By default, clingo doesn't show any optimization criteria (maximized or
minimized sums) if the set they aggregate is empty. Per the clingo
mailing list, we can get around that by adding, e.g.:
```
#minimize{ 0@2 : #true }.
```
for the 2nd criterion. This forces clingo to print out the criterion but
does not affect the optimization.
This PR adds directives as above for all of our optimization criteria, as
well as facts with descriptions of each criterion,like this:
```
opt_criterion(2, "number of non-default variants")
```
We use facts in `concretize.lp` rather than hard-coding these in `asp.py`
so that the names can be maintained in the same place as the other
optimization criteria.
The now-displayed weights and the names are used to display optimization
output like this:
```console
(spackle):solver> spack solve --show opt zlib
==> Best of 0 answers.
==> Optimization Criteria:
Priority Criterion Value
1 version weight 0
2 number of non-default variants (roots) 0
3 multi-valued variants + preferred providers for roots 0
4 number of non-default variants (non-roots) 0
5 number of non-default providers (non-roots) 0
6 count of non-root multi-valued variants 0
7 compiler matches + number of nodes 1
8 version badness 0
9 non-preferred compilers 0
10 target matches 0
11 non-preferred targets 0
zlib@1.2.11%apple-clang@12.0.0+optimize+pic+shared arch=darwin-catalina-skylake
```
Note that this is all hidden behind a `--show opt` option to `spack
solve`. Optimization weights are no longer shown by default, but you can
at least inspect them and more easily understand what is going on.
- [x] always show optimization criteria in `clingo` output
- [x] add `opt_criterion()` facts for all optimizationc criteria
- [x] make display of opt criteria optional in `spack solve`
- [x] rework how optimization criteria are displayed, and add a `--show opt`
optiong to `spack solve`
CachedCMakePackage is a CMakePackage subclass for using CMake initial
cache. This feature of CMake allows packages to increase reproducibility,
especially between spack builds and manual builds. It also allows
packages to sidestep certain parsing bugs in extremely long cmake
commands, and to avoid system limits on the length of the command line.
Co-authored by: Chris White <white238@llnl.gov>
In the face of two consecutive spaces in the command line, the compiler wrapper would skip all remaining arguments, causing problems building py-scipy with Intel compiler. This PR solves the problem.
* Fixed compiler wrapper in the face of extra spaces between arguments
Co-authored-by: Elizabeth Fischer <elizabeth.fischer@alaska.edu>
Original commit message:
This feature of CMake allows packages to increase reproducibility, especially between
Spack- and manual builds. It also allows packages to sidestep certain parsing bugs in
extremely long ``cmake`` commands, and to avoid system limits on the length of the
command line.
Adding:
Co-authored by: Chris White <white238@llnl.gov>
This reverts commit c4f0a3cf6c.
CachedCMakePackage is a specialized class for packages built using CMake initial cache.
This feature of CMake allows packages to increase reproducibility, especially between
Spack- and manual builds. It also allows packages to sidestep certain parsing bugs in
extremely long ``cmake`` commands, and to avoid system limits on the length of the
command line.
Autoconf before 2.70 will erroneously pass ifx's -loopopt argument to the
linker, requiring all packages to use autoconf 2.70 or newer to use ifx.
This is a hotfix enabling ifx to be used in Spack. Instead of bothering
to upgrade autoconf for every package, we'll just strip out the
problematic flag if we're in `ld` mode.
- [x] Add a conditional to the `cc` wrapper to skip `-loopopt` in `ld`
mode. This can probably be generalized in the future to strip more
things (e.g., via an environment variable we can constrol from
Spack) but it's good enough for now.
- [x] Add a test ensuring that `-loopopt` arguments are stripped in link
mode, but not in compile mode.
Since `lazy_lexicographic_ordering` handles `None` comparison for us, we
don't need to adjust the spec comparators to return empty strings or
other type-specific empty types. We can just leverage the None-awareness
of `lazy_lexicographic_ordering`.
