title: Design for a Python Toolchain status: Accepted created: 2019-02-12 updated: 2019-02-21 authors:
This doc outlines the design of a Python toolchain rule and its associated machinery. Essentially a new py_runtime_pair toolchain rule is created to wrap two py_runtime targets (one for Python 2 and one for Python 3), thereby making runtimes discoverable via toolchain resolution. This replaces the previous mechanism of explicitly specifying a global runtime via --python_top or --python_path; those flags are now deprecated.
The new toolchain-related definitions are implemented in Starlark. A byproduct of this is that the provider type for py_runtime is exposed to Starlark. We also add to py_runtime an attribute for declaring whether it represents a Python 2 or Python 3 runtime.
The goal is to make the native Python rules use the toolchain framework to resolve the Python runtime. Advantages include:
allowing each py_binary to use a runtime suitable for its target platform
allowing Python 2 and Python 3 targets to run in the same build without hacks
making it easier to run Python-related builds under remote execution
adding support for autodetection of available system Python runtimes, without requiring ad hoc rule logic
removing --python_top and --python_path
bringing Python in line with other rule sets and Bazel's best practices
Non-goal: This work does not allow individual py_binarys to directly name a Python runtime to use. Instead, this information should be worked into either the configuration or a future toolchain constraint system. See the FAQ, below.
A new toolchain type is created at @bazel_tools//tools/python:toolchain_type. This is the type for toolchains that provide a way to run Python code.
Toolchain rules of this type are expected to return a ToolchainInfo with two fields, py2_runtime and py3_runtime, each of type PyRuntimeInfo. They are used for PY2 and PY3 binaries respectively.
def _some_python_toolchain_impl(ctx): ... return [platform_common.ToolchainInfo( py2_runtime = PyRuntimeInfo(...), py3_runtime = PyRuntimeInfo(...))]
If either Python 2 or Python 3 is not provided by the toolchain, the corresponding field may be set to None. This is strongly discouraged, as it will prevent any target relying on that toolchain from using that version of Python. Toolchains that do use None here should be registered with lower priority than other toolchains, so that they are chosen only as a fallback.
PyRuntimeInfo is the newly-exposed Starlark name of the native provider returned by the py_runtime rule. Like PyInfo, it is a top-level built-in name. Also like PyInfo and the native Python rules, it will eventually be migrated to Starlark and moved out of the Bazel repository.
A PyRuntimeInfo describes either a platform runtime or an in-build runtime. A platform runtime accesses a system-installed interpreter at a known path, whereas an in-build runtime points to a build target that acts as the interpreter. In both cases, an “interpreter” is really any executable binary or wrapper script that is capable of running a Python script passed on the command line, following the same conventions as the standard CPython interpreter. Note that any platform runtime imposes a requirement on the target platform. Therefore, any toolchain returning such a PyRuntimeInfo should include a corresponding target platform constraint, to ensure it cannot be selected for a platform that does not have the interpreter at that path. Even an in-build runtime can require platform constraints, for instance in the case of a wrapper script that invokes the system interpreter.
We provide two constraint_settings to act as a standardized namespace for this kind of platform constraint: @bazel_tools//tools/python:py2_interpreter_path and @bazel_tools//tools/python:py3_interpreter_path. This doc does not mandate any particular structure for the names of constraint_values associated with these settings. If a platform does not provide a Python 2 runtime, it should have no constraint value associated with py2_interpreter_path, and similarly for Python 3.
PyRuntimeInfo has the following fields, each of which corresponds to an attribute on py_runtime. (The last one, python_version, is newly added in this doc.)
interpreter_path: If this is a platform runtime, this field is the absolute filesystem path to the interpreter on the target platform. Otherwise, this is None.
interpreter: If this is an in-build runtime, this field is a File representing the interpreter. Otherwise, this is None.
files: If this is an in-build runtime, this field is a depset of Files that need to be added to the runfiles of an executable target that uses this toolchain. The value of interpreter need not be included in this field. If this is a platform runtime then this field is None.
python_version: Either the string "PY2" or "PY3", indicating which version of Python the interpreter referenced by interpreter_path or interpreter is.
The constructor of PyRuntimeInfo takes each of these fields as keyword arguments. The constructor enforces the invariants about which combinations of fields may be None. Fields that are not meaningful may be omitted; e.g. when interpreter_path is given, interpreter and files may be omitted instead of passing None.
It is not possible to directly specify a system command (e.g. "python") in interpreter_path. However, this can be done indirectly by creating a wrapper script that invokes the system command, and referencing that script from the interpreter field.
