refers #20079
Added docstrings to 'concretize' and 'concretized' to
document the format for tests.
Added tests for the activation of test dependencies.
refers #20040
This modification emits rules like:
provides_virtual("netlib-lapack","blas") :- variant_value("netlib-lapack","external-blas","False").
for packages that provide virtual dependencies conditionally instead
of a fact that doesn't account for the condition.
This PR fixes two problems with clang/llvm's version detection. clang's
version output looks like this:
```
clang version 11.0.0
Target: x86_64-unknown-linux-gnu
```
This caused clang's version to be misdetected as:
```
clang@11.0.0
Target:
```
This resulted in errors when trying to actually use it as a compiler.
When using `spack external find`, we couldn't determine the compiler
version, resulting in errors like this:
```
==> Warning: "llvm@11.0.0+clang+lld+lldb" has been detected on the system but will not be added to packages.yaml [reason=c compiler not found for llvm@11.0.0+clang+lld+lldb]
```
Changing the regex to only match until the end of the line fixes these
problems.
Fixes: #19473
This adds a new `mark` command that can be used to mark packages as either
explicitly or implicitly installed. Apart from fixing the package
database after installing a dependency manually, it can be used to
implement upgrade workflows as outlined in #13385.
The following commands demonstrate how the `mark` and `gc` commands can be
used to only keep the current version of a package installed:
```console
$ spack install pkgA
$ spack install pkgB
$ git pull # Imagine new versions for pkgA and/or pkgB are introduced
$ spack mark -i -a
$ spack install pkgA
$ spack install pkgB
$ spack gc
```
If there is no new version for a package, `install` will simply mark it as
explicitly installed and `gc` will not remove it.
Co-authored-by: Greg Becker <becker33@llnl.gov>
Users can add test() methods to their packages to run smoke tests on
installations with the new `spack test` command (the old `spack test` is
now `spack unit-test`). spack test is environment-aware, so you can
`spack install` an environment and then run `spack test run` to run smoke
tests on all of its packages. Historical test logs can be perused with
`spack test results`. Generic smoke tests for MPI implementations, C,
C++, and Fortran compilers as well as specific smoke tests for 18
packages.
Inside the test method, individual tests can be run separately (and
continue to run best-effort after a test failure) using the `run_test`
method. The `run_test` method encapsulates finding test executables,
running and checking return codes, checking output, and error handling.
This handles the following trickier aspects of testing with direct
support in Spack's package API:
- [x] Caching source or intermediate build files at build time for
use at test time.
- [x] Test dependencies,
- [x] packages that require a compiler for testing (such as library only
packages).
See the packaging guide for more details on using Spack testing support.
Included is support for package.py files for virtual packages. This does
not change the Spack interface, but is a major change in internals.
Co-authored-by: Tamara Dahlgren <dahlgren1@llnl.gov>
Co-authored-by: wspear <wjspear@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
The deprecatedProperties custom validator now can accept a function
to compute a better error message.
Improve error/warning message for deprecated properties
As of #18260, `spack load` and `spack env activate` now use
`prefix_inspections` from the modules configuration to decide
how to modify environment variables.
This updates the modules configuration documentation to describe
how to update environment variables with the `prefix_inspections`
section. This also updates the `spack load` and environments
documentation to refer to the new `prefix_inspections` documentation.
`spack load` and `spack env activate` now use the prefix inspections
defined in `modules.yaml`. This allows users to customize/override
environment variable modifications if desired.
If no `prefix_inspections` configuration is present, Spack uses the
values in the default configuration.
This PR reworks a few attributes in the container subsection of
spack.yaml to permit the injection of custom base images when
generating containers with Spack. In more detail, users can still
specify the base operating system and Spack version they want to use:
spack:
container:
images:
os: ubuntu:18.04
spack: develop
in which case the generated recipe will use one of the Spack images
built on Docker Hub for the build stage and the base OS image in the
final stage. Alternatively, they can specify explicitly the two
base images:
spack:
container:
images:
build: spack/ubuntu-bionic:latest
final: ubuntu:18.04
and it will be up to them to ensure their consistency.
Additional changes:
* This commit adds documentation on the two approaches.
* Users can now specify OS packages to install (e.g. with apt or yum)
prior to the build (previously this was only available for the
finalized image).
* Handles to avoid an update of the available system packages have been
added to the configuration to facilitate the generation of recipes
permitting deterministic builds.
This commit address the case of concretizing a root spec with a
transitive conditional dependency on a virtual package, provided
by an external. Before these modifications default variant values
for the dependency bringing in the virtual package were not
respected, and the external package providing the virtual was added
to the DAG.
The issue stems from two facts:
- Selecting a provider has higher precedence than selecting default variants
- To ensure that an external is preferred, we used a negative weight
To solve it we shift all the providers weight so that:
- External providers have a weight of 0
- Non external provider have a weight of 10 or more
Using a weight of zero for external providers is such that having
an external provider, if present, or not having a provider at all
has the same effect on the higher priority minimization.