- [x] remove "or ''" from `_cmp_iter` in `Spec`
- [x] remove setting of `self.namespace` to `''` in `MockPackage`
We have been using the `@llnl.util.lang.key_ordering` decorator for specs
and most of their components. This leverages the fact that in Python,
tuple comparison is lexicographic. It allows you to implement a
`_cmp_key` method on your class, and have `__eq__`, `__lt__`, etc.
implemented automatically using that key. For example, you might use
tuple keys to implement comparison, e.g.:
```python
class Widget:
# author implements this
def _cmp_key(self):
return (
self.a,
self.b,
(self.c, self.d),
self.e
)
# operators are generated by @key_ordering
def __eq__(self, other):
return self._cmp_key() == other._cmp_key()
def __lt__(self):
return self._cmp_key() < other._cmp_key()
# etc.
```
The issue there for simple comparators is that we have to bulid the
tuples *and* we have to generate all the values in them up front. When
implementing comparisons for large data structures, this can be costly.
This PR replaces `@key_ordering` with a new decorator,
`@lazy_lexicographic_ordering`. Lazy lexicographic comparison maps the
tuple comparison shown above to generator functions. Instead of comparing
based on pre-constructed tuple keys, users of this decorator can compare
using elements from a generator. So, you'd write:
```python
@lazy_lexicographic_ordering
class Widget:
def _cmp_iter(self):
yield a
yield b
def cd_fun():
yield c
yield d
yield cd_fun
yield e
# operators are added by decorator (but are a bit more complex)
There are no tuples that have to be pre-constructed, and the generator
does not have to complete. Instead of tuples, we simply make functions
that lazily yield what would've been in the tuple. If a yielded value is
a `callable`, the comparison functions will call it and recursively
compar it. The comparator just walks the data structure like you'd expect
it to.
The ``@lazy_lexicographic_ordering`` decorator handles the details of
implementing comparison operators, and the ``Widget`` implementor only
has to worry about writing ``_cmp_iter``, and making sure the elements in
it are also comparable.
Using this PR shaves another 1.5 sec off the runtime of `spack buildcache
list`, and it also speeds up Spec comparison by about 30%. The runtime
improvement comes mostly from *not* calling `hash()` `_cmp_iter()`.
* Make -j flag less exceptional
The -j flag in spack behaves differently from make, ctest, ninja, etc,
because it caps the number of jobs to an arbitrary number 16.
Spack will behave like other tools if `spack install` uses a reasonable
default, and `spack install -j <num>` *overrides* that default.
This will be particularly useful for Spack usage outside of a traditional
HPC context and for HPC centers that encourage users to compile on
login nodes with many cores instead of on compute nodes, which has
become increasingly common as individual nodes have more cores.
This maintains the existing default value of min(num_cpus, 16). However,
as it is right now, Spack does a poor job at determining the number of
cpus on linux, since it doesn't take cgroups into account. This is
particularly problematic when using distributed builds with slurm. This PR
also introduces `spack.util.cpus.cpus_available()` to consolidate
knowledge on determining the number of available cores, and improves
core detection for linux. This should also improve core detection for Docker/
Kubernetes, which also use cgroups.
This commit extends the API of the __call__ method of the
SpackCommand class to permit passing global arguments
like those interposed between the main "spack" command
and the subsequent subcommand.
The functionality is used to fix an issue where running
```spack -e . location -b some_package```
ends up printing the name of the environment instead of
the build directory of the package, because the location arg
parser also stores this value as `arg.env`.
fixes#22294
A combination of the swapping order for global variables and
the fact that most of them are lazily evaluated resulted in
custom install tree not being taken into account if clingo
had to be bootstrapped.
This commit fixes that particular issue, but a broader refactor
may be needed to ensure that similar situations won't affect us
in the future.
