Design Kernel

Kernel Design — Shared Workflow

This skill contains everything that is common across all supported DSLs. Once the implementation language is known, also load the matching language-specific design skill for DSL-specific runtime patterns, pitfalls, and API guidance.

Language Selection

Naming Conventions

• Public-facing names, docs, skills, and knowledge-base entries use kebab-case language keys (e.g., cute-dsl).
• Python packages use underscores where required (e.g., cute_python).
• Kernel package nesting: src/mla_var3/kernel/<lang_pkg>/mla/<design>/....
• The shortcut CLI python -m mla_var3.kernel <kernel> [<version>] is the preferred user-facing entry point.

Kernel Structure and Versioning

We use a nested structure for kernel packages inside the kernel sub-package:

1. Language level: which DSL the kernel is implemented in (e.g., kernel.cutile, kernel.cute_python).
2. Layer level: which model layer the kernel targets (e.g., kernel.cutile.mla).
3. Design level: the kernel design (e.g., kernel.cutile.mla.flash_mla).
4. Version level: the kernel version (e.g., kernel.cutile.mla.flash_mla.flash_mla_v2). The first version has no suffix and is called the "base version".
5. Entry module: inside the version package, a module named after the version's full name (e.g., flash_mla_v2.py) must contain the KernelPlan subclass.

Package Nesting Pattern

kernel.<lang_pkg>.mla.<design>.<design>[_v<N>].<design>[_v<N>].py

Rules

• Base version: <design>/<design>/ (no suffix, aliased as v0)
• Version N: <design>/<design>_vN/
• Kernel entry function name MUST match the module filename
• Each version is a sibling package under the design package

CLI Usage

# Full path
python -m mla_var3.kernel.<lang_pkg>.mla.<design> [<version>] [args]
# Shortcut (discovers across all languages)
python -m mla_var3.kernel <design> [<version>] [args]

# Examples
python -m mla_var3.kernel.cutile.mla.mla_var6_plus v4 --b=32 --s=16 --t=4096
python -m mla_var3.kernel mla_var6_plus v4 --b=32 --s=16 --t=4096

Version Creation Checklist

1. Clone the previous version:

   source .venv/bin/activate
   python ./scripts/clone-kernel.py <kernel_full_name> <new_suffix>
   

2. The script rewrites versioned symbols automatically:
   • Python module filenames
   • Decorated kernel function names (@ct.kernel, @cute.kernel, @cute.jit)
   • KernelPlan subclass names
   • Tiling subclass names
   • Intra-package imports
   • Quoted forward references and embedded package-name string literals
3. Modify the cloned files to implement the new optimization.
4. Manual fallback if the script is unavailable:
   • Copy the latest version directory
   • Rename the versioned module files
   • Update class names, function names, imports, and pipeline name strings by hand
5. Verify correctness:

   source .venv/bin/activate
   python -m mla_var3.kernel.<lang_pkg>.mla.<design> <version> --prof_type=disabled --check
   

6. Update devlog with "What changed" section.

KernelPlan Structure

Every kernel version must implement a KernelPlan subclass. The plan() method returns a DSL-specific runtime wrapper (see the language-specific skill for the concrete type).

@dataclass
class MyKernel(KernelPlan):
    b: int = 64; s: int = 1; t: int = 4096  # problem dimensions
    tiling: MyTiling = field(default_factory=MyTiling)

    def prepare_inputs(self, device) -> tuple:
        # Allocate and return input tensors

    def reference_fn(self, *inputs) -> tuple:
        # Reference implementation for --check

    def _autotune_configs(self) -> list[MyTiling]:
        # Candidate tiling configs for autotuner search

    def _algorithmic_flops_bytes(self, tiling) -> tuple[int, int]:
        # Analytical (FLOPs, bytes) for roofline

    def plan(self, *inputs) -> BenchmarkFn:
        # Build executable runtime object (DSL-specific)

    def plan_empty(self, peak_tflops, peak_gbps) -> BenchmarkFn:
        # Roofline-only prediction (no real tensors)

Tiling Dataclass

@dataclass
class MyTiling(Tiling):
    # DSL-specific fields — see the language-specific skill for examples

    def validate(self, pd: "MyKernel") -> bool:
        # Return True if this tiling is valid for the given problem dimensions
        ...

Composition Patterns

Sequential pipeline

def plan(self, *inputs) -> KernelPipeline:
    stage1 = stage1_plan.plan(...)
    stage2 = stage2_plan.plan(...)
    return KernelPipeline(_name="my_pipeline", stages=[stage1, stage2])

Concurrent stages + sequential combine

def plan(self, *inputs) -> KernelPipeline:
    a = plan_a.plan(...)
    b = plan_b.plan(...)
    concurrent = ConcurrentKernels(
        _name="overlap_group", concurrent_kernels=[a, b],
        validate_joint_tiling_fn=validate_fn,
    )
    combine = combine_plan.plan(...)
    return KernelPipeline(_name="pipeline", stages=[concurrent, combine])

Implementation Workflow

1. Read current kernel source — understand the existing implementation
2. Read optimization instructions from orchestrator (specific optimizations to apply)
3. Load the DSL API reference skill — do not hallucinate APIs
4. Check the DSL suitability gate (in the language-specific skill) before implementing
5. Clone the current version with python ./scripts/clone-kernel.py
6. Read optimization detail files from docs/knowledge/ for implementation patterns
7. Implement changes in the new version files
8. Test correctness: --prof_type=disabled --check
9. Update devlog with "What changed" and "High-level description"
10. If committing, use a Conventional Commits message

Knowledge Base Links

• Put reusable algorithmic/device/hardware findings in docs/knowledge/optimizations/ or docs/knowledge/anti-patterns/.
• Put DSL-specific implementation findings in docs/knowledge/languages/<language>/....
• Use the language-specific optimization catalog skill together with the language-specific design skill.

Development Log Entry Template

Add to docs/kernels/<kernel>.md under ## Development log:

### V<N>: [Brief Description]

**Location**: `src/mla_var3/kernel/<lang_pkg>/mla/<kernel>/<kernel>_v<N>/`

**What changed**:
- [Bullet list of changes]

**High-level description of main code changes**:
- [Description of optimizations and how they relate to profiling insights]

Performance metrics, bottleneck analysis, issues, and insights are filled by the profiler agent after profiling.

Designer Output Contract

Return results to the orchestrator in this format:

## New Version: [kernel] [version]

### Changes Applied
1. [change + rationale]

### Files
- Created: [paths]
- Modified: [paths]

### Correctness: [PASS/FAIL]

### Devlog Entry Written: [path]

References

• Optimization patterns: docs/knowledge/optimizations/
• Anti-patterns: docs/knowledge/anti-patterns/
• Kernel devlogs: docs/kernels/<kernel>.md