[CK][CK Tile] Drop profiler for experimental builder codegen
(#8573)
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## Motivation
Switch to dispatcher profiler for ck tile conv.
## Technical Details
- Switch to dispatcher profiler for ck tile conv.
- Drop profiler for experimental codegen
- Minor fixes for bwd data printing
- Minor fixes for 3d conv in dispatcher codegen
## Test Plan
test_grouped_conv*tile
## Test Result
Passed
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK Tile] Rule-based configuration generation in CK
Dispatcher codegen (#8157)
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## Motivation
The CK Tile Dispatcher code generation for CK Tile Profiler relies on
flat JSON files to list the generated configurations. This approach has
the following problems
- The JSON files are verbose
- The JSON files get easily out of sync with the CK Builder .config
files from which they were generated from.
- The JSON file based configuration make it hard to list explicitly the
rules that govern the instance generation.
## Technical Details
Replaced the JSON files with a rule based configuration. To preserve the
existing functionality, the `profiler` and the `tests` instance sets are
generated directly from the CK Builder config files. The JSON config
files are removed from source control, and the "on-the-fly" generation
guarantees that the Dispatcher codegen uses up to date configurations.
This is PR introduces six different rule sets for the CK Tile Dispatcher
code generation
1. `profiler`: matches with the old JSON set of profiler configurations.
2. `tests`: matches with the old JSON set of tests configurations.
3. `full`: full configuration set created from a rule-based config
selection
4. `full-tests`: a subset of `full` for generating configurations for
convolution integration tests.
5. `tiny`: a subset of `full-tests` to produce the minimal set of
configurations to test the Dispatcher codegen.
6. `default`: the default rules, which corresponds to the existing
heuristic rules for configuration selection. This ensures that ML based
kernel selection doesn't get broken.
The main use of the `full` rule set is to define a reasonable solution
space for the possible implicit GEMM configurations. We start from the
configurations that allowed by the device architecture. The `full` rule
set defines the relevant tile sizes for each convolution direction. From
the tile size we have a curated mapping to the number of waves over the
different GEMM axes, i.e., we describe how many waves each GEMM
dimensions corresponds to. The GEMM-K wave tile dimension can be
computed from the other parameters and does not need to be listed
explicitly.
An orthogonal axis to the tiling strategy is the vectorization strategy.
This mainly defined by the data type and hardware as in general, we want
to use the maximum possible load widths. The maximum sizes for each
convolution direction variant are defined by the implicit GEMM matrix
dimensions. For cases where have a low number of channels per
convolution group, we need smaller vector load sizes. These are captured
by the `VecStrategy` enumeration in the codegen rules.
The problem with the rule based configuration selection is that we "over
generate" configurations. The old JSON configurations compose
approximately 25% of all configuration that the `full` rule set creates.
The additional configurations are valid, but they many not provide any
performance benefits. Hence, we keep the `profiler` and `tests` rule set
for now to avoid building an excessive amount configurations by default.
The `full` rule set can be taken into use by specifying CMake
configuration flag `-D DISPATCHER_RULE_SET=full`. By default, the
`tests` rule set is used, i.e., we don't change the existing bahaviour.
## Test Plan
Added a new stage in the CI/CD pipeline that ensures the Dispatcher
codegen rules are up to date. Otherwise the functionality is covered by
the existing CI/CD tests. There are no functional changes to the
convolution kernels. Only how the different instances are generated.
## Test Result
If the CK Tile conv instances build without errors, the Dispatcher
codegen is generating valid code. If all tests in CI/CD pipeline are
passing, the Dispatcher codegen generates valid instances.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Remove Stream-K from old CK
## Motivation
Since Stream-K has a CK Tile implementation, we no longer need Stream-K
in old CK. Hence, this PR removes Stream-K from old CK.
## Technical Details
All Stream-K artifacts in old CK have been removed including examples,
tests, kernels, and CK profiler artifacts.
## Test Plan
Ran a CI run on the branch before publishing PR.
## Test Result
All tests passed.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Claude Sonnet 4 <noreply@anthropic.com>
[CK] Grouped Convolution Global Load/Store instances
## Motivation
Support global load and store in grouped convolutions using instance
factory.