Remote buildcache indices need to be stored in a place that does not
require writing to the Spack prefix. Move them from the install_tree to
the misc_cache.
fixes#22565
This change enforces the uniqueness of the version_weight
atom per node(Package) in the DAG. It does so by applying
FTSE and adding an extra layer of indirection with the
possible_version_weight/2 atom.
Before this change it may have happened that for the same
node two different version_weight/2 were in the answer set,
each of which referred to a different spec with the same
version, and their weights would sum up.
This lead to unexpected result like preferring to build a
new version of an external if the external version was
older.
* Make stage use concrete specs from environment
Same as in https://github.com/spack/spack/pull/21642, the idea is that
we want to easily stage a package that fails to build in a complex
environment. Instead of making the user create a spec by hand (basically
transforming all the rules in the environment manifest into a spec,
defying the purpose of the environment...), use the provided spec as a
filter for the already concretized specs. This also speeds up things,
cause we don't have to reconcretize.
* clingo: modify recipe for bootstrapping
Modifications:
- clingo builds with shared Python only if ^python+shared
- avoid building the clingo app for bootstrapping
- don't link to libpython when bootstrapping
* Remove option that breaks on linux
* Give more hints for the current Python
* Disable CLINGO_BUILD_PY_SHARED for bootstrapping
* bootstrapping: try to detect the current python from std library
This is much faster than calling external executables
* Fix compatibility with Python 2.6
* Give hints on which compiler and OS to use when bootstrapping
This change hints which compiler to use for bootstrapping clingo
(either GCC or Apple Clang on MacOS). On Cray platforms it also
hints to build for the frontend system, where software is meant
to be installed.
* Use spec_for_current_python to constrain module requirement
* ASP-based solver: avoid adding values to variants when they're set
fixes#22533fixes#21911
Added a rule that prevents any value to slip in a variant when the
variant is set explicitly. This is relevant for multi-valued variants,
in particular for those that have disjoint sets of values.
* Ensure disjoint sets have a clear semantics for external packages
fixes#22547
SingleFileScope was not able to repopulate its cache before this
change. This was affecting the configuration seen by environments
using clingo bootstrapped from sources, since the bootstrapping
operation involved a few cache invalidation for config files.
This change accounts for platform specific configuration scopes,
like ~/.spack/linux, during bootstrapping. These scopes were
previously not accounted for and that was causing issues e.g.
when searching for compilers.
* Replace URL computation in base IntelOneApiPackage class with
defining URLs in component packages (this is expected to be
simpler for now)
* Add component_dir property that all oneAPI component packages must
define. This property names a directory that should exist after
installation completes (useful for making sure the install was
successful) and also defines the search location for the
component's environment update script.
* Add needed dependencies for components (e.g. intel-oneapi-dnn
requires intel-oneapi-tbb). The compilers provided by
intel-oneapi-compilers need some components under certain
circumstances (e.g. when enabling SYCL support) but these were
omitted since the libraries should only be linked when a
dependent package requests that feature
* Remove individual setup_run_environment implementations and use
IntelOneApiPackage superclass method which sources vars.sh
(located in a subdirectory of component_dir)
* Add documentation for IntelOneApiPackge build system
Co-authored-by: Vasily Danilin <vasily.danilin@yandex.ru>
* unit tests: mark slow tests as "maybeslow"
This commit also removes the "network" marker and
marks every "network" test as "maybeslow". Tests
marked as db are maintained, but they're not slow
anymore.
* GA: require style tests to pass before running unit-tests
* GA: make MacOS unit tests fail fast
* GA: move all unit tests into the same workflow, run style tests as a prerequisite
All the unit tests have been moved into the same workflow so that a single
run of the dorny/paths-filter action can be used to ask for coverage based
on the files that have been changed in a PR. The basic idea is that for PRs
that introduce only changes to packages coverage is not necessary, this
resulting in a faster execution of the tests.
Also, for package only PRs slow unit tests are skipped.
