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composable_kernel/dispatcher/codegen/ADDING_NEW_GPU.md
Vidyasagar Ananthan 9e049a32a1 Adding dispatcher architecture (#3300)
* 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.

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* Minor fixes.

* Improving python examples.

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* Remove conv functionality.

* Cleanup following conv removable.
2026-01-22 09:34:33 -08:00

4.7 KiB

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 | Codegen README

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

# 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":

{
  "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:

"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

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

cd ../build
cmake --build . -j8
ctest --output-on-failure

Step 6: Verify

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

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 for full documentation.