the accelerated function, then
clear the cache using the clearCache
object function or alternatively use the command
clear functions
.
Examples
Accelerate Model Gradients Function
Load the dlnetwork
object and class names from the MAT file dlnetDigits.mat
.
s = load("dlnetDigits.mat");
net = s.net;
classNames = s.classNames;
Accelerate the model loss function modelLoss
listed at the end of the example.
fun = @modelLoss; accfun = dlaccelerate(fun);
Clear any previously cached traces of the accelerated function using the clearCache
function.
clearCache(accfun)
View the properties of the accelerated function. Because the cache is empty, the Occupancy
property is 0.
accfun
accfun = AcceleratedFunction with properties: Function: @modelLoss Enabled: 1 CacheSize: 50 HitRate: 0 Occupancy: 0 CheckMode: 'none' CheckTolerance: 1.0000e-04
The returned AcceleratedFunction
object stores the traces of underlying function calls and reuses the cached result when the same input pattern reoccurs. To use the accelerated function in a custom training loop, replace calls to the model gradients function with calls to the accelerated function. You can invoke the accelerated function as you would invoke the underlying function. Note that the accelerated function is not a function handle.
Evaluate the accelerated model gradients function with random data using the dlfeval
function.
X = rand(28,28,1,128,"single"); X = dlarray(X,"SSCB"); T = categorical(classNames(randi(10,[128 1]))); T = onehotencode(T,2)'; T = dlarray(T,"CB"); [loss,gradients,state] = dlfeval(accfun,net,X,T);
View the Occupancy
property of the accelerated function. Because the function has been evaluated, the cache is nonempty.
accfun.Occupancy
ans = 2
Model Loss Function
The modelLoss
function takes a dlnetwork
object net
, a mini-batch of input data X
with corresponding target labels T
and returns the loss, the gradients of the loss with respect to the learnable parameters in net
, and the network state. To compute the gradients, use the dlgradient
function.
function [loss,gradients,state] = modelLoss(net,X,T) [Y,state] = forward(net,X); loss = crossentropy(Y,T); gradients = dlgradient(loss,net.Learnables); end
Clear Cache of Accelerated Function
Load the dlnetwork
object and class names from the MAT file dlnetDigits.mat
.
s = load("dlnetDigits.mat");
net = s.net;
classNames = s.classNames;
Accelerate the model loss function modelLoss
listed at the end of the example.
fun = @modelLoss; accfun = dlaccelerate(fun);
Clear any previously cached traces of the accelerated function using the clearCache
function.
clearCache(accfun)
View the properties of the accelerated function. Because the cache is empty, the Occupancy
property is 0.
accfun
accfun = AcceleratedFunction with properties: Function: @modelLoss Enabled: 1 CacheSize: 50 HitRate: 0 Occupancy: 0 CheckMode: 'none' CheckTolerance: 1.0000e-04
The returned AcceleratedFunction
object stores the traces of underlying function calls and reuses the cached result when the same input pattern reoccurs. To use the accelerated function in a custom training loop, replace calls to the model gradients function with calls to the accelerated function. You can invoke the accelerated function as you would invoke the underlying function. Note that the accelerated function is not a function handle.
Evaluate the accelerated model gradients function with random data using the dlfeval
function.
X = rand(28,28,1,128,"single"); X = dlarray(X,"SSCB"); T = categorical(classNames(randi(10,[128 1]))); T = onehotencode(T,2)'; T = dlarray(T,"CB"); [loss,gradients,state] = dlfeval(accfun,net,X,T);
View the Occupancy
property of the accelerated function. Because the function has been evaluated, the cache is nonempty.
accfun.Occupancy
ans = 2
Clear the cache using the clearCache
function.
clearCache(accfun)
View the Occupancy
property of the accelerated function. Because the cache has been cleared, the cache is empty.
accfun.Occupancy
ans = 0
Model Loss Function
The modelLoss
function takes a dlnetwork
object net
, a mini-batch of input data X
with corresponding target labels T
and returns the loss, the gradients of the loss with respect to the learnable parameters in net
, and the network state. To compute the gradients, use the dlgradient
function.
function [loss,gradients,state] = modelLoss(net,X,T) [Y,state] = forward(net,X); loss = crossentropy(Y,T); gradients = dlgradient(loss,net.Learnables); end
Check Accelerated Deep Learning Function Outputs
This example shows how to check that the outputs of accelerated functions match the outputs of the underlying function.
In some cases, the outputs of accelerated functions differ to the outputs of the underlying function. For example, you must take care when accelerating functions that use random number generation, such as a function that generates random noise to add to the network input. When caching the trace of a function that generates random numbers that are not dlarray
objects, the accelerated function caches resulting random numbers in the trace. When reusing the trace, the accelerated function uses the cached random values. The accelerated function does not generate new random values.
To check that the outputs of the accelerated function match the outputs of the underlying function, use the CheckMode
property of the accelerated function. When the CheckMode
property of the accelerated function is 'tolerance'
and the outputs differ by more than a specified tolerance, the accelerated function throws a warning.
