spirv-fuzz features transformations that should be applicable by
construction. Assertions are used to detect when such transformations
turn out to be inapplicable. Failures of such assertions indicate bugs
in the fuzzer. However, when using the fuzzer at scale (e.g. in
ClusterFuzz) reports of these assertion failures create noise, and
cause the fuzzer to exit early. This change adds an option whereby
inapplicable transformations can be ignored. This reduces noise and
allows fuzzing to continue even when a transformation that should be
applicable but is not has been erroneously created.
Some transformations (e.g. TransformationAddFunction) rely on running
the validator to decide whether the transformation is applicable. A
recent change allowed spirv-fuzz to take validator options, to cater
for the case where a module should be considered valid under
particular conditions. However, validation during the checking of
transformations had no access to these validator options.
This change introduced TransformationContext, which currently consists
of a fact manager and a set of validator options, but could in the
future have other fields corresponding to other objects that it is
useful to have access to when applying transformations. Now, instead
of checking and applying transformations in the context of a
FactManager, a TransformationContext is used. This gives access to
the fact manager as before, and also access to the validator options
when they are needed.
Before this change there was quite a lot of duplication in the code
being used to choose random percentages, and some of it was incorrect
so that a percentage chance of (100-N)% instead of N% was being used.
Also there was a lot of duplicate code to choose a random index into a
vector. This change eliminates that duplication (fixing up the
percentage problem), and gets rid of direct access to the random
number generator being used for fuzzing, so that all randomization
requests must go through the FuzzerContext class, discouraging future
ad-hoc uses of the random number generator.
