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Toolchains

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This page describes the toolchain framework, which is a way for rule authors to decouple their rule logic from platform-based selection of tools. It is recommended to read the rules and platforms pages before continuing. This page covers why toolchains are needed, how to define and use them, and how Bazel selects an appropriate toolchain based on platform constraints.

Motivation

Let's first look at the problem toolchains are designed to solve. Suppose you are writing rules to support the "bar" programming language. Your bar_binary rule would compile *.bar files using the barc compiler, a tool that itself is built as another target in your workspace. Since users who write bar_binary targets shouldn't have to specify a dependency on the compiler, you make it an implicit dependency by adding it to the rule definition as a private attribute.

bar_binary = rule(
    implementation = _bar_binary_impl,
    attrs = {
        "srcs": attr.label_list(allow_files = True),
        ...
        "_compiler": attr.label(
            default = "//bar_tools:barc_linux",  # the compiler running on linux
            providers = [BarcInfo],
        ),
    },
)

//bar_tools:barc_linux is now a dependency of every bar_binary target, so it'll be built before any bar_binary target. It can be accessed by the rule's implementation function just like any other attribute:

BarcInfo = provider(
    doc = "Information about how to invoke the barc compiler.",
    # In the real world, compiler_path and system_lib might hold File objects,
    # but for simplicity they are strings for this example. arch_flags is a list
    # of strings.
    fields = ["compiler_path", "system_lib", "arch_flags"],
)

def _bar_binary_impl(ctx):
    ...
    info = ctx.attr._compiler[BarcInfo]
    command = "%s -l %s %s" % (
        info.compiler_path,
        info.system_lib,
        " ".join(info.arch_flags),
    )
    ...

The issue here is that the compiler's label is hardcoded into bar_binary, yet different targets may need different compilers depending on what platform they are being built for and what platform they are being built on -- called the target platform and execution platform, respectively. Furthermore, the rule author does not necessarily even know all the available tools and platforms, so it is not feasible to hardcode them in the rule's definition.

A less-than-ideal solution would be to shift the burden onto users, by making the _compiler attribute non-private. Then individual targets could be hardcoded to build for one platform or another.

bar_binary(
    name = "myprog_on_linux",
    srcs = ["mysrc.bar"],
    compiler = "//bar_tools:barc_linux",
)

bar_binary(
    name = "myprog_on_windows",
    srcs = ["mysrc.bar"],
    compiler = "//bar_tools:barc_windows",
)

You can improve on this solution by using select to choose the compiler based on the platform:

config_setting(
    name = "on_linux",
    constraint_values = [
        "@platforms//os:linux",
    ],
)

config_setting(
    name = "on_windows",
    constraint_values = [
        "@platforms//os:windows",
    ],
)

bar_binary(
    name = "myprog",
    srcs = ["mysrc.bar"],
    compiler = select({
        ":on_linux": "//bar_tools:barc_linux",
        ":on_windows": "//bar_tools:barc_windows",
    }),
)

But this is tedious and a bit much to ask of every single bar_binary user. If this style is not used consistently throughout the workspace, it leads to builds that work fine on a single platform but fail when extended to multi-platform scenarios. It also does not address the problem of adding support for new platforms and compilers without modifying existing rules or targets.

The toolchain framework solves this problem by adding an extra level of indirection. Essentially, you declare that your rule has an abstract dependency on some member of a family of targets (a toolchain type), and Bazel automatically resolves this to a particular target (a toolchain) based on the applicable platform constraints. Neither the rule author nor the target author need know the complete set of available platforms and toolchains.

Writing rules that use toolchains

Under the toolchain framework, instead of having rules depend directly on tools, they instead depend on toolchain types. A toolchain type is a simple target that represents a class of tools that serve the same role for different platforms. For instance, you can declare a type that represents the bar compiler:

# By convention, toolchain_type targets are named "toolchain_type" and
# distinguished by their package path. So the full path for this would be
# //bar_tools:toolchain_type.
toolchain_type(name = "toolchain_type")

The rule definition in the previous section is modified so that instead of taking in the compiler as an attribute, it declares that it consumes a //bar_tools:toolchain_type toolchain.