Finally, MacOS and linux unit tests are now conditional on style tests passing
meaning that e.g. we won't waste a MacOS worker if we know that the PR has
flake8 issues.
* Addressed review comments
* Skipping slow tests on MacOS for package only recipes
* QA: make tests on changes correct before merging
In most cases, we want condition_holds(ID) to imply any imposed
constraints associated with the ID. However, the dependency relationship
in Spack is special because it's "extra" conditional -- a dependency
*condition* may hold, but we have decided that externals will not have
dependencies, so we need a way to avoid having imposed constraints appear
for nodes that don't exist.
This introduces a new rule that says that constraints are imposed
*unless* we define `do_not_impose(ID)`. This allows rules like
dependencies, which rely on more than just spec conditions, to cancel
imposed constraints.
We add one special case for this: dependencies of externals.
We only consider test dependencies some of the time. Some packages are
*only* test dependencies. Spack's algorithm was previously generating
dependency conditions that could hold, *even* if there was no potential
dependency type.
- [x] change asp.py so that this can't happen -- we now only generate
dependency types for possible dependencies.
This builds on #20638 by unifying all the places in the concretizer where
things are conditional on specs. Previously, we duplicated a common spec
conditional pattern for dependencies, virtual providers, conflicts, and
externals. That was introduced in #20423 and refined in #20507, and
roughly looked as follows.
Given some directives in a package like:
```python
depends_on("foo@1.0+bar", when="@2.0+variant")
provides("mpi@2:", when="@1.9:")
```
We handled the `@2.0+variant` and `@1.9:` parts by generating generated
`dependency_condition()`, `required_dependency_condition()`, and
`imposed_dependency_condition()` facts to trigger rules like this:
```prolog
dependency_conditions_hold(ID, Parent, Dependency) :-
attr(Name, Arg1) : required_dependency_condition(ID, Name, Arg1);
attr(Name, Arg1, Arg2) : required_dependency_condition(ID, Name, Arg1, Arg2);
attr(Name, Arg1, Arg2, Arg3) : required_dependency_condition(ID, Name, Arg1, Arg2, Arg3);
dependency_condition(ID, Parent, Dependency);
node(Parent).
```
And we handled `foo@1.0+bar` and `mpi@2:` parts ("imposed constraints")
like this:
```prolog
attr(Name, Arg1, Arg2) :-
dependency_conditions_hold(ID, Package, Dependency),
imposed_dependency_condition(ID, Name, Arg1, Arg2).
attr(Name, Arg1, Arg2, Arg3) :-
dependency_conditions_hold(ID, Package, Dependency),
imposed_dependency_condition(ID, Name, Arg1, Arg2, Arg3).
```
These rules were repeated with different input predicates for
requirements (e.g., `required_dependency_condition`) and imposed
constraints (e.g., `imposed_dependency_condition`) throughout
`concretize.lp`. In #20638 it got to be a bit confusing, because we used
the same `dependency_condition_holds` predicate to impose constraints on
conditional dependencies and virtual providers. So, even though the
pattern was repeated, some of the conditional rules were conjoined in a
weird way.
Instead of repeating this pattern everywhere, we now have *one* set of
consolidated rules for conditions:
```prolog
condition_holds(ID) :-
condition(ID);
attr(Name, A1) : condition_requirement(ID, Name, A1);
attr(Name, A1, A2) : condition_requirement(ID, Name, A1, A2);
attr(Name, A1, A2, A3) : condition_requirement(ID, Name, A1, A2, A3).
attr(Name, A1) :- condition_holds(ID), imposed_constraint(ID, Name, A1).
attr(Name, A1, A2) :- condition_holds(ID), imposed_constraint(ID, Name, A1, A2).
attr(Name, A1, A2, A3) :- condition_holds(ID), imposed_constraint(ID, Name, A1, A2, A3).
