[CK TILE] Increase default kPerXdl for grouped convolution instances (#7465)
## Summary
Increases the default `kPerXdl` used in CK Tile grouped convolution
instance generation for forward, backward-data, and backward-weight
operations.
### Changes in `generate_instances.py`
- **Larger default `kPerXdl` for all fp16/bf16 tile sizes**:
`get_k_mfma()` now
returns `32` for `m/nPerXdl = 16` and `16` for `m/nPerXdl = 32`.
- **Cap `kPerXdl` to `kPerBlock`**: All three parsers
(`parse_fwd_instances`, `parse_bwd_weight_instances`,
`parse_bwd_data_instances`) now clamp the computed value with `min(...,
k_per_block)` to prevent generating invalid instances where `kPerXdl >
kPerBlock`.
### Expected impact
Higher `kPerXdl` increases the number of MFMA instructions issued per
warp per inner-loop iteration, improving arithmetic intensity and
reducing pipeline stall overhead for memory-bound shapes.
[CK Tile] Support multi-vector reads in static encoding patterns (#7528)
## Motivation
The thread-raked / warp-raked / block-raked static tile distribution
patterns in `ck_tile` silently produce wrong results when the contiguous
tile dimension is larger than `warp_size * vector_size`, because the
encoding has no per-thread iteration dimension along X.
Concretely, with `M_Tile=N_Tile=128`, `VectorSize{A,B,C}=1` in
`ConvConfigComputeV3`, the grouped convolution backward-weight example
reports about 50 percent wrong values, with errors starting exactly at
the `X0*X1 = 64` boundary. The second pass over the contiguous dim is
never performed.
This PR extends the encoding so multi-vector reads in the contiguous
tile dimension are supported, while keeping every existing call site
bit-for-bit identical.
## Technical Details
Three files changed.
### 1. `include/ck_tile/core/algorithm/static_encoding_pattern.hpp`
Add a per-thread X iteration dimension in all three raked
specializations:
- `X0 = min(warp_size, XPerTile / X1)` — threads in X dim
- `X1 = min(LargestVec, VecSize)` — vector size per access
- `X2 = XPerTile / (X0 * X1)` — number of X-iters per thread (new)
`X2` is gated with `if constexpr (X2 == 1) { old } else { new }` in both
`make_2d_static_tile_distribution()` and
`make_shuffled_2d_static_tile_distribution()`.
The new encoding places `X2` in the middle of the Ys iteration list,
which preserves reverse symmetry between the regular `<..., X2, X1>` and
shuffled `<X1, X2, ...>` encodings.
Patterns updated: `thread_raked`, `warp_raked`, `block_raked`.
### 2. `include/ck_tile/core/tensor/transpose_tile.hpp`
Added a parallel `else if constexpr (... && NDimY == 3 && ...)` branch
alongside the existing `NDimY == 2` branch. The original branch is
byte-for-byte unchanged.
Both branches dispatch to the same `transpose_tile2d_impl_in_thread`,
whose body has always been NDimY-generic (iterates with `static_for<0,
NDimY, 1>` and `number<NDimY>{}`).
### 3.
`experimental/grouped_convolution_tile_instances/generate_instances.py`
Removed the two now-obsolete skip guards in `parse_bwd_weight_instances`
and `parse_bwd_data_instances`:
```python
if m_per_block > (warp_size * a_scalar_per_vector) or n_per_block > (warp_size * b_scalar_per_vector):
print(f"Skipping instance {instance_id} with multiple warps per continous tile dim since it's not supported yet.")
continue
```
Other unrelated skips (V5 / V6 / ASYNC_V4 pipeline gating,
irregular-load shapes, scalar-per-vector > tile size) are kept
untouched.
### Compatibility
Strict. Every existing caller has `X2 == 1` and therefore hits the
original encoding path verbatim. No upstream config or pipeline behavior
changes.
## Test Plan
The grouped convolution example is the natural exerciser since
`GroupedConvUniversalPipelineAgBgCrPolicy` selects `thread_raked` for
both A and B tiles, and all three conv directions share the same
`ConvConfigComputeV3`.
For each test below we ran:
```
./build/bin/tile_example_grouped_conv_bwd_weight [-prec={fp16,bf16}]
./build/bin/tile_example_grouped_conv_fwd [-prec={fp16,bf16}]
./build/bin/tile_example_grouped_conv_bwd_data [-prec={fp16,bf16}]
```
with `ConvConfigComputeV3` tile/vector parameters tweaked to cover both
code paths:
| Test | M / N / K | VecA/B/C | A path | B path | dtype |
|------|-------------|----------|------------|----------------|-------------|
| T1 | 16/64/32 | 4/8/4 | old (X2=1) | old (X2=1) | fp16 |
| T2 | 128/128/64 | 2/2/2 | old (X2=1) | old (X2=1) | fp16 |
| T3 | 256/256/64 | 1/1/1 | old (X2=1) | new (X2=4) | fp16 |
| T5 | 256/256/64 | 1/1/1 | old (X2=1) | new (X2=4) | fp16 (3 dir)|
| T4b | 128/128/128 | 1/1/1 | new (X2=2) | new (X2=2) | fp16 + bf16 (3
dir) |
A larger T4a (256/256/128) was attempted to stress both A and B with
X2>1 on bigger tiles but was blocked by the gfx942 hardware LDS cap (128
KB > 64 KB limit), independent of this PR.