bar_binary = rule(
    implementation = _bar_binary_impl,
    attrs = {
        "srcs": attr.label_list(allow_files = True),
        ...
        # No `_compiler` attribute anymore.
    },
    toolchains = ["//bar_tools:toolchain_type"],
)

The implementation function now accesses this dependency under ctx.toolchains instead of ctx.attr, using the toolchain type as the key.

def _bar_binary_impl(ctx):
    ...
    info = ctx.toolchains["//bar_tools:toolchain_type"].barcinfo
    # The rest is unchanged.
    command = "%s -l %s %s" % (
        info.compiler_path,
        info.system_lib,
        " ".join(info.arch_flags),
    )
    ...

ctx.toolchains["//bar_tools:toolchain_type"] returns the ToolchainInfo provider of whatever target Bazel resolved the toolchain dependency to. The fields of the ToolchainInfo object are set by the underlying tool's rule; in the next section, this rule is defined such that there is a barcinfo field that wraps a BarcInfo object.

Bazel's procedure for resolving toolchains to targets is described below. Only the resolved toolchain target is actually made a dependency of the bar_binary target, not the whole space of candidate toolchains.

Mandatory and Optional Toolchains

By default, when a rule expresses a toolchain type dependency using a bare label (as shown above), the toolchain type is considered to be mandatory. If Bazel is unable to find a matching toolchain (see Toolchain resolution below) for a mandatory toolchain type, this is an error and analysis halts.

It is possible instead to declare an optional toolchain type dependency, as follows:

bar_binary = rule(
    ...
    toolchains = [
        config_common.toolchain_type("//bar_tools:toolchain_type", mandatory = False),
    ],
)

When an optional toolchain type cannot be resolved, analysis continues, and the result of ctx.toolchains[""//bar_tools:toolchain_type"] is None.

The config_common.toolchain_type function defaults to mandatory.

The following forms can be used:

  • Mandatory toolchain types:
    • toolchains = ["//bar_tools:toolchain_type"]
    • toolchains = [config_common.toolchain_type("//bar_tools:toolchain_type")]
    • toolchains = [config_common.toolchain_type("//bar_tools:toolchain_type", mandatory = True)]
  • Optional toolchain types:
    • toolchains = [config_common.toolchain_type("//bar_tools:toolchain_type", mandatory = False)]
bar_binary = rule(
    ...
    toolchains = [
        "//foo_tools:toolchain_type",
        config_common.toolchain_type("//bar_tools:toolchain_type", mandatory = False),
    ],
)

You can mix and match forms in the same rule, also. However, if the same toolchain type is listed multiple times, it will take the most strict version, where mandatory is more strict than optional.

Writing aspects that use toolchains

Aspects have access to the same toolchain API as rules: you can define required toolchain types, access toolchains via the context, and use them to generate new actions using the toolchain.

bar_aspect = aspect(
    implementation = _bar_aspect_impl,
    attrs = {},
    toolchains = ['//bar_tools:toolchain_type'],
)

def _bar_aspect_impl(target, ctx):
  toolchain = ctx.toolchains['//bar_tools:toolchain_type']
  # Use the toolchain provider like in a rule.
  return []

Defining toolchains

To define some toolchains for a given toolchain type, you need three things:

  1. A language-specific rule representing the kind of tool or tool suite. By convention this rule's name is suffixed with "_toolchain".

    1. Note: The \_toolchain rule cannot create any build actions. Rather, it collects artifacts from other rules and forwards them to the rule that uses the toolchain. That rule is responsible for creating all build actions.
  2. Several targets of this rule type, representing versions of the tool or tool suite for different platforms.

  3. For each such target, an associated target of the generic toolchain rule, to provide metadata used by the toolchain framework. This toolchain target also refers to the toolchain_type associated with this toolchain. This means that a given _toolchain rule could be associated with any toolchain_type, and that only in a toolchain instance that uses this _toolchain rule that the rule is associated with a toolchain_type.

