Files
composable_kernel/dispatcher/codegen/ADDING_NEW_GPU.md
Vidyasagar Ananthan 920acd2c12 [rocm-libraries] ROCm/rocm-libraries#5168 (commit 8b5afcb)
[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.
2026-04-09 17:39:35 +00:00

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# Adding New GPU Architecture Support
Guide for adding support for a new AMD GPU architecture to the CK Tile Dispatcher.
> **See also:** [Main Dispatcher README](../README.md) | [Codegen README](README.md)
## Overview
The dispatcher uses `arch_specs.json` as the **single source of truth** for GPU specifications:
```
arch_specs.json -> generate_arch_specs.py -> arch_specs_generated.py (Python)
-> arch_specs_generated.hpp (C++)
```
## Quick Start
```bash
# 1. Edit arch_specs.json
# 2. Run generator
python generate_arch_specs.py
# 3. Rebuild
cd ../build && cmake --build . -j8
# 4. Test
ctest
```
## Step-by-Step Guide
### Step 1: Edit arch_specs.json
Add new architecture under `"architectures"`:
```json
{
"architectures": {
"gfx1100": {
"family": "rdna3",
"description": "AMD Radeon RX 7000 series (RDNA3)",
"warp_size": 32,
"lds_capacity_kb": 64,
"warp_configs": [
[2, 4, 1],
[4, 2, 1]
],
"warp_tile_combos": {
"fp16_fp16_fp16": [[16, 16, 16], [32, 32, 16]],
"bf16_bf16_bf16": [[16, 16, 16], [32, 32, 16]]
}
}
}
}
```
### Step 2: Configuration Fields
| Field | Description | Example |
|-------|-------------|---------|
| `family` | GPU family | `"cdna3"`, `"rdna4"` |
| `description` | Human-readable name | `"AMD Instinct MI300"` |
| `warp_size` | Wave/warp size | `64` (CDNA), `32` (RDNA) |
| `lds_capacity_kb` | LDS memory in KB | `64` |
| `warp_configs` | Valid `[warp_m, warp_n, warp_k]` | `[[2,2,1], [4,4,1]]` |
| `warp_tile_combos` | Warp tiles per dtype | See below |
### Step 3: Warp Tile Combinations
Map data type combinations to valid warp tile sizes:
```json
"warp_tile_combos": {
"fp16_fp16_fp16": [[32, 32, 8], [16, 16, 16], [32, 32, 16]],
"bf16_bf16_bf16": [[32, 32, 8], [16, 16, 16]],
"fp8_fp8_fp16": [[32, 32, 16], [32, 32, 32]],
"int8_int8_int32": [[16, 16, 32], [32, 32, 16]]
}
```
Key format: `{A_dtype}_{B_dtype}_{C_dtype}`
### Step 4: Run Generator
```bash
cd dispatcher/codegen
python generate_arch_specs.py
```
This generates:
- `arch_specs_generated.py` (Python module)
- `../include/ck_tile/dispatcher/arch_specs_generated.hpp` (C++ header)
### Step 5: Rebuild and Test
```bash
cd ../build
cmake --build . -j8
ctest --output-on-failure
```
### Step 6: Verify
```python
from arch_filter import ArchFilter
filter = ArchFilter("gfx1100")
is_valid = filter.is_kernel_valid(
datatype_a="fp16", datatype_b="fp16", datatype_c="fp16",
tile_m=128, tile_n=128, tile_k=32,
warp_m=2, warp_n=2, warp_k=1,
warp_tile_m=16, warp_tile_n=16, warp_tile_k=16
)
print(f"Valid: {is_valid}")
```
## Reference
### Supported Data Types
| Key | Description |
|-----|-------------|
| `fp16` | Half precision (16-bit) |
| `bf16` | Brain float 16 |
| `fp32` | Single precision (32-bit) |
| `fp64` | Double precision (64-bit) |
| `fp8` | 8-bit float (E4M3) |
| `bf8` | 8-bit brain float (E5M2) |
| `int8` | 8-bit integer |
| `int4` | 4-bit integer |
### GPU Families
| Family | Description |
|--------|-------------|
| `cdna2` | MI200 series (gfx90a) |
| `cdna3` | MI300 series (gfx942) |
| `cdna4` | MI350 series (gfx950) |
| `rdna3` | RX 7000 series (gfx1100) |
| `rdna4` | RX 9000 series (gfx1201) |
### Pipeline LDS Limits
| Pipeline | LDS Limit |
|----------|-----------|
| `compv4` | 32 KB |
| `preshufflev2` | 32 KB |
| `default` | 64 KB |
## Troubleshooting
### "Unknown GPU architecture"
1. Check architecture key matches exactly (e.g., `"gfx942"` not `"GFX942"`)
2. Verify you ran `generate_arch_specs.py`
3. Rebuild C++ code
### Kernels being rejected
```python
from arch_filter import ArchFilter, KernelConfig
filter = ArchFilter("gfx942")
result = filter.validate_kernel(config)
print(f"Valid: {result.valid}")
for error in result.errors:
print(f" Error: {error}")
```
### Missing warp tile combination
1. Check `warp_tile_combos` in `arch_specs.json`
2. Ensure `[warp_tile_m, warp_tile_n, warp_tile_k]` is in the list
3. Verify data type key format
## File Structure
```
codegen/
|---- arch_specs.json # Single source of truth (EDIT THIS)
|---- generate_arch_specs.py # Generator script
|---- arch_specs_generated.py # Generated Python module
+---- ADDING_NEW_GPU.md # This file
include/ck_tile/dispatcher/
|---- arch_specs_generated.hpp # Generated C++ header
+---- arch_filter.hpp # C++ filter
```
## Best Practices
1. **Test thoroughly** - Run all tests after adding a new GPU
2. **Start minimal** - Add only validated configurations
3. **Document sources** - Note where warp tile combinations came from
4. **Keep in sync** - If using tile_engine, keep both updated
---
> **More info:** See [../README.md](../README.md) for full documentation.