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[CK] [CK_Tile] Add GroupConv to Kernel Dispatcher ## Motivation This PR adds CK Tile group convolution (forward, backward-data, backward-weight) support to the kernel dispatcher, matching and unifying with the existing dispatcher GEMM infrastructure in architecture and usability. The dispatcher provides a unified kernel dispatch system with both C++ and Python frontends, and until now only supported GEMM operations. This PR enables framework integrators to use the same declarative kernel workflow for convolutions as they do for GEMM: declare kernels, build a registry JIT, select kernels within the registry at runtime, and dispatch to GPU. Future PRs will include runtime kernel selection heuristics for autotuning of kernel parameters based on (problem, hardware arch). ## Technical Details Grouped convolution support has been added to the CK Tile Dispatcher with generated_conv_backend.hpp enabling dispatcher.run(in, wei, out, problem) for all 6 conv variants (fwd/bwdd/bwdw x 2D/3D), runtime heuristic kernel selection, and GroupedConvKernelKey with full ConvConfigBase fields. Python side adds parallel JIT via registry.build(max_workers) and heuristic registry.select(). Includes 7 C++ and 6 Python examples covering all directions with CPU reference validation, and shared infrastructure improvements (BaseRegistry CRTP, structured exceptions). As a sanity check, JIT compile times for a single kernel remains the same and for multiple kernels there is better parallelism: Kernels | 1 worker | 8 workers 1 | 7.7 s | 7.7 s 2 | 15.9 s | 8.2 s 4 | 33.4 s | 9.7 s 6 | 52.3 s | 10.2 s ## Test Plan 145 ephemeral unit tests have been added to test basic functionality. All 30 examples/integration tests run end-to-end on gfx950 (MI350): 7 C++ conv, 7 C++ GEMM, 6 Python conv, 10 Python GEMM. CPU reference validation for forward, backward-data, and backward-weight (2D) in both C++ and Python examples pass. ## Test Result 30 examples pass. Peak performance: 132 TFLOPS (Batch-32 forward 56x56), 53 TFLOPS (pointwise 1x1). CPU reference accuracy: max_abs_diff < 0.002 for all directions (fp16 vs fp32 reference). ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
116 lines
3.0 KiB
Markdown
116 lines
3.0 KiB
Markdown
# CK Tile Dispatcher - Language Bindings
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This directory contains language bindings for the CK Tile Dispatcher.
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## Structure
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```
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bindings/
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|---- ctypes/ # Python ctypes bindings (C API)
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| |---- gemm_ctypes_lib.cpp # GEMM dispatcher C API
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| |---- conv_ctypes_lib.cpp # Grouped conv dispatcher C API (fwd + bwd_data)
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| |---- conv_bwdw_ctypes_lib.cpp # Grouped conv backward weight C API (separate library)
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| |---- gpu_helper.cpp # CLI helper for Python
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| +---- CMakeLists.txt
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+---- README.md
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```
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## ctypes Bindings
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The ctypes bindings provide a C API that Python can load via `ctypes.CDLL()`.
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### Building
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```bash
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cd build
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cmake .. -DCMAKE_PREFIX_PATH=/opt/rocm
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make dispatcher_gemm_lib dispatcher_conv_lib gpu_helper
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```
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### Usage from Python
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```python
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import ctypes
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# Load the library
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lib = ctypes.CDLL("path/to/libdispatcher_gemm_lib.so")
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# Initialize
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lib.dispatcher_init()
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# Check if problem is supported
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is_supported = lib.dispatcher_is_supported(M, N, K)
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# Run GEMM
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time_ms = ctypes.c_float()
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result = lib.dispatcher_run_gemm(
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A_ptr, B_ptr, C_ptr,
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M, N, K,
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ctypes.byref(time_ms)
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)
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# Cleanup
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lib.dispatcher_cleanup()
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```
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### GEMM API
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| Function | Description |
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|----------|-------------|
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| `dispatcher_init()` | Initialize the dispatcher |
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| `dispatcher_is_supported(M, N, K)` | Check if problem size is supported |
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| `dispatcher_select_kernel(M, N, K, name_buf, buf_size)` | Get kernel name for problem |
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| `dispatcher_run_gemm(A, B, C, M, N, K, time_ms)` | Execute GEMM |
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| `dispatcher_get_kernel_count()` | Get number of registered kernels |
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| `dispatcher_export_registry_json()` | Export registry as JSON |
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| `dispatcher_cleanup()` | Release resources |
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### Grouped Convolution API
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| Function | Description |
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|----------|-------------|
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| `conv_dispatcher_init()` | Initialize the dispatcher |
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| `conv_dispatcher_is_supported(prob)` | Check if problem is supported |
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| `conv_dispatcher_select_kernel(prob, name_buf, buf_size)` | Get kernel name |
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| `conv_dispatcher_run(input, weight, output, prob, stream)` | Execute convolution |
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| `conv_dispatcher_get_kernel_count()` | Get number of registered kernels |
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| `conv_dispatcher_cleanup()` | Release resources |
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## GPU Helper
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The `gpu_helper` executable provides a CLI interface for Python:
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```bash
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./gpu_helper 1024 1024 1024 --validate
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```
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Output is JSON for easy parsing:
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```json
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{
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"problem": {"M": 1024, "N": 1024, "K": 1024},
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"kernel": "gemm_fp16_rcr_...",
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"execution": {
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"time_ms": 0.5,
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"tflops": 4.2
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},
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"validation": {
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"accuracy": 100.0
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},
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"status": "success"
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}
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```
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## Examples
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See the examples that use these bindings:
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- **GEMM**: `dispatcher/examples/gemm/python/`
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### Grouped Convolution
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Grouped convolution C++ headers and Python utilities are in:
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- **C++ Headers**: `dispatcher/include/ck_tile/dispatcher/grouped_conv_*.hpp`
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- **Python Utils**: `dispatcher/python/grouped_conv_utils.py`
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- **Build Script**: `dispatcher/scripts/compile_grouped_conv_examples.py`
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