## Technical Details
- add new instances for each direction
- add new tests for large cases
## Test Plan
New test for large cases
## Test Result
pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1255
[CK] [MIOPEN] Split convolution library by layout
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# Split Composable Kernel convolution operations by data layout
TLDR:
1. This is a reorganization of files, folders, and CMakeLists for
convolution kernels and facilitates a splitting of the convolution
library into layouts.
2. The speedup can range anywhere between 15-40% depending on the target
architecture for miopen only builds of CK. For TheRock nightly builds of
CK, which includes both miopen and hip tensor kernel instances, this
constituted in a 10% decrease in compile time for gfx1100.
## Overview
Based on https://github.com/ROCm/composable_kernel/pull/3010/ (except
keeping 1 static library)
## What MIOpen Actually Uses
MIOpen **exclusively uses:
- **NHWGC** for all 2D convolutions
- **NDHWGC** for all 3D convolutions
This is because MIOpen's tensor descriptors natively use channel-last,
group-aware formats.
## Key Changes
### 1. Layout-Based Directory Structure
Reorganized convolution instance files from flat per-operation to
hierarchical layout-based structure. For example:
**Before:**
grouped_conv2d_fwd/
├── device_grouped_conv2d_fwd_xdl_nhwgc_*.cpp (MIOpen-required)
├── device_grouped_conv2d_fwd_xdl_gnhwc_*.cpp (optional)
└── device_grouped_conv2d_fwd_xdl_ngchw_*.cpp (optional)
**After:**
grouped_conv2d_fwd/
├── nhwgc/ ← MIOpen-required
│ ├── xdl/device_grouped_conv2d_fwd_xdl_*.cpp
│ └── wmma/device_grouped_conv2d_fwd_wmma_*.cpp
├── gnhwc/ ← Optional (excluded with MIOPEN_REQ_LIBS_ONLY)
└── ngchw/ ← Optional (excluded with MIOPEN_REQ_LIBS_ONLY)
### 2. Preserved Umbrella Library
As before, all convolution operations are consolidated into a single
static `device_conv_operations` library:
- Aggregates layout-specific instance object files via
`ADD_CONV_LAYOUT_INSTANCES` macro
- **Default build:** Includes all layouts (NHWGC + GNHWC + NGCHW +
NDHWGC + GNDHWC + NGCDHW)
- **MIOpen build (`MIOPEN_REQ_LIBS_ONLY=ON`):** Includes only NHWGC and
NDHWGC layouts
### 3. Binary Size Reduction
When building with `MIOPEN_REQ_LIBS_ONLY=ON`:
**Layouts Included (26 targets):**
- 7× NHWGC instances (2D operations + variants)
- 19× NDHWGC instances (3D operations + variants)
**Layouts Excluded (16 targets):**
- 3× GNHWC instances (2D operations)
- 3× NGCHW instances (2D operations)
- 3× GNDHWC instances (3D operations)
- 3× NGCDHW instances (3D operations)
- 2× GNWC instances (1D operations)
- 1× NWGC instance (1D operations)
- 1× additional NHWGC instance (grouped_conv1d_fwd, not needed by
MIOpen)
This represents a **~38% reduction in instance targets** (16 excluded
out of 42 total
layout-specific targets).
### Testing
- ✅ All existing CK tests link against the umbrella library
- ✅ MIOpen links successfully with the reduced umbrella library
- ✅ Profiler builds with all layout-specific targets explicitly listed
Notes from the Author:
Since this refactor moved most of the convolution files further into
subdirectories, I concentrated on ensuring that no source files were
excluded, including sharded sources: Targets are correctly migrated — no
missing targets, no shard count mismatches.
[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] suppress compiler warnings while building pytorch. (#7760)
## Motivation
Recently added compiler flags that are required to suppress false
warnings by latest staging compiler are not recognized by older compiler
versions and are triggering an avalanche of warnings. Previous attempt
to suppress them by using -Wno-unknown-warning-option flag didn't help,
because that flag wasn't recognized either and just added more warnings.
I've verified that current approach by checking the clang version
actually works as intended and makes the warnings go away.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK][CK Tile] Grouped Conv Tile Profiler Verification (#7021)
## Motivation
Improve CK Tile Conv Profiler for perf measurements.
