[CK_TILE] Integrate CK Tile Dispatcher code generation into
CK Tile Profiler (#7284)
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## Motivation
CK Tile is going to be delivered to hipDNN via CK Dispatcher. Currently
the CK Tile Profiler using CK Builder for generating the profiled
instances from the configuration files that identify the instances that
old CK exposes. We need to replace this instance generation with the CK
Tile Dispatcher codegen.
## Technical Details
The old CK Profiler config files are converted to JSON files that the CK
Tile Dispatcher can digest. The conversion script for configurations is
stored to source control in case we need to update the JSON
configurations later. The dispatcher generates instance libraries per
conv direction (fwd, bwd data, and bwd weight) that are linked to the CK
Profiler executable. I also implemented codegne for the stream-K and
depthwise conv instances. The proposed solution replaces the CK Builder
codegen with the CK Tile Dispatcher codegen.
There are two new methods that are exposed via the dispatcher backend
- `is_supported` - required to enabled the profiler workflow where we
check the applicability of the kernel instance before running it.
- `get_instance_string` - this mainly for verification. This provide the
CK Builder instance string for verifying that the old CK Builder based
profiler and the new CK Tile Dispatcher based profiler have the same
instances.
The rules that limit the generated instances are now collected to a
single location under the dispacther. The CK Builder codegen uses these,
which ensures that the two codegen pipelines are in sync. The next step
(different PR) is to remove the CK Builder codegen pipeline altogether.
## Test Plan
Verified that the old CK Builder based profiler and the new CK Tile
Dispatcher based profiler have the same instances, that is, the
Dispatcher based codgen can generate the same instances as the old CK
Builder.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK][CK TILE] Dispatcher kernel selection heuristic for grouped conv (#6327)
## Motivation
The ML heuristic in dispatcher does not support grouped-conv operator
yet. In this PR, the support for fwd, bdw-data, and bwd-weight
grouped-conv kernels have been added. A tile_engine utility has also
been added to compile and run any selected kernel configuration through
dispatcher infrastructure.
## Technical Details
1. Tile engine utility is added to benchmark each shape with all the
possible kernel+tile_size combinations here -
[https://github.com/ROCm/rocm-libraries/blob/users/yraparti/ck/dispatcher-grouped-conv-heuristics/projects/composablekernel/tile_engine/ops/grouped_conv/grouped_conv_full_benchmark.py](url)
2. New LGBM regressor models for grouped conv are added to models
directory. We have 3 separate models for fwd, bwd-data, and bwd-weights
[https://github.com/ROCm/rocm-libraries/tree/users/yraparti/ck/dispatcher-grouped-conv-heuristics/projects/composablekernel/dispatcher/heuristics/models](url)
3. Implemented lazy GPU initialization (dispatcher/python)
- **Issue**: ProcessPoolExecutor fork() + GPU context caused memory
access faults
- **Solution**: Mirror FMHA pattern - defer GPU initialization until
first run()
- **Changes**:
- setup_multiple_grouped_conv_dispatchers() returns List[Path], not
loaded libs
- GpuGroupedConvRunner.__init__() no longer calls ctypes.CDLL
- Added _ensure_initialized() method for lazy GPU loading
- GPU context created only on first run() call
- **Benefit**: Parallel compilation now works without GPU conflicts
4. Addressed few miscellaneous issues such as:
- Fixed BF16->FP16 naming bug in the dispatcher wrapper
- Added new tile sizes, and comp_v5 pipeline to the arch spec to expand
the kernel selection
- Added automatic padding support for unsupported shapes in dispatcher
runner
- Created a single source of truth between tile_engine and dispatcher
about the architecture and tile_size details
- Build a validation scripts to compare oracle_best vs ml_heuristic
comparison
## Test Plan
1. Validated fwd, bwd-data, and bwd-weight kernels with both known and
unseen data sets with up to 300 problems.
2. Ensured that test cases are added in both dispatcher and tile_engine
to validate the heuristic.
## Test Result
Results on Unseen shapes validated on gfx950
#### Forward Pass Model
- **Training Data**: 48,845 measurements across 1,372 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
- Mean Efficiency: **93.05%**
- Median Efficiency: **96.8%**
- P10 Efficiency: **79.9%**
#### Backward Data Gradient (bwd_data) Model
- **Training Data**: 18,773 measurements across 891 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
- Mean Efficiency: **93.8%**
- Median Efficiency: **96.5%**
- P10 Efficiency: **82.9%**
#### Backward Weight Gradient (bwd_weight) Model
- **Training Data**: 34,900 measurements across 1,508 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
- Mean Efficiency: **96.1%**
- Median Efficiency: **99.2%**
- P10 Efficiency: **89.4%**
## Submission Checklist
- [ x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Vidyasagar Ananthan <vidyasagar.ananthan@amd.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Jan Patrick Lehr <JanPatrick.Lehr@amd.com>
* WIP POC of dispatcher
* Dispatcher python workflow setup.
* Dispatcher cleanup and updates.
Further dispatcher cleanup and updates.
Build fixes
Improvements and python to CK example
Improvements to readme
* Fixes to python paths
* Cleaning up code
* Improving dispatcher support for different arch
Fixing typos
* Fix formatting errors
* Cleaning up examples
* Improving codegeneration
* Improving and fixing C++ examples
* Adding conv functionality (fwd,bwd,bwdw) and examples.
* Fixes based on feedback.
* Further fixes based on feedback.
* Adding stress test for autogeneration and autocorrection, and fixing preshuffle bug.
* Another round of improvements based on feedback.
* Trimming out unnecessary code.
* Fixing the multi-D implementation.
* Using gpu verification for gemms and fixing convolutions tflops calculation.
* Fix counter usage issue and arch filtering per ops.
* Adding changelog and other fixes.
* Improve examples and resolve critical bugs.
* Reduce build time for python examples.
* Fixing minor bug.
* Fix compilation error.
* Improve installation instructions for dispatcher.
* Add docker based installation instructions for dispatcher.
* Fixing arch-based filtering to match tile engine.
* Remove dead code and fix arch filtering.
* Minor bugfix.
* Updates after rebase.
* Trimming code.
* Fix copyright headers.
* Consolidate examples, cut down code.
* Minor fixes.
* Improving python examples.
* Update readmes.
* Remove conv functionality.
* Cleanup following conv removable.