Finally, we define a standard Python toolchain rule implementing the new toolchain type. The rule's name is py_runtime_pair and it can be loaded from @bazel_tools//tools/python:toolchain.bzl. It has two label-valued attributes, py2_runtime and py3_runtime, that refer to py_runtime targets.
The executable Python rules py_binary and py_test are modified to require the new toolchain type. The Python runtime information is obtained by retrieving a PyRuntimeInfo from either the py2_runtime or py3_runtime field of the toolchain, rather than from --python_top. The python_version field of the PyRuntimeInfo is also checked to ensure that a py_runtime didn't accidentally end up in the wrong place.
Since --python_top is no longer read, it is deprecated. Since --python_path was only read when no runtime information is available, but the toolchain must always be present, it too is deprecated.
Implementation wise, the native PyRuntimeProvider is turned into the user-visible PyRuntimeInfo by adding Starlark API annotations in the usual way (@SkylarkCallable, etc.). A previous version of this proposal suggested defining PyRuntimeInfo in Starlark underneath @bazel_tools and accessing it from the native rules, but this is technically difficult to implement.
A python_version attribute is added to py_runtime. It is mandatory and accepts values "PY2" and "PY3" only.
As a drive-by cleanup (and non-breaking change), the files attribute of py_runtime is made optional. For the non-hermetic case, specifying files is nonsensical and it is even an error to give it a non-empty value. For the hermetic case, files can be useful but is by no means necessary if the interpreter requires no additional in-repo inputs (such as when the “interpreter” is just a wrapper script that dispatches to the platform's system interpreter).
For convenience, we supply a predefined toolchain of last resort, @bazel_tools//tools/python:autodetecting_python_toolchain. This toolchain is registered with lower priority than any user-registered Python toolchain. It simply dispatches to a wrapper script that tries to locate a suitable interpreter from PATH at runtime, on a best-effort basis. It has no platform constraints.
Here is a minimal example that defines a platform whose Python interpreters are located under a non-standard path. The example also defines a Python toolchain to accompany this platform.
# //platform_defs:BUILD load("@bazel_tools//tools/python:toolchain.bzl", "py_runtime_pair") # Constraint values that represent that the system's "python2" and "python3" # executables are located under /usr/weirdpath. constraint_value( name = "usr_weirdpath_python2", constraint_setting = "@bazel_tools//tools/python:py2_interpreter_path", ) constraint_value( name = "usr_weirdpath_python3", constraint_setting = "@bazel_tools//tools/python:py3_interpreter_path", ) # A definition of a platform whose Python interpreters are under these paths. platform( name = "my_platform", constraint_values = [ ":usr_weirdpath_python2", ":usr_weirdpath_python3", ], ) # Python runtime definitions that reify these system paths as BUILD targets. py_runtime( name = "my_platform_py2_runtime", interpreter_path = "/usr/weirdpath/python2", ) py_runtime( name = "my_platform_py3_runtime", interpreter_path = "/usr/weirdpath/python3", ) py_runtime_pair( name = "my_platform_runtimes", py2_runtime = ":my_platform_py2_runtime", py3_runtime = ":my_platform_py3_runtime", ) # A toolchain definition to expose these runtimes to toolchain resolution. toolchain( name = "my_platform_python_toolchain", # Since the Python interpreter is invoked at runtime on the target # platform, there's no need to specify execution platform constraints here. target_compatible_with = [ # Make sure this toolchain is only selected for a target platform that # advertises that it has interpreters available under /usr/weirdpath. ":usr_weirdpath_python2", ":usr_weirdpath_python3", ], toolchain = ":my_platform_runtimes", toolchain_type = "@bazel_tools//tools/python:toolchain_type", )
# //pkg:BUILD # An ordinary Python target to build. py_binary( name = "my_pybin", srcs = ["my_pybin.py"], python_version = "PY3", )
# WORKSPACE # Register the custom Python toolchain so it can be chosen for my_platform. register_toolchains( "//platform_defs:my_platform_python_toolchain", )
We can then build with
bazel build //pkg:my_pybin --platforms=//platform_defs:my_platform
and thanks to toolchain resolution, the resulting executable will automatically know to use the interpreter located at /usr/weirdpath/python3.
If we had not defined a custom toolchain, then we'd be stuck with autodetecting_python_toolchain, which would fail at execution time if /usr/weirdpath were not on PATH. (It would also be slightly slower since it requires an extra invocation of the interpreter at execution time to confirm its version.)