## Technical Details
Add option to disable verification and script to run conv tile profiler.
## Test Plan
CI
## Test Result
Pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-75
[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] Suppress new staging compiler errors (#7384)
## Motivation
This should make new builds with staging compiler pass.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK][CK Tile] Conv Bwd Data flush cache and profiling improvements (#6090)
## Motivation
Improve accuracy of conv bwd data perf measurements
## Technical Details
- enable flush cache
- for grouped conv we zero conv input(gemm output) inside device op, so
we also include this in time measurement
- for non-grouped conv we zero conv input(gemm output) outside device op
(in profile_conv_bwd_data_impl.hpp) so it is not included.
- In this pr I changed it to include zeroing if time_kernel/flush cache
is enabled so at now you should have more fair comparison. I changed it
only for time_kernel/flush_cache because MIOpen run own zeroing for
non-grouped solvers.
## Test Plan
test_grouped_conv_bwd_data_*
## Test Result
CI pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] fix clang lifetime bound error in ck_builder. (#5571)
## Motivation
This resolves the compilation error with latest develop compiler branch.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[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] Grouped Convolution backward weight profiler flush cache (#5454)
## Motivation
Flush cache to get more stable results during profiling old ck and ck
tile.
## Technical Details
Flush cache before each kernel call and one more first run.
## Test Plan
test_grouped_conv_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.
AICK-966
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
[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][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_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 Profiler] Instance selection for grouped conv profilers (#4800)
## Motivation
This PR adds instance selection support for ckProfiler grouped
convolution operations (forward, backward data, backward weight),
allowing users to run specific kernel instances rather than sweeping all
available instances.
When profiling or debugging convolution kernels, users often need to
test specific kernel configurations without running the full instance
sweep. This is particularly useful for:
- Debugging a specific failing instance
- Profiling a known-best configuration
- Quick validation during development
## Technical Details
**Features added**:
- `--instance <id>` flag to run only the N-th valid instance (0-indexed)
- `--list-instances` flag to list all valid instances without running
any kernels
- Named arguments can appear anywhere on the command line
- Best instance index is now printed with results for reference
- Python script support via `-ii` / `--instance_index` arguments
**Design decisions**:
- Named arguments (`--instance`, `--list-instances`) instead of
positional to avoid conflicts with existing parameters
- Instance index refers to the N-th valid instance (0-indexed), not the
global instance index
- Auto-disable verification when `--list-instances` is used for fast
enumeration
- Shared utilities in `profiler_arg_utils.hpp` to deduplicate parsing
logic
## Test Plan
Manual testing with various scenarios:
List all valid instances:
```bash
./bin/ckProfiler grouped_conv_fwd <usual args> --list-instances
```
Run only instance 5:
```bash
./bin/ckProfiler grouped_conv_fwd <usual args> --instance 5
```
Test cases:
- Single instance selection
- List instances mode
- Out-of-bounds instance index (verified warning messages)
- No instance flag (runs all instances - default behavior)
- All three operations (fwd, bwd_data, bwd_weight)
## Test Result
All test scenarios passed:
- Instance selection correctly filters kernel executions
- List mode enumerates valid instances without running kernels
- Invalid indices produce appropriate warnings without crashing
- Default behavior (all instances) unchanged when flags not provided
- Consistent behavior across all three grouped convolution operations
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[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>
173 implement device grouped gemm fixed nk for rdna4 (#4299)
## Proposed changes
This PR adds an RDNA4 implementation of the device_grouped_gemm_fixed_nk
instance library using for WMMA.
The implementation is based on the existing
DeviceGroupedGemm_Xdl_Fixed_NK design and reuses the same high-level
structure, but replaces the XDL kernel with a WMMA-based one. It uses
the GridwiseGemm_wmma_cshuffle_v3 kernel.
At this stage, the focus is functional correctness and compatibility,
not performance tuning.
## Technical Details
- Device struct for grouped gemm fixed NK
- Example code for the WMMA version
- Unit tests for both new wmma implementation and the reference XDL code
(previously missing)
- Generic ck profiler interface with the purpose of calling unit tests.
## Checklist
Please put an into the boxes that apply. You can also fill these out
after creating the PR. If you're not sure, please don't hesitate to ask.