```
this allows us to use `condition(ID)` and `condition_holds(ID)` to
encapsulate the conditional logic on specs in all the scenarios where we
need it. Instead of defining predicates for the requirements and imposed
constraints, we generate the condition inputs with generic facts, and
define predicates to associate the condition ID with a particular
scenario. So, now, the generated facts for a condition look like this:
```prolog
condition(121).
condition_requirement(121,"node","cairo").
condition_requirement(121,"variant_value","cairo","fc","True").
imposed_constraint(121,"version_satisfies","fontconfig","2.10.91:").
dependency_condition(121,"cairo","fontconfig").
dependency_type(121,"build").
dependency_type(121,"link").
```
The requirements and imposed constraints are generic, and we associate
them with their meaning via the id. Here, `dependency_condition(121,
"cairo", "fontconfig")` tells us that condition 121 has to do with the
dependency of `cairo` on `fontconfig`, and the conditional dependency
rules just become:
```prolog
dependency_holds(Package, Dependency, Type) :-
dependency_condition(ID, Package, Dependency),
dependency_type(ID, Type),
condition_holds(ID).
```
Dependencies, virtuals, conflicts, and externals all now use similar
patterns, and the logic for generating condition facts is common to all
of them on the python side, as well. The more specific routines like
`package_dependencies_rules` just call `self.condition(...)` to get an id
and generate requirements and imposed constraints, then they generate
their extra facts with the returned id, like this:
```python
def package_dependencies_rules(self, pkg, tests):
"""Translate 'depends_on' directives into ASP logic."""
for _, conditions in sorted(pkg.dependencies.items()):
for cond, dep in sorted(conditions.items()):
condition_id = self.condition(cond, dep.spec, pkg.name) # create a condition and get its id
self.gen.fact(fn.dependency_condition( # associate specifics about the dependency w/the id
condition_id, pkg.name, dep.spec.name
))
# etc.
```
- [x] unify generation and logic for conditions
- [x] use unified logic for dependencies
- [x] use unified logic for virtuals
- [x] use unified logic for conflicts
- [x] use unified logic for externals
LocalWords: concretizer mpi attr Arg concretize lp cairo fc fontconfig
LocalWords: virtuals def pkg cond dep fn refactor github py
* Rewrite relative dev_spec paths internally to absolute paths in case of relocation of the environment file
* Test relative paths for dev_path in environments
* Add a --keep-relative flag to spack env create
This ensures that relative paths of develop paths are not expanded to
absolute paths when initializing the environment in a different location
from the spack.yaml init file.
Currently, regardless of a spec being concrete or not, we validate its variants in `spec_clauses` (part of `SpackSolverSetup`).
This PR skips the check if the spec is concrete.
The reason we want to do this is so that the solver setup class (really, `spec_clauses`) can be used for cases when we just want the logic statements / facts (is that what they are called?) and we don't need to re-validate an already concrete spec. We can't change existing concrete specs, and we have to be able to handle them *even if they violate constraints in the current spack*. This happens in practice if we are doing the validation for a spec produced by a different spack install.
Signed-off-by: vsoch <vsoch@users.noreply.github.com>
This pull request will add the ability for a user to add a configuration argument on the fly, on the command line, e.g.,:
```bash
$ spack -c config:install_tree:root:/path/to/config.yaml -c packages:all:compiler:[gcc] list --help
```
The above command doesn't do anything (I'm just getting help for list) but you can imagine having another root of packages, and updating it on the fly for a command (something I'd like to do in the near future!)
I've moved the logic for config_add that used to be in spack/cmd/config.py into spack/config.py proper, and now both the main.py (where spack commands live) and spack/cmd/config.py use these functions. I only needed spack config add, so I didn't move the others. We can move the others if there are also needed in multiple places.
Was getting the following error:
```
$ spack test list
==> Error: issubclass() arg 1 must be a class
```
This PR adds a check in `has_test_method` (in case it is re-used elsewhere such as #22097) and ensures a class is passed to the method from `spack test list`.
