cuda.core: Replace string literals with enums in public API

[mdboom]

This is just a result of a sweep for using strings where enums could be used. This is just to aid the discussion about whether they should be addressed.

Several places in the cuda.core public API accept or return string values chosen from a small, fixed vocabulary. These are candidates for replacement with proper Python enum types, which would improve discoverability, IDE autocompletion, isinstance checking, and catch typos at the call site rather than at runtime.

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Findings

Compilation / Linking pipeline

Location	Kind	Values
Program.__init__(code_type)	arg	"c++", "ptx", "nvvm"
Program.compile(target_type)	arg	"ptx", "cubin", "ltoir"
Program.backend	return	"NVRTC", "NVVM", "nvJitLink", "driver"
Program.pch_status	return	"created", "not_attempted", "failed"
Linker.link(target_type)	arg	"cubin", "ptx"
Linker.backend	return	"nvJitLink", "driver"
ObjectCode.code_type	return	"cubin", "ptx", "ltoir", "fatbin", "object", "library"

Memory

Location	Kind	Values
ManagedMemoryResourceOptions.preferred_location_type	arg	"device", "host", "host_numa"
ManagedMemoryResource.preferred_location	return (tuple[0])	"device", "host", "host_numa"
VirtualMemoryResourceOptions.allocation_type	arg	"pinned", "managed"
VirtualMemoryResourceOptions.location_type	arg	"device", "host", "host_numa", "host_numa_current"
VirtualMemoryResourceOptions.handle_type	arg	"posix_fd", "generic", "win32_kmt", "fabric"
VirtualMemoryResourceOptions.granularity	arg	"recommended", "minimum"
VirtualMemoryResourceOptions.self_access / peer_access	arg	"rw", "r"

Note: the VirtualMemoryResourceOptions fields already use Literal[...] type hints, which partially addresses the issue, but proper enums would still improve iteration, isinstance checks, and user discoverability.

Graph API

Location	Kind	Values
GraphAllocOptions.memory_type	arg	"device", "host", "managed"
AllocNode.memory_type	return	"device", "host", "managed"
ConditionalNode.cond_type	return	"if", "while", "switch"

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Proposed enum groupings

Enum name	Values	Consumers
CodeType	cubin, ptx, ltoir, fatbin, object, library	ObjectCode.code_type, Linker.link, Program.compile
SourceType	c++, ptx, nvvm	Program.__init__
CompilerBackend	NVRTC, NVVM, nvJitLink, driver	Program.backend, Linker.backend
PchStatus	created, not_attempted, failed	Program.pch_status
MemoryType	device, host, managed	GraphAllocOptions, AllocNode, ManagedMemoryResource
LocationType	device, host, host_numa, host_numa_current	VirtualMemoryResourceOptions, ManagedMemoryResourceOptions
ConditionalType	if_, while_, switch_	ConditionalNode.cond_type
HandleType	posix_fd, generic, win32_kmt, fabric	VirtualMemoryResourceOptions
Granularity	recommended, minimum	VirtualMemoryResourceOptions
AccessMode	rw, r	VirtualMemoryResourceOptions

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Discussion points

• The compilation/linking pipeline (Program, Linker, ObjectCode) is the highest-impact area since string values flow across multiple API boundaries.
• Switching from str to enum would be a breaking change for existing callers. One migration path is to accept both str and the enum type during a deprecation window.
• Some values ("c++", "if", "while") are not valid Python identifiers and would need renaming (e.g. SourceType.CPP, ConditionalType.IF).

[mdboom]

Note that the above list is just where strings are used where enums could be, not the opposite, where we use enums but strings are not accepted. We could also produce that list.

My proposed solution:

• Everywhere we accept something "enum like", the type is SpecificEnum | str. (SpecificEnum | Literal[...] would be better from a type perspective, but IMHO is not worth the repeating yourself burden).
• Everywhere we return something "enum like", we return an enum, and str(enum) gives a string.

This seems to address type checking much better than what we have today, and also reduces a class of errors where it is easier to mis-spell a string than an enum value.

It also gives users a fast path (using enums) that using strings may not have.

[mdboom]

From a user/API perspective, I think consensus from the meeting is that it would be ideal to support both strings and enums, i.e.

call_func(MyEnum.VALUE)
get_result() == MyEnum.VALUE

and

call_func("value")
get_result() == "value"

would be equivalent and supported everywhere "logically enum-like things" would be needed.

It is desirable to give type checkers and linters as much as they can to deal with here. Much of that will be theoretical today, since we don't have .pyi files, but we should plan for a future where we do.

We have two sets of enums in cuda-core: (A) those that are internal to cuda-core and exist only for the Pythonic layer and (B) those that are re-exposures of C-style int enums from CUDA libraries (usually through cuda.bindings). The enum implementation does not need to be the same in both cases (it's an implementation detail), but having a consistent interface across all of them seems desirable (especially whether they implicitly convert to/from strings), so the user doesn't have to know which type of enum they are dealing with.

After doing a bunch of benchmarking of alternatives, I've reached the following conclusion:

(A) Existing cuda-core enums

The stdlib enum.StrEnum does not have the performance issues of enum.IntEnum, and is every bit as fast as using strings. For the existing cuda-core enum-like-things using strings that don't need to map directly to C (int) enums, this is the right choice. It will allow both strings or StrEnum at all of our call sites with basically zero overhead. Making this change, our users can decide whether they prefer enums or strings and the type checking vs. convenience tradeoffs.

Extending FastEnum slightly to use them for the above purpose adds about 40ns of overhead vs. StrEnum. Unlike FastEnum, StrEnum values do not have docstrings, so the doc pages can only list their values, not add explanations of what each value means. I think we treat this as a doc build problem: it would better to extend/fix Sphinx somehow to make that work than pay a runtime penalty just to have good docs. 🙃

(B) Re-exposed C-style enums

These are currently using FastEnum, since they are integer values. These do not currently implicitly convert to/from strings, but can be easily extended to do so. The performance penalty of doing that accepting either a string or a FastEnum at each call site is around 40ns. I think it is worth that to have a consistent interface for all of our enums across cuda-core.

Urgency?

All of this is a little complex and risky to get squeezed in right before the 1.0 release. So what happens if we don't and instead do it later?

Fundamentally, we are /extending/ the set of values our functions can accept, which is not API breaking.

For argument types of type (A), we would be going from str to str | Enum (or in some cases we have Literal["a", "b", "c"] which would go to str | Enum). Type (B) would go from Enum to Enum | str.

For returning values, we would be going from str to Enum, and we would have to ensure that result == "value" would continue to work. If we need to retain isinstance(result, str) or result is "value", that may not be possible. That seems fine to me, barring XKCD 1172.

What we couldn't do later (easily) is decide that we want to remove uses of strings and use only enums everywhere (or conversely for type (B) accept only strings everywhere).

I think I'm ok to commit to that much and not try to rush this through (though I think it is generally a good idea to do this).

[mdboom]

I think we treat this as a doc build problem: it would better to extend/fix Sphinx somehow to make that work than pay a runtime penalty just to have good docs.

FWIW: It's ugly but this works:

class SourceType(StrEnum):
 CXX = "c++"
 PTX = "ptx"
 NVVM = "nvvm"

SourceType.CXX.__doc__ = "C++ source code."