Accelerate the function myUnsupportedFun
, listed at the end of the example using the dlaccelerate
function. The function myUnsupportedFun
generates random noise and adds it to the input. This function does not support acceleration because the function generates random numbers that are not dlarray
objects.
accfun = dlaccelerate(@myUnsupportedFun)
accfun = AcceleratedFunction with properties: Function: @myUnsupportedFun Enabled: 1 CacheSize: 50 HitRate: 0 Occupancy: 0 CheckMode: 'none' CheckTolerance: 1.0000e-04
Clear any previously cached traces using the clearCache
function.
clearCache(accfun)
To check that the outputs of reused cached traces match the outputs of the underlying function, set the CheckMode
property to 'tolerance'
.
accfun.CheckMode = 'tolerance'
accfun = AcceleratedFunction with properties: Function: @myUnsupportedFun Enabled: 1 CacheSize: 50 HitRate: 0 Occupancy: 0 CheckMode: 'tolerance' CheckTolerance: 1.0000e-04
Evaluate the accelerated function with an array of ones as input, specified as a dlarray
input.
dlX = dlarray(ones(3,3)); dlY = accfun(dlX)
dlY = 3×3 dlarray 1.8147 1.9134 1.2785 1.9058 1.6324 1.5469 1.1270 1.0975 1.9575
Evaluate the accelerated function again with the same input. Because the accelerated function reuses the cached random noise values instead of generating new random values, the outputs of the reused trace differs from the outputs of the underlying function. When the CheckMode
property of the accelerated function is 'tolerance'
and the outputs differ, the accelerated function throws a warning.
dlY = accfun(dlX)
Warning: Accelerated outputs differ from underlying function outputs.
dlY = 3×3 dlarray 1.8147 1.9134 1.2785 1.9058 1.6324 1.5469 1.1270 1.0975 1.9575
Random number generation using the 'like'
option of the rand
function with a dlarray
object supports acceleration. To use random number generation in an accelerated function, ensure that the function uses the rand
function with the 'like'
option set to a traced dlarray
object (a dlarray
object that depends on an input dlarray
object).
Accelerate the function mySupportedFun
, listed at the end of the example. The function mySupportedFun
adds noise to the input by generating noise using the 'like'
option with a traced dlarray
object.
accfun2 = dlaccelerate(@mySupportedFun);
Clear any previously cached traces using the clearCache
function.
clearCache(accfun2)
To check that the outputs of reused cached traces match the outputs of the underlying function, set the CheckMode
property to 'tolerance'
.
accfun2.CheckMode = 'tolerance';
Evaluate the accelerated function twice with the same input as before. Because the outputs of the reused cache match the outputs of the underlying function, the accelerated function does not throw a warning.
dlY = accfun2(dlX)
dlY = 3×3 dlarray 1.7922 1.0357 1.6787 1.9595 1.8491 1.7577 1.6557 1.9340 1.7431
dlY = accfun2(dlX)
dlY = 3×3 dlarray 1.3922 1.7060 1.0462 1.6555 1.0318 1.0971 1.1712 1.2769 1.8235
Checking the outputs match requires extra processing and increases the time required for function evaluation. After checking the outputs, set the CheckMode
property to 'none'
.
accfun1.CheckMode = 'none'; accfun2.CheckMode = 'none';
Example Functions
The function myUnsupportedFun
generates random noise and adds it to the input. This function does not support acceleration because the function generates random numbers that are not dlarray
objects.
function out = myUnsupportedFun(dlX) sz = size(dlX); noise = rand(sz); out = dlX + noise; end
The function mySupportedFun
adds noise to the input by generating noise using the 'like'
option with a traced dlarray
object.
function out = mySupportedFun(dlX) sz = size(dlX); noise = rand(sz,'like',dlX); out = dlX + noise; end
Input Arguments
fun
— Deep learning function
function handle
Deep learning function to accelerate, specified as a function handle.
To learn more about developing deep learning functions for acceleration, see Deep Learning Function Acceleration for Custom Training Loops.
Example: @modelLoss
Data Types: function_handle
Output Arguments
accfun
— Accelerated deep learning function
AcceleratedFunction
object
Accelerated deep learning function, returned as an AcceleratedFunction
object.
More About
Acceleration Considerations
Because of the nature of caching traces, not all functions support acceleration.
The caching process can cache values that you might expect to change or that depend on external factors. You must take care when you accelerate functions that:
have inputs with random or frequently changing values
have outputs with frequently changing values
generate random numbers
use
if
statements andwhile
loops with conditions that depend on the values ofdlarray
objectshave inputs that are handles or that depend on handles
Read data from external sources (for example, by using a datastore or a
minibatchqueue
object)
Because the caching process requires extra computation, acceleration can lead to longer running code in some cases. This scenario can happen when the software spends time creating new caches that do not get reused often. For example, when you pass multiple mini-batches of different sequence lengths to the function, the software triggers a new trace for each unique sequence length.
Accelerated functions can do the following when calculating a new trace only.
modify the global state such as, the random number stream or global variables
use file input or output
display data using graphics or the command line display
When using accelerated functions in parallel, such as when using a
parfor
loop, then each worker maintains its own cache. The cache is
not transferred to the host.
Functions and custom layers used in accelerated functions must also support acceleration.
For more information, see Deep Learning Function Acceleration for Custom Training Loops.
dlode45
Does Not Support Acceleration When
GradientMode
is "direct"
The dlaccelerate
function does not support accelerating the
dlode45
function when the GradientMode
option is
"direct"
. To accelerate the code that calls the
dlode45
function, set the GradientMode
option to
"adjoint"
or accelerate parts of your code that do not call the
dlode45
function with the GradientMode
option
set to "direct"
.
Version History
Introduced in R2021a
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