For the generator change we ran:
```
python3 generate_instances.py --mode profiler --direction all
```
and verified `Skipping instance ... with multiple warps per continous
tile dim` no longer appears (count went from non-zero to 0); other skip
categories are unchanged.
`clang-format-18` was applied to both modified `.hpp` files (matches the
repo's `.clang-format`).
## Test Result
- T1 and T2 (compat-strict, every X2 is 1, old code path): `correct`.
Confirms existing callers are unaffected.
- T3 (X2=4 on B only): `correct`. First true exercise of the new NDimY=3
encoding + transpose branch.
- T5 (T3 across `fwd` + `bwd_data` + `bwd_weight`, fp16): all 3
`correct`.
- T4b (X2>1 on both A and B, fp16 + bf16, all 3 directions): all 6 runs
`correct`.
- Generator: 0 `multiple warps per continous tile dim` skips remaining;
other skips unchanged.
Sample run output (T4b, bf16, bwd_data):
```
shape: tile_gemm_shape_128x128x128x4_1x4x1_16x16x32
pipeline: pipeline_AgBgCrCompV3_128x128x128_256_1x1x1_1x4_1x1x1_..._DoubleSmemBuffer_0
Vector size A: 1, Vector size B: 1, Vector size C: 1
0.934907 ms, 8.34683 TFlops, 34.3178 GB/s
Relative error threshold: 0.00390625 Absolute error threshold: 0.25
The CPU verification result is: correct
```
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
[CK][CK Tile] Grouped Conv Backward Weight Streamk instances (#5904)
## Motivation
Add streamk instance to grouped convolution backward weight profiler.
## Technical Details
- New instances for grouped conv backward weight with streamk
## Test Plan
test_grouped_convnd_bwd_weight_tile
## Test Result
passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Graner, Johannes <johannes.graner@amd.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
[CK_TILE] Add depthwise conv2d forward kernel (FP16/FP32) (#6838)
## Motivation
CK currently has no kernel optimized for depthwise convolution
(G=C_in=C_out, C=K=1 per group) and existing generic paths perform
poorly for this workload. This PR adds a dedicated depthwise conv
forward kernel in CK Tile.
## Technical Details
Adds a dedicated depthwise conv2d forward op to CK Tile that performs
direct convolution rather than falling back to the generic GEMM path.
The kernel is templatized by filter size, stride, and data type, and
compiled into ~60 instances covering common configurations (kernel
3/5/7/9, stride 1/2, FP16/FP32). Supports both CDNA (gfx942/gfx950) and
RDNA (gfx1100/gfx1200) architectures.
## Test Plan
- [x] Correctness and performance validated on gfx942, gfx950, and
gfx1100, with ckProfiler `grouped_conv_fwd` as baseline.
- [ ] MI300A (gfx942) and gfx1200 validation.
## Submission Checklist
- [x ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1137
---------
Co-authored-by: GenDu <Gen.Du@amd.com>
[CK_TILE] Add conv bwd data tests (#5646)
## Motivation
This PR adds tests for CK Tile's convolution backward data operation to
enable functionality regression tracking and error-detection.
## Technical Details
Currently only NHWGC/GKCYX/NHWGK and NDHWGC/GKCZYX/NDHWGK(2 dim and 3
dim channel-last) layouts are being tested, since only they are
implemented in CK Tile. Current tests support FP16, BF16 and FP32
datatypes and various different convolutions scenarios. The tested
instances are listed in
`experimental/grouped_convolution_tile_instances` directory.
## Test Result
All implemented tests are working properly and passing.
---------
Co-authored-by: Ville Pietilä <>
Co-authored-by: Ville Pietilä <188998872+vpietila-amd@users.noreply.github.com>
Co-authored-by: Jakub Piasecki <jakpia21@gmail.com>
[CK_TILE, CK_BUILDER] Add bwd data to CK Tile profiler (#5516)
## Motivation
We want close the performance gap between old CK and CK Tile for bwd
data convolutions. To achieve this, we need tow things
- Configurations for the old CK kernel instances such that we can map
them into CK Tile instances.
- Support in CK profiler to run the CK Tile instance with the same API
as for old CK instances.
## Technical Details
Extracted kernel configurations from old CK. The codegen python script
for CK Tile convs is extended to support also bwd data. The generated
instances are added to the CMake build (target
`device_grouped_conv_bwd_data_tile_instances`).
