composable_kernel/dispatcher/codegen

CK Tile Unified Code Generators

Single source of truth for GEMM and Grouped Convolution kernel generation.

See also: Main Dispatcher README for installation and core concepts.

Shared Infrastructure

Both GEMM and Grouped Conv generators share common code via codegen_common.py:

• TileConfig - Dataclass for tile dimensions
• TraitConfigBase - Base for kernel trait configurations with arch-aware validation
• CommonTypeMappings - Dtype-to-C++ type mappings
• parallel_generate() - Parallel kernel generation with per-kernel progress logging
• Arch-aware expansion helpers (valid_wave_configs, valid_warp_configs, etc.)

Quick Start

GEMM

cd dispatcher/codegen

# Generate standard FP16 kernels
python3 unified_gemm_codegen.py \
    --output-dir ../build/generated_kernels \
    --datatype fp16 \
    --layout rcr \
    --variants standard

# Generate all variants
python3 unified_gemm_codegen.py \
    --output-dir ../build/generated_kernels \
    --variants standard preshuffle multi_d

Grouped Convolution

cd dispatcher/codegen

# Generate forward FP16 grouped conv kernels
python3 unified_grouped_conv_codegen.py \
    --output-dir ../build/generated_kernels \
    --datatype fp16 \
    --variant forward \
    --ndim-spatial 2

# Generate backward data kernels
python3 unified_grouped_conv_codegen.py \
    --output-dir ../build/generated_kernels \
    --variant backward_data \
    --ndim-spatial 2

Using from Python

from ctypes_utils import CodegenRunner, KernelConfig

# Generate from specific config
config = KernelConfig(tile_m=256, tile_n=256, tile_k=64)
codegen = CodegenRunner()
result = codegen.generate_from_config(config)

# Generate variant
result = codegen.generate("preshuffle")

# Generate all
results = codegen.generate_all()

Command Line Options

Option	Values	Description
--output-dir	path	Output directory
--datatype	fp16, bf16, fp32, int8	Data type
--layout	rcr, rrr, crr, ccr	Matrix layouts
--gpu-target	gfx942, gfx90a, gfx950	Target GPU
--variants	standard, preshuffle, multi_d	Kernel variants
--preselected	fp16_rcr_essential, etc.	Predefined kernel set

Layout Notation

• R = Row-major, C = Column-major
• Order: A, B, C (e.g., rcr = A row, B col, C row)

Variants

Standard

Basic GEMM: C = A x B

PreShuffle

Optimized weight access with LDS pre-shuffling. Best for large matrices.

Multi-D

Element-wise fusion: C = op(A x B + D0 + D1 + ...)

Supported ops: PassThrough, MultiDAdd, Relu, Gelu, Sigmoid, Tanh

Output Structure

generated_kernels/
|---- gemm_fp16_rcr_compv4_..._128x128x32_....hpp          # GEMM kernels
|---- gemm_fp16_rcr_compv4_..._preshuffle.hpp
|---- gemm_fp16_rcr_compv4_..._multid_Relu_d1.hpp
|---- grouped_conv_fwd_fp16_nhwgc_..._128x128x32_....hpp   # Grouped conv kernels
+---- ...

Configuration Files

arch_specs.json

GPU architecture specifications (single source of truth):

{
  "architectures": {
    "gfx942": {
      "family": "cdna3",
      "warp_size": 64,
      "warp_configs": [[2, 2, 1], [4, 4, 1]],
      ...
    }
  }
}

preselected_kernels.py

Curated kernel sets for common use cases.

Adding New GPU Support

See ADDING_NEW_GPU.md for complete guide.

Quick steps:

1. Edit arch_specs.json
2. Run python generate_arch_specs.py
3. Rebuild

Troubleshooting

Issue	Solution
"Arguments not supported"	Check tile config validity
Missing element-wise op	Check elementwise_ops.hpp
Compilation errors	Verify C++17, include paths

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More info: See ../README.md for full documentation.