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 notdlarray函数对象,加速缓存结果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.

检查输出的加速功能ion match the outputs of the underlying function, use theCheckModeproperty of the accelerated function. When theCheckModeproperty of the accelerated function is'tolerance'and the outputs differ by more than a specified tolerance, the accelerated function throws a warning.

Accelerate the functionmyUnsupportedFun, listed at the end of the example using thedlacceleratefunction. The functionmyUnsupportedFungenerates random noise and adds it to the input. This function does not support acceleration because the function generates random numbers that are notdlarrayobjects.

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 theclearCachefunction.

clearCache(accfun)

To check that the outputs of reused cached traces match the outputs of the underlying function, set theCheckModeproperty 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 adlarrayinput.

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 theCheckModeproperty 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 therandfunction with adlarrayobject supports acceleration. To use random number generation in an accelerated function, ensure that the function uses therandfunction with the'like'option set to a traceddlarrayobject (adlarrayobject that depends on an inputdlarrayobject).

Accelerate the functionmySupportedFun, listed at the end of the example. The functionmySupportedFunadds noise to the input by generating noise using the'like'option with a traceddlarrayobject.

accfun2 = dlaccelerate(@mySupportedFun);

Clear any previously cached traces using theclearCachefunction.

clearCache(accfun2)

To check that the outputs of reused cached traces match the outputs of the underlying function, set theCheckModeproperty 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 theCheckModeproperty to'none'.

accfun1.CheckMode ='none'; accfun2.CheckMode ='none';

functionout = myUnsupportedFun(dlX) sz = size(dlX); noise = rand(sz); out = dlX + noise;end

functionout = mySupportedFun(dlX) sz = size(dlX); noise = rand(sz,'like',dlX); out = dlX + noise;end

See Also

dlaccelerate|AcceleratedFunction|clearCache|dlarray|dlgradient|dlfeval

Related Topics

• Deep Learning Function Acceleration for Custom Training Loops
• Accelerate Custom Training Loop Functions
• Evaluate Performance of Accelerated Deep Learning Function