Also fixed a few minor bugs in concretize.lp, that were causing
spurious entries in the final answer set.
Cleaned concretize.lp from leftover rules.
If a the default of a multi-valued variant is set to
multiple values either in package.py or in packages.yaml
we need to ensure that all the values are present in the
concretized spec.
Since each default value has a weight of 0 and the
variant value is set implicitly by the concretizer
we need to add a rule to maximize on the number of
default values that are used.
This commit introduces a new rule:
real_node(Package) :- not external(Package), node(Package).
that permits to distinguish between an external node and a
real node that shouldn't trim dependency. It solves the
case of concretizing ninja with an external Python.
`node_compiler_hard()` means that something explicitly asked for a node's
compiler to be set -- i.e., it's not inherited, it's required. We're
generating this in spec_clauses even for specs in rule bodies, which
results in conditions like this for optional dependencies:
In py-torch/package.py:
depends_on('llvm-openmp', when='%apple-clang +openmp')
In the generated ASP:
declared_dependency("py-torch","llvm-openmp","build")
:- node("py-torch"),
variant_value("py-torch","openmp","True"),
node_compiler("py-torch","apple-clang"),
node_compiler_hard("py-torch","apple-clang"),
node_compiler_version_satisfies("py-torch","apple-clang",":").
The `node_compiler_hard` there means we would have to *explicitly* set
py-torch's compiler to trigger the llvm-openmp dependency, rather than
just letting it be set by preferences. This is wrong; the dependency
should be there regardless of how the compiler was set.
- [x] remove fn.node_compiler_hard() call from spec_clauses when
generating rule body clauses.
If the version list passed to one_of_iff is empty, it still generates a
rule like this:
node_compiler_version_satisfies("fujitsu-mpi", "arm", ":") :- 1 { } 1.
1 { } 1 :- node_compiler_version_satisfies("fujitsu-mpi", "arm", ":").
The cardinality rules on the right and left above are never
satisfiale, and these rules do nothing.
- [x] Skip generating any rules at all for empty version lists.
As reported, conflicts with compiler ranges were not treated
correctly. This commit adds tests to verify the expected behavior
for the new concretizer.
The new rules to enforce a correct behavior involve:
- Adding a rule to prefer the compiler selected for
the root package, if no other preference is set
- Give a strong negative weight to compiler preferences
expressed in packages.yaml
- Maximize on compiler AND compiler version match
Variant of this kind don't have a list of possible
values encoded in the ASP facts. Since all we have
is a validator the list of possible values just includes
just the default value and possibly the value passed
from packages.yaml or cli.
This is done after the builder has actually built
the specs, to respect the semantics use with the
old concretizer.
Later we could move this to the solver as
a multivalued variant.
This is done after the builder has actually built
the specs, to respect the semantics use with the
old concretizer.
A better approach is to substitute the spec
directly in concretization.
The "none" variant value cannot be combined with
other values.
The '*' wildcard matches anything, including "none".
It's thus relevant in queries, but disregarded in
concretization.
- The test on concretization of anonymous dependencies
has been fixed by raising the expected exception.
- The test on compiler bootstrap has been fixed by
updating the version of GCC used in the test.
Since gcc@2.0 does not support targets later than
x86_64, the new concretizer was looking for a
non-existing spec, i.e. it was correctly trying
to retrieve 'gcc target=x86_64' instead of
'gcc target=core2'.
- The test on gitlab CI needed an update of the target
This commit adds support for specifying rules in
packages.yaml that refer to virtual packages.
The approach is to normalize in memory each
configuration and turn it into an equivalent
configuration without rules on virtual. This
is possible if the set of packages to be handled
is considered fixed.
The weight of the target used in concretization is, in order:
1. A specific per package weight, if set in packages.yaml
2. Inherited from the parent, if possible
3. The default target weight (always set)
Generate facts on externals by inspecting
packages.yaml. Added rules in concretize.lp
Added extra logic so that external specs
disregard any conflict encoded in the
package.
In ASP this would be a simple addition to
an integrity constraint:
:- c1, c2, c3, not external(pkg)
Using the the Backend API from Python it
requires some scaffolding to obtain a default
negated statement.
Conflict rules from packages are added as integrity
constraints in the ASP formulation. Most of the code
to generate them has been reused from PyclingoDriver.rules
The new concretizer and the old concretizer solve constraints
in a different way. Here we ensure that a SpackError is raised,
instead of a specific error that made sense in the old concretizer
but probably not in the new.
Instead of python callbacks, use cardinality constraints for package
versions. This is slightly faster and has the advantage that it can be
written to an ASP program to be executed *outside* of Spack. We can use
this in the future to unify the pyclingo driver and the clingo text
driver.