For our running example, here's a definition for a bar_toolchain rule. Our example has only a compiler, but other tools such as a linker could also be grouped underneath it.

def _bar_toolchain_impl(ctx):
    toolchain_info = platform_common.ToolchainInfo(
        barcinfo = BarcInfo(
            compiler_path = ctx.attr.compiler_path,
            system_lib = ctx.attr.system_lib,
            arch_flags = ctx.attr.arch_flags,
        ),
    )
    return [toolchain_info]

bar_toolchain = rule(
    implementation = _bar_toolchain_impl,
    attrs = {
        "compiler_path": attr.string(),
        "system_lib": attr.string(),
        "arch_flags": attr.string_list(),
    },
)

The rule must return a ToolchainInfo provider, which becomes the object that the consuming rule retrieves using ctx.toolchains and the label of the toolchain type. ToolchainInfo, like struct, can hold arbitrary field-value pairs. The specification of exactly what fields are added to the ToolchainInfo should be clearly documented at the toolchain type. In this example, the values return wrapped in a BarcInfo object to reuse the schema defined above; this style may be useful for validation and code reuse.

Now you can define targets for specific barc compilers.

bar_toolchain(
    name = "barc_linux",
    arch_flags = [
        "--arch=Linux",
        "--debug_everything",
    ],
    compiler_path = "/path/to/barc/on/linux",
    system_lib = "/usr/lib/libbarc.so",
)

bar_toolchain(
    name = "barc_windows",
    arch_flags = [
        "--arch=Windows",
        # Different flags, no debug support on windows.
    ],
    compiler_path = "C:\\path\\on\\windows\\barc.exe",
    system_lib = "C:\\path\\on\\windows\\barclib.dll",
)

Finally, you create toolchain definitions for the two bar_toolchain targets. These definitions link the language-specific targets to the toolchain type and provide the constraint information that tells Bazel when the toolchain is appropriate for a given platform.

toolchain(
    name = "barc_linux_toolchain",
    exec_compatible_with = [
        "@platforms//os:linux",
        "@platforms//cpu:x86_64",
    ],
    target_compatible_with = [
        "@platforms//os:linux",
        "@platforms//cpu:x86_64",
    ],
    toolchain = ":barc_linux",
    toolchain_type = ":toolchain_type",
)

toolchain(
    name = "barc_windows_toolchain",
    exec_compatible_with = [
        "@platforms//os:windows",
        "@platforms//cpu:x86_64",
    ],
    target_compatible_with = [
        "@platforms//os:windows",
        "@platforms//cpu:x86_64",
    ],
    toolchain = ":barc_windows",
    toolchain_type = ":toolchain_type",
)

The use of relative path syntax above suggests these definitions are all in the same package, but there's no reason the toolchain type, language-specific toolchain targets, and toolchain definition targets can't all be in separate packages.

See the go_toolchain for a real-world example.

Toolchains and configurations

An important question for rule authors is, when a bar_toolchain target is analyzed, what configuration does it see, and what transitions should be used for dependencies? The example above uses string attributes, but what would happen for a more complicated toolchain that depends on other targets in the Bazel repository?

Let's see a more complex version of bar_toolchain:

def _bar_toolchain_impl(ctx):
    # The implementation is mostly the same as above, so skipping.
    pass

bar_toolchain = rule(
    implementation = _bar_toolchain_impl,
    attrs = {
        "compiler": attr.label(
            executable = True,
            mandatory = True,
            cfg = "exec",
        ),
        "system_lib": attr.label(
            mandatory = True,
            cfg = "target",
        ),
        "arch_flags": attr.string_list(),
    },
)

The use of attr.label is the same as for a standard rule, but the meaning of the cfg parameter is slightly different.

The dependency from a target (called the "parent") to a toolchain via toolchain resolution uses a special configuration transition called the "toolchain transition". The toolchain transition keeps the configuration the same, except that it forces the execution platform to be the same for the toolchain as for the parent (otherwise, toolchain resolution for the toolchain could pick any execution platform, and wouldn't necessarily be the same as for parent). This allows any exec dependencies of the toolchain to also be executable for the parent's build actions. Any of the toolchain's dependencies which use cfg = "target" (or which don't specify cfg, since "target" is the default) are built for the same target platform as the parent. This allows toolchain rules to contribute both libraries (the system_lib attribute above) and tools (the compiler attribute) to the build rules which need them. The system libraries are linked into the final artifact, and so need to be built for the same platform, whereas the compiler is a tool invoked during the build, and needs to be able to run on the execution platform.