- [x] I have added tests relevant to the introduced functionality, and
the unit tests are passing locally
- [x] I have added the test to REGRESSION_TESTS list defined at the top
of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more
than 30 seconds to run.
- [ ] I have added inline documentation which enables the maintainers
with understanding the motivation
- [ ] I have removed the stale documentation which is no longer relevant
after this pull request
- [x] (If this change is user-facing) I have added release notes which
provide the end users with a brief summary of the improvement from this
pull request
- [x] I have run on all changed files
- [x] Any dependent changes have been merged
## Discussion
If this is a relatively large or complex change, feel free to start a
discussion by explaining why you chose the solution you did and what
alternatives you considered
---
🔁 Imported from
[ROCm/composable_kernel#3668](https://github.com/ROCm/composable_kernel/pull/3668)
🧑💻 Originally authored by @bidlekm
---------
Co-authored-by: Marton Bidlek <marton.bidlek@streamhpc.com>
Co-authored-by: Erwin Terpstra <erwin.terpstra@streamhpc.com>
Co-authored-by: bidlekm <bidlekmarton@gmail.com>
Co-authored-by: assistant-librarian[bot] <assistant-librarian[bot]@users.noreply.github.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* [Compiler] Addressing new compiler warnings
Clang enables new lifetime warnings in production and we see build
errors due to this with the staging compiler.
The attributes added in this PR are suggested by the compiler. However,
I'm not very familiar with the code base, so the changes may be
incorrect.
* Update some more instances
* Adds file-level ignores via clang diagnostic pragma
The number of instances was large, so I decided to use file-level scope
to disable the warning via pragma clang diagnostic ignored.
It also showed this warning coming from the gtest dependency. For that,
I did add the respective command line flag to the CMake variables. I
don't know if this is acceptable or not.
* This adds the remaining instances
For a build on gfx90a.
* fix clang format
* Adding couple more instances from gfx1200 build
* Fixed another few instances
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* Adding remaining flavors for grouped conv fwd
As titled. Following variants are added:
- grouped_conv2d_fwd_dynamic_op
- grouped_conv3d_fwd_dynamic_op
- grouped_conv3d_fwd_bilinear
- grouped_conv3d_fwd_convscale
- grouped_conv3d_fwd_convinvscale
- grouped_conv3d_fwd_convscale_add
- grouped_conv3d_fwd_convscale_relu
- grouped_conv3d_fwd_scale
- grouped_conv3d_fwd_combconvscale
- grouped_conv3d_fwd_scaleadd_scaleadd_relu
* Fix incomplete parsing of types from source names in add_instance_library() cmakelists function so we don't build f8 on RDNA3.
* Do not build f8 / bf8 only flavor tests on RDNA3
* Make sure we have proper generic instances for all instance lists related to the post-ces extra flavors, with scalarPerVector = 1. Then disable all but one generic instance per instance list to reduce compile time.
* Post rebase fix: Template parameters for Grouped Conv Fwd Device Impl got tweaked upstream.
* adding int8 and fp16 overloads to the elementwise operations
* fixed copilot nits
* Addressing review comments:
- removed unnecessary examples for dynamic op
- removed unnecessary conv specalizations for all the flavors
- removed spurious bilinear and scale source files
* clang-format
* reduced no of tests
---------
Co-authored-by: Wojciech Laskowski <wojciech.laskowski@streamhpc.com>
* ck-builder: restructure testing conv
In order to prepare for bwd of conv testing, this commit moves some
files and types around so that we can reuse ckt::Args for both forward
and backwards convolution.
* ck-builder: decouple fwd_ck.hpp and fwd_reference.hpp from fwd.hpp
This will allow us to more easily include fwd.hpp from backwards
definitions, which is required for initializing bwd values.
* ck-builder: fix layout of test_ckb_conv_bwd_weight_xdl_cshuffle_v3
Turns out that the supplied layout isn't actually supported...
* ck-builder: ck and reference conv integration for bwd weight
* ck-builder: ck bwd weight execution test
* ck-builder: ckt::run support for ck-tile bwd weight
* ck-builder: ck tile bwd weight execution test
* ck-builder: extra debug printing in MatchesReference
* ck-builder: make ckt::run return RunResult
This type is more convenient than std::tuple, as it will allow us to
use google test matchers with this in the future.