This makes use of add_weight_rule() to implement cardinality constraints.
add_weight_rule() only has a lower bound parameter, but you can implement
a strict "exactly one of" constraint using it. In particular, wee want to
define:
1 {v1; v2; v3; ...} 1 :- version_satisfies(pkg, constraint).
version_satisfies(pkg, constraint) :- 1 {v1; v2; v3; ...} 1.
And we do that like this, for every version constraint:
atleast1(pkg, constr) :- 1 {version(pkg, v1); version(pkg, v2); ...}.
morethan1(pkg, constr) :- 2 {version(pkg, v1); version(pkg, v2); ...}.
version_satisfies(pkg, constr) :- atleast1, not morethan1(pkg, constr).
:- version_satisfies(pkg, constr), morethan1.
:- version_satisfies(pkg, constr), not atleast1.
v1, v2, v3, etc. are computed on the Python side by comparing every
possible package version with the constraint.
Computing things like this has the added advantage that if v1, v2, v3,
etc. comprise *all* possible versions of a package, we can just omit the
rules for the constraint under consideration. This happens pretty
frequently in the Spack mainline.
- [x] Solver now uses the Python interface to clingo
- [x] can extract unsatisfiable cores from problems when things go wrong
- [x] use Python callbacks for versions instead of choice rules (this may
ultimately hurt performance)
There are now three parts:
- `SpackSolverSetup`
- Spack-specific logic for generating constraints. Calls methods on
`AspTextGenerator` to set up the solver with a Spack problem. This
shouln't change much from solver backend to solver backend.
- ClingoDriver
- The solver driver provides methods for SolverSetup to generates an ASP
program, send it to `clingo` (run as an external tool), and parse the
output into function tuples suitable for `SpecBuilder`.
- The interface is generic and should not have to change much for a
driver for, say, the Clingo Python interface.
- SpecBuilder
- Builds Spack specs from function tuples parsed by the solver driver.
The original implementation was difficult to read, as it only had
single-letter variable names. This converts all of them to descriptive
names, e.g., P -> Package, V -> Virtual/Version/Variant, etc.
To handle unknown compilers propely in tests (and elsewhere), we need to
add unknown compilers from the spec to the list of possible compilers.
Rework how the compiler list is generated and includes compilers from
specs if the existence check is disabled.
Specs like hdf5 ^mpi were unsatisfiable because we added a requierment
for `node("mpi").`. This can't be resolved because "mpi" is not a
package.
- [x] Introduce `virtual_node()`, which says *some* provider must be in
the DAG.
This adds compiler flags to the ASP solve so that we can have conditions
based on them in the solve. But, it keeps order out of the solve to
avoid unneeded complexity and combinatorial explosions.
The solver determines which flags are on a spec, but the order is
determined by DAG precedence (childrens' flags take precedence over
parents' and are added on the right) and order (order flags were
specified on the command line is respected).
The solver is responsible for determining when to propagate flags, when
to inheit them from other nodes, when to take them from compiler
preferences, etc.
Weight microarchitectures and prefers more rercent ones. Also disallow
nodes where the compiler does not support the selected target.
We should revisit this at some point as it seems like if I play around
with the compiler support for different architectures, the solver runs
very slowly. See notes in comments -- the bad case was gcc supporting
broadwell and skylake with clang maxing out at haswell.
We didn't have a cardinality constraint for multi-valued variants, so the
solver wasn't filling them in.
- [x] add a requirement for at least one value for multi-valued variants
Variants like `cpu_target` on `openblas` don't have defineed values, but
they have a default. Ensure that the default is always a possible value
for the solver.
Spack was generating the same dependency connstraints twice in the output ASP:
```
declared_dependency("abinit", "hdf5", "link")
:- node("abinit"),
variant_value("abinit", "mpi", "True"),
variant_value("abinit", "mpi", "True").
```
This was because `AspFunction` was modifying itself when called.
- [x] fix `AspFunction` so that every call returns a new object
- [x] Add support for packages.yaml and command-line compiler preferences.
- [x] Rework compiler version propagation to use optimization rather than
hard logic constraints
Technically the ASP output order does not matter, but it's hard to diff
two different solve fomulations unless we order it.
- [x] make sure ASP output is emitted in a deterministic order (by
sorting all hash keys)
This needs more thought, as I am pretty sure the weights are not correct.
Or, at least, I'm not convinced that they do what we want in all cases.
See note in concretize.lp.
Solver now prefers newer versions like the old concretizer. Prefer
package preferences from packages.yaml, preferred=True, package
definition, and finally each version itself.
Competition output only prints out one model, so we do not have to
unnecessarily parse all the non-optimal models. We'll just look at the
best model and bring that in.
In practice, this saves a lot of JSON parsing and spec construction time.
Clingo actually has an option to output JSON -- use that instead of
parsing the raw otuput ourselves.
This also allows us to pick the best answer -- modify the parser to
*only* construct a spec for that one rather than building all of them
like we did before.