A new profiler op (`grouped_conv_bwd_data_tile`) has been added to the
CK Profiler. The API is same as for old CK's profiler op
`grouped_conv_bwd_data`.
---------
Co-authored-by: Ville Pietilä <>
[CK][CK Tile] Move grouped conv cpp instances to build dir (#5609)
## Motivation
Move grouped conv .cpp instances to build dir. Fix generate instances
script.
## Technical Details
Avoid CI problem when instances in experimental directory are not
removed
## Test Plan
test_grouped_convnd_*_tile
## Test Result
Pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK][CK Tile] Fix dram step for KM/KN layouts in V1 pipeline (#5470)
## Motivation
Fix v1 pipeline for KM/KN layouts by passing correct step for dram tile
window.
## Technical Details
- Fix dram step for KM/KN layouts in V1 pipeline
- Disable instances which use more threads than warp size in continous
dim (not supported in ck tile yet)
- Use 1x1 specialization for explicit gemm
- Use two stage for vectorsize =1 and sizeof(datatype) ==2
- remove not needed check sinze GetVectorSizeA/B check if vector size is
fixed
## Test Plan
test_grouped_convnd_bwd_weight_tile
## Test Result
passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-966
[CK][CK Tile] Grouped Convolution Backward Weight set of fixes (#5387)
## Motivation
Grouped Convolution Backward Weight split k fixes for CK tile kernels
## Technical Details
- get k batch from kargs to get deduced k batch
- multiply zeroing size by data type size
- disable v6 (producing a incorrect results)
## Test Plan
test_grouped_convnd_bwd_weight_tile
## Test Result
Pass
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Ville Pietilä <>
[CK_TILE, CK_BUILDER] Add two-stage bwd weight kernels to CK Tile profiler (#5237)
## Motivation
PR #4797 added CK Tile bwd weight kernels to the CK Profiler. The
two-stage kernels were not supported in the initial PR. This PR adds the
the missing bwd weight two-stage kernels to the CK Profiler.
## Technical Details
Extended the CK Tile conv builder factory to build also the elementwise
ops required for the two-stage kernels. Extended the CK Builder for CK
Tile instance to accept the two-stage flag as part of the algorithm
configuration.
## Test Plan
Added units tests for CK Builder that verify the two-stage kernel
construction.
## Test Result
If CI passes, the added unit tests are passing.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Ville Pietilä <>
[CK][CK Tile] Improvements for grouped conv fwd tile profiling (#5114)
## Motivation
Improve profiling for grouped convolution forward for better comparison
between CK and CK Tile
## Technical Details
- Include preprocessing time for ck tile
- Add flush cache for conv fwd profiler
- Switch configs to builder reflect
- Add KPerXdl deduce
- Add non-grouped ported instances
## Test Plan
test_grouped_convnd_fwd_tile
## Test Result
pass
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-786
[CK][CK Tile] Add grouped conv backward weight tile test and fix tr load in BASE_V1 pipeline (#5115)
## Motivation
Test grouped conv backward weight from ck tile and fix incorrect values.
## Technical Details
- Add test for CI
- Add daily tests
- Fix transpose load in BASE_V1 pipeline
## Test Plan
test_grouped_convnd_backward_weight_tile
## Test Result
in progress
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-783
[CK_TILE] Add CK Tile bwd weight profiler (#4797)
## Motivation
To compare old CK and CK Tile, we need to extend the current CK profiler
to support running also CK Tile instance with the same API. In order to
have the same instance coverage in CK Tile compared to the old CK, I've
added code generation from old CK configurations to CK Tile instances
using the CK Builder.
## Technical Details
- The codegen python script for CK Tile fwd convs is extended to support
also bwd weight and bwd data.
- The generated instances are added to the CMake build (target
`device_grouped_conv_bwd_weight_tile_instance`s).
- A new profiler op (`grouped_conv_bwd_weight_tile`) has been added to
the CK Profiler.
---------
Co-authored-by: Ville Pietilä <>
Co-authored-by: Bartlomiej Kocot <barkocot@amd.com>
[CK][CK TILE] Improve oob check (#4791)
## Motivation
Improve OOB checks. Remove permutes which have been generated by thread
buffer zero clear. at now in assembly there is only condmask instead of
permute + condmask.
Change number of KPack for generated instances
## Technical Details
Remove permute instructions from assembly
## Test Plan
test_grouped_convnd_fwd_tile
## Test Result
passed
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: jakpiase <jakpia21@gmail.com>
[CK] CK Tile grouped convolution direct load
## Motivation
CK Tile grouped convolution forward direct load support.
## Technical Details
Basic pipeline for direct load and new instances for forward for v1 and
v4 pipelines.
## Test Plan
test_grouped_convnd_fwd_tile
## Test Result
CI pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-130