* ck-builder: RunResult matcher
Using EXPECT_THAT(..., SuccessfulRun()) will generate a check and a nice error
message about how and why running an algorithm failed.
* ck-builder: doc fixes
* ck-builder: add missing headers
* Added bias_bnorm_clamp instances.
* fwd_bias_bnorm_clamp comp instances
* fwd_bias_bnorm_mem_inter and mem_intra instances
* fwd_bias_bnorm_merged_group_instances
* fwd_bias_bnorm_clamp_conv3d_bf16 and f16 instances
* Device level changes for fwd_bias_bnorm_clamp
* Added the test to the regression test list.
* Removed the part 2 and 2x instances
* Removed the irrelevant checks in wmma
* Refactored the instances to adapt to new device implementation
* Updated the reference and include files
* enabling tests
* Added missing profiler
* Added missing instance entry , deleted by mistake
* Reduce bias bnorm clamp instances to only a single generic one.
* Clean up cmakelists file
* clang-format
* Change bias bnorm clamp tests to use monotone initialization values to avoid tiny off-integer gemm results on RDNA3 from blowing up.
* Renaming some instance lists and add functions to be more standardized.
* Commented out non default instances.
---------
Co-authored-by: kiefer <kiefer.van.teutem@streamhpc.com>
* [CK-TILE] Guard against compiler lexer diagnostic
A recent change to Clang added a lexer-level diagnostic about that C2y
language feature. Since that is lexer level, the `__extension__`
compiler built-in does not work as it is only respected *after* the
lexer when parsing.
This change adds guarding pragmas to disable the diagnostic in the
lexer and not lead to warnings being treated as errors.
* Fixing still existing build issue
Once the one warning was removed, another one poppoed up. Both are
related to the same c2y feature. Thus, ignoring both.
* clang-format handling
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
* Updated the set of tests for FP16
* Fix typo
* Moved f16xi4 test under the correct data layout group
* example for gemm_universal_bf16
* Adding examples for gemm_wmma instances
* Added the missing parameters
* Fixed review comments and added executable to cmakeLists
* Fixing clang format
* Fixing build erros
* Fixed compilation failure.
* Modified some code as per gemm_universal_examples
* Fixed the gemm specialization error
* Fixed the build errors.
* Fix strides of a/b_thread_desc
The descriptors are larger than needed (even though the compiler don't alloc registers for unused values).
* Load in M/NRepeat dims with thread copy's slice instead of a loop
* Clone BlockwiseGemmXdlops_pipeline_v1 for WMMA implementation
* Implement Intrawave and Interwave variants of pipeline v1
* Add instances for Interwave and Intrawave v1
* Add instances with ABlockLdsExtraM and BBlockLdsExtraN = 0
* Remove instances that are too slow (mostly because of register spilling)
* Add a workaround for fp8/bf8->f32 packed conversion issue
* Add instances for Interwave and Intrawave v1
* Enable profiling of mixed precision with f8 and int4 on WMMA
* Fix segfault in profiler when B is pk_i4_t
b_device_buf's size in bytes is larger than b_k_n_permute so b_device_buf.ToDevice reads out-of-bounds.
* Remove instances that are too slow (mostly because of register spilling)
* Add missing add_device_gemm_wmma_universal_f8_f8_bf16 declarations
* Add test case for bf16_i4
* Add missing Regular tests
* Add test_gemm_universal_xdl/wmma_fp16 to REGRESSION_TESTS
They take more than 30 seconds
* Fix a bug that fp16_i4 validation passes only with PermuteB
A permutation required by conversion from pk_i4_t to half_t does not
depend on PermuteB, they can be used independently.
* Use PermuteB with f16_i4 in most instances (as xdl)
Some instances use PermuteB = false for checking correctness.
See also the previous commit.
* Fix cache flushing for pk_i4
* Add mixed precision examples
* Disable all tests and instances with f8 on gfx11
Even though f8_f16 and f16_f8 don't require f8 WMMA instructions,
gfx11 still lacks hardware instructions for fast f8->f32 conversion.