The new @bazel_tools definitions and the PyRuntimeInfo provider are made available immediately. A new flag, --incompatible_use_python_toolchains, is created to assist migration. When the flag is enabled, py_binary and py_test will use the PyRuntimeInfo obtained from the toolchain, instead of the one obtained from --python_top or the default information in --python_path. In addition, when --incompatible_use_python_toolchains is enabled it is an error to set the following flags: --python_top, --python_path, --python2_path, --python3_path. (The latter two were already deprecated.) These flags will be deleted when the incompatible flag is removed.
Because of how the toolchain framework is implemented, it is not possible to gate whether a rule requires a toolchain type based on a flag. Therefore py_binary and py_test are made to require @bazel_tools//tools/python:toolchain_type immediately and unconditionally. This may impact how toolchain resolution determines the toolchains and execution platforms for a given build, but should not otherwise cause problems so long as the build uses constraints correctly.
The new python_version attribute is added to py_runtime immediately. Its default value is the same as the python_version attribute for py_binary, i.e. PY3 if --incompatible_py3_is_default is true and PY2 otherwise. When --incompatible_use_python_toolchains is enabled this attribute becomes mandatory.
py_binary to use a given runtime, say for a particular minor version of Python?This is not directly addressed by this doc. Note that such a system could be used not just for controlling the minor version of the interpreter, but also to choose between different Python implementations (CPython vs PyPy), compilation modes (optimized, debug), an interpreter linked with a pre-selected set of extensions, etc.
There are two possible designs.
The first design is to put this information in the configuration, and have the toolchain read the configuration to decide which PyRuntimeInfo to return. We‘d use Starlark Build Configurations to define a flag to represent the Python minor version, and transition the py_binary target’s configuration to use this version. This configuration would be inherited by the resolved toolchain just like any other dependency inherits its parents configuration. The toolchain could then use a select() on the minor version flag to choose which py_runtime to depend on.
There's one problem: Currently all toolchains are analyzed in the host configuration. It is expected that this will be addressed soon.
We could even migrate the Python major version to use this approach. Instead of having two different ToolchainInfo fields, py2_runtime and py3_runtime, we‘d have a single py_runtime field that would be populated with one or the other based on the configuration. (It’s still a good idea to keep them as separate attributes in the user-facing toolchain rule, i.e. py_runtime_pair, because it's a very common use case to require both major versions of Python in a build. But note that this causes both runtimes to be analyzed as dependencies, even if the whole build uses only one or the other.)
The second design for controlling what runtime is chosen is to introduce additional constraints on the toolchain, and let toolchain resolution solve the problem. However, currently toolchains only support constraints on the target and execution platforms, and this is not a platform-related constraint. What would be needed is a per-target semantic-level constraint system.
The second approach has the advantage of allowing individual runtimes to be registered independently, without having to combine them into a massive select(). But the first approach is much more feasible to implement in the short-term.
py_runtime_pair as opposed to some other way of organizing multiple Python runtimes?Alternatives might include a dictionary mapping from version identifiers to runtimes, or a list of runtimes paired with additional metadata.
The PY2/PY3 dichotomy is already baked into the Python rule set and indeed the Python ecosystem at large. Keeping this concept in the toolchain rule serves to complement, rather than complicate, Bazel's existing Python support.
It will always be possible to add new toolchains, first by extending the schema of the ToolchainInfo accepted by the Python rules, and then by defining new user-facing toolchain rules that serve as front-ends for this provider.
The general pattern for rule sets seems to be to have a single toolchain type representing all of a language's concerns. Case in point: The naming convention for toolchain types is to literally name the target “toolchain_type”, and let the package path distinguish its label.
If the way of categorizing Python runtimes changes in the future, it will probably be easier to migrate rules to use a new provider schema than to use a new set of toolchain types.
@bazel_tools affect the eventual plan to migrate the Python rules to bazelbuild/rules_python?The new PyRuntimeInfo provider and py_runtime_pair rule would have forwarding aliases set up, so they could be accessed both from @bazel_tools and rules_python during a future migration window.
Forwarding aliases would also be defined for the toolchain type and the two constraint_settings. Note that aliasing toolchain_types is currently broken (#7404).
In the initial implementation of this proposal, the predefined autodetecting_python_toolchain will be automatically registered in the user's workspace by Bazel. This follows precedent for other languages with built-in support in Bazel. Once the rules are migrated to rules_python, registration will not be automatic; the user will have to explicitly call a configuration helper defined in rules_python from their own WORKSPACE file.
| Date | Change |
|---|---|
| 2019-02-12 | Initial version |
| 2019-02-14 | Make PyRuntimeInfo natively defined |
| 2019-02-15 | Clarify platform runtime vs in-build runtime |
| 2019-02-21 | Formal approval |