Registering and building with toolchains

At this point all the building blocks are assembled, and you just need to make the toolchains available to Bazel's resolution procedure. This is done by registering the toolchain, either in a WORKSPACE file using register_toolchains(), or by passing the toolchains' labels on the command line using the --extra_toolchains flag.

register_toolchains(
    "//bar_tools:barc_linux_toolchain",
    "//bar_tools:barc_windows_toolchain",
    # Target patterns are also permitted, so you could have also written:
    # "//bar_tools:all",
)

Now when you build a target that depends on a toolchain type, an appropriate toolchain will be selected based on the target and execution platforms.

# my_pkg/BUILD

platform(
    name = "my_target_platform",
    constraint_values = [
        "@platforms//os:linux",
    ],
)

bar_binary(
    name = "my_bar_binary",
    ...
)
bazel build //my_pkg:my_bar_binary --platforms=//my_pkg:my_target_platform

Bazel will see that //my_pkg:my_bar_binary is being built with a platform that has @platforms//os:linux and therefore resolve the //bar_tools:toolchain_type reference to //bar_tools:barc_linux_toolchain. This will end up building //bar_tools:barc_linux but not //bar_tools:barc_windows.

Toolchain resolution

For each target that uses toolchains, Bazel's toolchain resolution procedure determines the target's concrete toolchain dependencies. The procedure takes as input a set of required toolchain types, the target platform, the list of available execution platforms, and the list of available toolchains. Its outputs are a selected toolchain for each toolchain type as well as a selected execution platform for the current target.

The available execution platforms and toolchains are gathered from the WORKSPACE file via register_execution_platforms and register_toolchains. Additional execution platforms and toolchains may also be specified on the command line via --extra_execution_platforms and --extra_toolchains. The host platform is automatically included as an available execution platform. Available platforms and toolchains are tracked as ordered lists for determinism, with preference given to earlier items in the list.

The resolution steps are as follows.

  1. A target_compatible_with or exec_compatible_with clause matches a platform if, for each constraint_value in its list, the platform also has that constraint_value (either explicitly or as a default).

    If the platform has constraint_values from constraint_settings not referenced by the clause, these do not affect matching.

  2. If the target being built specifies the exec_compatible_with attribute (or its rule definition specifies the exec_compatible_with argument), the list of available execution platforms is filtered to remove any that do not match the execution constraints.

  3. For each available execution platform, you associate each toolchain type with the first available toolchain, if any, that is compatible with this execution platform and the target platform.

  4. Any execution platform that failed to find a compatible mandatory toolchain for one of its toolchain types is ruled out. Of the remaining platforms, the first one becomes the current target's execution platform, and its associated toolchains (if any) become dependencies of the target.

The chosen execution platform is used to run all actions that the target generates.

In cases where the same target can be built in multiple configurations (such as for different CPUs) within the same build, the resolution procedure is applied independently to each version of the target.

If the rule uses execution groups, each execution group performs toolchain resolution separately, and each has its own execution platform and toolchains.

Debugging toolchains

If you are adding toolchain support to an existing rule, use the --toolchain_resolution_debug=regex flag. During toolchain resolution, the flag provides verbose output for toolchain types or target names that match the regex variable. You can use .* to output all information. Bazel will output names of toolchains it checks and skips during the resolution process.

If you'd like to see which cquery dependencies are from toolchain resolution, use cquery's --transitions flag:

# Find all direct dependencies of //cc:my_cc_lib. This includes explicitly
# declared dependencies, implicit dependencies, and toolchain dependencies.
$ bazel cquery 'deps(//cc:my_cc_lib, 1)'
//cc:my_cc_lib (96d6638)
@bazel_tools//tools/cpp:toolchain (96d6638)
@bazel_tools//tools/def_parser:def_parser (HOST)
//cc:my_cc_dep (96d6638)
@local_config_platform//:host (96d6638)
@bazel_tools//tools/cpp:toolchain_type (96d6638)
//:default_host_platform (96d6638)
@local_config_cc//:cc-compiler-k8 (HOST)
//cc:my_cc_lib.cc (null)
@bazel_tools//tools/cpp:grep-includes (HOST)

# Which of these are from toolchain resolution?
$ bazel cquery 'deps(//cc:my_cc_lib, 1)' --transitions=lite | grep "toolchain dependency"
  [toolchain dependency]#@local_config_cc//:cc-compiler-k8#HostTransition -> b6df211