* Add FP16 KM_NK and KM_KN test suites for XDL
These tests were added to common .inc for better testing of WMMA instances
* Support multiple D in GridwiseGemm_wmma_cshuffle_v3
DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters.
* Use ThreadGroupTensorSliceTransfer_v7r3
* Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support
* Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma
* Implement DeviceGemmMultipleD_Wmma_CShuffleV3
* Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3
* Prepare gemma_add tests for adding wmma
* Add gemm_add_fastgelu instances and test
* Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API
ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use
DeviceGemmMultipleDSplitK instances there.
* removed unnecessary ck parts from compilation
* initial gemm_add_multiply instance implementations
* fixed profiler help message for gemm_add_multiply
* improved multiply_add profiler layout help
* fixed template arguments for test instances
* added test for gemm_add_multiply
* Support multiple D in GridwiseGemm_wmma_cshuffle_v3
DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters.
* Use ThreadGroupTensorSliceTransfer_v7r3
* Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support
* Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma
* Implement DeviceGemmMultipleD_Wmma_CShuffleV3
* Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3
* Prepare gemma_add tests for adding wmma
* Add gemm_add_fastgelu instances and test
* Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API
ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use
DeviceGemmMultipleDSplitK instances there.
* switched to splitK interface
* log print added to splitk benchmarks
* revert main cmake comments
* newline change reverted
* added add_fastgelu instances
* revert unintended change in xdl add_fastgelu
* created gemm_add_add_fastgelu instances
* created fastegelu instances
* added tests for all splitk fastgelus
* Added tests.
* multiply_add instances created
* updates to add_multiply splitk instances
* splitk xdl test fixes
* added wmma multiply_multiply instances
* fixed ONLY_XDL_AND_WMMA_KERNELS tag
* Added gemm_add examples for wmma v1 and v3
* fixed / workarounded i8 instances
* Modified the v3 code to added one fp16 bxdl instance.
* added bf16 xdl instance.
* adding gemm_add wmma_cshuffle and other support
(cherry picked from commit ec447e7f564095ea969eddc39ec77b843aa52976)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* add instances into camkelists
(cherry picked from commit 23bf2d2771c939ea3ca7f493433c55255bffd08e)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* This is work in progress, edited the template parameters in order to build
(cherry picked from commit b4fde8a3314cb44659c4bbda35f1a0133c63dc41)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* temp work saved, changed the BDataType to f16 or bf16 since wmma currently not support non-equal A and B datatype
(cherry picked from commit 22fbd68f1db458ab50780a394ee2544c7a1484d1)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* added datatype and use clang-format-12
(cherry picked from commit ae4e853682ef1bb27784b2f965b4a66b3751ceec)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* Fixing build errors
* Added instances for v3
* Adding instances and executables
* Code update of template parameters modified.
* Renamed file.
* Added tests.
* resolved error tests.
* Fixing build errors
* Updated comments
* removed the changes as per the MR review comment.
* Updated tests.
* fp8 instances - not tested
* Restored the Cmake file that was reverted by mistake during rebase.
* fixed wmma_op test
* Updated comments.
* Updated the template parameter description
* fixed rdna4 instances
* fixed back compatibility on gfx11
* cleanups
* fix ckProfiler
* one more cmake fix
* added fp8 instances
* Updated tests to ad BF16 instances as per review comment
* Added include file and cleaned up(as per review comment)
* Updated and optimized the example code for all types.
* Fixed clang format
* Resolve "Implement `device_gemm_bilinear` for RDNA4"
* test generalization to handle FP16 shuffle better
* added missing changes
* Added bf16 wmma instance for add_relu
* Added f16 wmma instance and corrected bf16 instance errors.
* Added instances to Cmake
* Modified the template parameters to make the instances work.
* Fixed typo in profiler
* Added v3 instances for gemm_add_relu
* addressed core review comments
* Added test for gemm_add_relu wmma instance
* Cleaned up the code.
* Added examples for gemm_add_relu
* Fixing typo to resolve build errors.
* Fixes applied to fix the precision loss.
* fix billinear test after merge
* Removed the old wmma instances.
* Added wrapper and renamed the wmma_v3 instances
* Updated copyrights and added wrappers.
