Add categories in the pipeline, allow an artifact to have a category
specified which allows it to be shown or hidden by default. Adapt the
CLI tools to hide artifacts of the category that don't
`show_by_default=True`.
Since most functions in `pipeline_parser.py` had a lot of redundancy in
the parameters passed, convert all of them to be part of a
`PipelineParser` class which stores all the variables.
Rework which information is transmitted in the pipeline metadata,
avoiding redundancy and moving some information there instead of
returning it every time a request is made.
The `EmitFieldAccesses` pass transforms `clift` by taking pointer-typed
expressions computed via integerr arithmetic with type-safe field
accesses and array accesses.
The transformation is split in three main phases:
1) `PointerArithmetic` computation.
2) `BestTraversal` computation.
3) `FieldAccess` `clift` rewrite.
The high level driver is implemented in the `EmitFieldAccesses` header
and cpp, while the nested 3 phases are implemented respectively in
`PointerArithmetic`, `BestTraversal` and `FieldAccessReplacement`.
The `computerPointerArithmetic` phase is concerned with taking a
pointer-typed `ExpressionOp`, called `PointerToReplace`, and expressing
it in a `BasePointer+Offset` form.
The `computeBestTraversal` phase is concerned with computing the best
traversal of the type pointed to by `BasePointer`, that can be used to
rewrite the pointer arithmetic in `clift` with just field accesses and
array subscripts.
The `replaceFieldAccess` phase takes the `Traversal` computed at the
previous step, and actually rewrites in `clift` the `PointerToReplace`
in terms of field accesses and array accesses w.r.t. the `BasePointer`.
Add the `--analyses` option which allows specifying the list of analyses
to be run before producing the artifact instead of the default initial
auto analysis.
Before this commit the `invalidate` code did invalidate all the objects
but left all the rows in `dependencies` that were paired to the produced
objects except the invalidated paths.
In this commit the logic is changed to be done in 3 steps:
1. SELECT all the objects that need to be invalidated, write down their
rowid
2. Delete all the rows in `dependencies` that were produced in
conjunction to the objects from (1)
3. Remove the objects from the table, returning invalidation data
This guarantees that the database does not accumulate invalidation data
over time due to invalidations.
Change the `get_model` and `set_model` interface of `StorageProvider` so
that it is responsibility of the `StorageProvider` to
serialize/deserialize the model before returning to the caller.
This is in preparation to the model migration being implemented, since
it's now a responsibility of the storage provider to deserialize it it
can trivially re-save it to disk if it is migrated.
Overhaul the logic and method names involved in enabling and disabling
reference caching in `TupleTree<T>`. `TupleTreeReference<T, U>` now
lazily caches the target and will traverse the path only when needed.
Also expose and use these functions in the new pipeline, which should
provide some speedup when executing a `Schedule`.
The functions that take care of running an analysis/analysis list have a
lot of duplication, merge the two into a single function that takes care
of running both.
Fix the code that handles setting the `--format`, `--tar` and `--yaml`
command line options, which incorrectly wrote to the `format` kwargs
instead of the `container_format` one.
Instead of executing `chdir` when using the `-C` option, which is
fragile when other paths are involved, store the option in the click
context and propagate it to the required code paths that require knowing
what the base directory is.
Allow the storage provider to be initialized with `local://?inline`.
This will set the cache directory to `$PWD/.cache` instead of
`~/.cache/revng`. This is to simplify testing for pypeline.
Add the functionality the the pipeline infrastructure and CLI to run
individual pipe and analyses as subcommands instead of in-process. This
allow better debuggability of individual pipes.
In preparation for debugging individual pipes add an additional method
to the `Pipe` interface, `needed_files`, which allows the pipe to
statically report which files it will request ahead of time.
If a venv is running, the `revng` script should re-exec to allow the
correct modules to be loaded and allow running the `main` function of
the respective modules.
Improve the handling of signals by leveraging the `Py_AtExit`
functionality to trigger cleanup when the interpreter exits.
When receiving SIGINT handle it specially because exceptions in python
are only thrown when the interpreter is running and not when C code is.
Convert the `RuntimeError`s emitted by the pipebox (e.g. when checking
preconditions or running analyses) into `PypelineError`s and have the
pypeline cli infrastructure handle them gracefully, without emitting
stacktraces.
Add infrastructure to pypeline that allows containers to be notified
when they are being used last, this allows two things:
* `Pipe`s eagerly clearing those containers once they are done reading
their contents
* `ScheduledTask`s clearing those out at the end of their execution in
case the pipe did not do it
This overall should improve memory usage as container no longer take up
memory if they are no longer used as part of a `Schedule`.
Fix the behavior of binary importers by propagating the path of the
input binary from revng2 downwards, allowing finding `.debug` files in
the correct paths.
Inline the body of `PipelineNode.run` into `ScheduledTask.run`, making
`PipelineNode` a pure data structure. This also allows eliding all the
arguments since all of them as properties of `ScheduledTask`.
Add a new option to the mass-testing `meta.yml` that allows skipping
some paths when generating the flamegraph. In the case of revng this
allows skipping the intermediate python frames.
Giacomo Vercesi