Caching model evaluations

Sherpa contains a rudimentary system for caching the results of 1D model evaluations in order to speed up the time to evaluate models, at the expense of using more memory. The modelCacher1d() function decorator is applied to the calc() method of ArithmeticModel models, and this then uses the parameter values, evaluation grid, and integrate setting to look for a value from that model’s cache. If found the value is returned, otherwise the model is evaluated and the result is added to the cache.

It is hard to predict how effective caching is so the defaults are set for typical Sherpa use cases, based on some benchmarking, but your performance might be improved with different settings.

What models are cached?

A model uses the cache if @modelCacher1d is applied to the calc method and model.cache is set to a positive integer. Unfortunately it is not easy to check weather @modelCacher1d is applied without looking at the source codeb- or by running a test as shown below, in the example section.

When is the cache useful?

At present most 1D models use the cache by default. It is intended to improve fit performance, but the actual time saved depends on the model and the data being fit. Compared to most built-in sherpa models, models in the optional XSPEC model library (sherpa.astro.xspec) tend to be more complex and thus benefit more from caching.

By default, the cache is switched off (mdl.cache=0) for simple models where the model evaluation is fast, sometimes even faster than the hashing that is needed to look up a value in the cache. An obvious example is a scale or constant model (sherpa.models.basic.Scale1D or sherpa.models.basic.Constant1D), but this also applies to some fast analytical models such as an XSPEC black body (sherpa.astro.xspec.XSbbody).

Can I turn off this behavior for other models?

The size of the cache for a specific model component called mdl can be set to zero (mdl.cache=0) to turn off the cache behavior. This may be useful if you are evaluating models over a large grid, to save memory. For a composite model (e.g. a sum of models) you need to set the cache for each component.

When we use the fit method of the UI or the fit method of an optimizer, it is possible to turn off caching for all models with the cache parameter, e.g. ui.fit(..., cache=False). This works by iterating over all the model components and setting the cache attribute to zero. Note that the opposite is not true: setting cache=True does not turn on the cache for all model components, it simply leaves it at the previous setting. This is because some models may not work with caching at all and need to stay at cache=0 at all times. The cache has to be manually set to a positive number for all models that should use the cache to allow caching again.

How do I set the default in my own models to use or not use the cache?

In order to be cacheable at all, a model must have the @modelCacher1d decorator applied to the calc method. The default for an ArithmeticModel is a cache size of 5, but this can be changed by setting the cache attribute in the model’s constructor. For example, here we deactivate the cache by default by setting the value to 0, but we still decorate the calc method so that a user can switch if back on for individual instances of the model:

>>> from sherpa.models.model import ArithmeticModel, modelCacher1d, Parameter
>>> class MyModel(ArithmeticModel):
...     def __init__(self, name='mymodel'):
...         self.xpos = Parameter(name, 'offset', 0)
...         super().__init__(name, (self.offset,))
...         self.cache = 0
...
...     @modelCacher1d
...     def calc(self, p, *args, **kwargs):
...         # do something
...         return p[0] + args[0]
>>> m = MyModel()
>>> m.cache = 3  # use the cache in models instance m

Do not set cache = 0 as a class attribute. If you use modelCacher1d cache is actually a property that does other things when the cache is set (e.g. reset the cache content). Setting a class attribute will lead to errors when the decorated calc method is called, i.e. the following will not work:

>>> class MyModel(ArithmeticModel):
...     cache = 0  # DO NOT DO THIS!

How does the cache work?

The parameter values, integrate setting, and grid values are used to create a unique token - the SHA256 hash of the values - which is used to look up a value in the _cache dictionary. If it exists then the stored value is returned, otherwise the model is evaluated and added to the _cache dictionary. In order to keep the cache size small, the oldest element in the cache is removed when the number of entries becomes larger than cache elements (the default value for this attribute is 5).

Examples

Checking the cache

In the following example we evaluate a model and check the _cache attribute, and see that it has been updated by the model evaluation.

>>> from sherpa.models.basic import Box1D
>>> m = Box1D()
>>> m.xlow = 1.5
>>> m.xhi = 4.5
>>> print(m._cache)
{}
>>> print(m([1, 2, 3, 4, 5, 6]))
[0. 1. 1. 1. 0. 0.]
>>> print(m._cache)  
{b'<random byte string>': array([0., 1., 1., 1., 0., 0.])}

Fit and the startup method

The fit method can also be seen to use the cache (although in this case it isn’t worth it!). First we set up the data:

>>> import numpy as np
>>> from sherpa.data import Data1D
>>> x = np.arange(0, 3)
>>> y = [2, 0.3, 0.02]
>>> data = Data1D('example', x, y)

A simple model is used:

>>> from sherpa.models.basic import Exp10
>>> mdl = Exp10()
>>> mdl.offset.frozen = True
>>> mdl.offset = 1.0
>>> mdl.coeff.frozen = True
>>> mdl.coeff = -1.0
>>> print(mdl.ampl.val)
1.0
>>> print(mdl._cache)
{}

The fit only takes 4 iterations, so the cache doesn’t help here! Note that the startup and teardown methods are called automatically by fit():

>>> from sherpa.fit import Fit
>>> f = Fit(data, mdl)
>>> result = f.fit()
>>> print(result.format())
Method                = levmar
Statistic             = chi2gehrels
Initial fit statistic = 9.178
Final fit statistic   = 0.00239806 at function evaluation 4
Data points           = 3
Degrees of freedom    = 2
Probability [Q-value] = 0.998802
Reduced statistic     = 0.00119903
Change in statistic   = 9.1756
   exp10.ampl     0.201694     +/- 0.263543

The cache contains 4 elements which we can display:

>>> print(len(mdl._cache))
4
>>> for v in mdl._cache.values():
...     print(v)
...
[10.   1.   0.1]
[10.00345267  1.00034527  0.10003453]
[2.01694277 0.20169428 0.02016943]
[2.01763916 0.20176392 0.02017639]