* Refactor instance_traits_util and add unit tests tests
* Address reviewer comments.
Just adds some TODOs to indicate deprecated layouts in our reflection. Our strategy is to leave the reflection code broad (covering deprecated features), but keep the builder concepts narrow. Once we've removed deprecated features from all instances, we can remove them from reflection.
Also add a comment to the cmake to explain the unit test target test_conv_builder.
* Addressed more reviewer comments.
* Remove duplicate PassThrough::name
Accidentally added this field to the end of the struct, too. The `name` field should be a the start of the struct for consistency.
We need to check all the architectures for build errors. This missing tf32 type came up as a build failure when I compiled for different instinct architectures.
* Add name member to unary elementwise ops.
* Update elementwise_op_name to check for name attribute.
* Require that the layout is derived from BaseTensorLayout struct.
* Disable c++20-compat warnings when building old CK in C++20 mode
Turns out that this creates some warnings for no good reason.
* ck-builder: add missing layouts and element-wise op names
For layouts, we can directly use the ::name attribute, which should
cover all layouts. For element-wise ops, I just added the ones which
are currently missing when compiling CK with -DMIOPEN_REQ_LIBS_ONLY.
* [CK_BILDER] Add compile-time reflection for a convolution instance
Introduce InstanceTraits template metaprogramming framework to enable runtime introspection of device kernel template parameters without requiring implementation knowledge. This reflection system extracts configuration details (block sizes, data types, layouts, tuning parameters) directly from kernel specializations through template
pattern matching. In particular, the GetInstanceString method returns a string that uniquely idenitfies the kernel, by explicitly serializing all template paramter values.
This provides critical functionality for MIOpen integration, since the existing GetTypeString method is ambiguous, and only captures some of the template paramters.
The implementation uses a two-level design: a primary InstanceTraits template declaration in instance_traits.hpp serves as the interface, while kernel-specific specializations (e.g., for DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3) provide the actual extraction logic. This separation allows the reflection system to scale to additional kernel types without modifying the core interface.
Key architectural decisions:
- Forward-declare device kernels in instance_traits.hpp to avoid circular dependencies, since device implementation headers will include the reflection headers
- Use compile-time constants and type aliases to expose kernel parameters, enabling zero-overhead introspection
- Provide a templated instance_string() function that generates human-readable kernel configuration strings by serializing all template parameters in order, useful for debugging and kernel identification
- Guard reflection integration with preprocessor definition CK_EXPERIMENTAL_BUILDER to keep it opt-in until the API stabilizes
- Add GetInstanceString() virtual method to BaseOperator, allowing runtime polymorphic access to compile-time kernel information
This infrastructure also enables upcoming higher-level semantic reflection abstractions (like ConvTraits) to query kernel configurations programmatically.
Includes unit tests validating both the trait extraction accuracy and the string generation format.
Add experimental builder infrastructure for composable_kernel
- Add experimental/builder directory with README documentation.
- Create initial test infrastructure with CMakeLists.txt and placeholder test.
- Update root CMakeLists.txt to support CK_EXPERIMENTAL_BUILDER option.
- Update .gitignore to not treat `experimental/builder` as a CMake build directory.
This establishes the directory structure for a high-level builder pattern that will provide a semantically-clear interface for constructing CK operations, with initial focus on convolution kernels for MIOpen integration.