* Fixes applied according to review comments
* Apply 1 suggestion(s) to 1 file(s)
Co-authored-by: Robin Voetter <robin@streamhpc.com>
* Removed the old wmma instances.
* Updated wrapper for the v3 instances
* removed the old wmma examples
* Renamed the v3 instances
* Deleted the gtest file added by mistake.
* Updated thge profiler with wrapper
* Fixed test errors.
* Fixed the review comments
* Fixed the if condition MACROS.
* REVERTED THE PROFILER CHANGES
* Revert "REVERTED THE PROFILER CHANGES"
This reverts commit 21cb98546c.
* Revert "Fixed test errors."
This reverts commit 13efcc6fe1.
* Revert "Updated thge profiler with wrapper"
This reverts commit 536f86661d.
* Added missing wrapper instances
* Updated copyrights.
* Fixed typo.
* Fixed copyrights.
* Updated copyrights.
* updated copyrights.
* comments on the atomics workaround
* fixed cmake comment
* Fix bug from merge
* clang-format-18
* Fix compilation error
* multi_abd wmma support:
- Add multiple A and B support to multiple D implementation (gridwise level)
- Add multi_abd GEMM (device level)
- Add instances (xdl parity)
- Add tests (both xdl and wmma)
- Add examples
- Add ckProfiler support (both xdl and wmma)
* Fix bug in device print function
* Fix unused template parameter
* Add support for fwd conv in gridwise implementation. Identical to run function for bwd data.
* Initial device implementation for grouped conv fwd multiABD wmma cshuffleV3. Functional but needs some fixups and extra features in the future.
* Make relevant profilers print the number of valid instances to aid testing.
* Add instances for all vanilla 2D and 3D flavors for f16 and bf16, only one instance per instance list to save compile time for now. Also added incomplete set of comp instances and bias_clamp for f16 2D, just to make sure the multiple-D aspects of the device implementation are working.
* Reset output buffer after each run in profile_grouped_conv_fwd_impl().
* Disable sharding for the new instances for now, has tendency to lead to linker errors on repeat builds.
* Add CTranspose optimization for NCHW cases just like in xdl cshuffle non-v3 device implementation.
* Add instances for all 8-bit 3D vanilla grouped conv fwd types, including mixed types but with the exception of deprecated f16 comp fp8. Adapt test so we can test 8-bit and mixed types.
* Add int8 instances for 2D vanilla grouped conv fwd all layouts.
* Implement merged groups in device impl and add instances for merged groups 3D vanilla conv fwd
* Add merged groups instances for all 2D vanilla grouped conv fwd types and layouts.
* Implement multi-AB support for grouped conv fwd and add example.
* Add 1D instances
* Add D layout tests to IsSupportedArgument()
* Add comp and mem instances for all vanilla 2D grouped conv fwd types. Skipping "x2" and "part2" instance lists, can be added later without special names if necessary.
* Add comp and mem instances for vanilla 3D grouped conv fwd. Skipped 2x and part2 instances, can be added later in the same instance lists.
* Add some more tests for vanilla grouped conv fwd
* Add 2D bias clamp instances and tests
* Add 3D bias clamp instances and tests
* Add 2D and 3D clamp instances and tests
* Unify problem sizes across vanilla and clamp flavor tests
* Clean up device implementation: remove old todos, remove unnecessary comments and print statements, tweak description, wrap all prints in env check.
* Implement rotating memory and flush cache. Requires ad-hoc buffer size calculations.
* Remove wmma fp8 and bf8 instances when not targetting gfx12
* Add newer instances to DEVICE_INSTANCES so the main ckProfiler can build
* Remove old years for newly created files.
* No need to time kernels for now.
* Fixup comments
* Pass struct args to Gridwise Run() function by reference.
* Don't use workspace memory in the case where A needs explicit transposition but B does not.
* Move calculation of rotating memory buffer sizes to Argument member functions.
* After the convolution to gemm transformation, the resulting 2D tensor descriptors are not necessarily RowMajor or ColumnMajor, so things should not rely on this distinction. Therefore, pass all RowMajor to the Gridwise and use a special version of CheckValidity that does not rely on 2D tensor layouts.
* Unify xdl and wmma example code for grouped conv fwd scaleadd ab
* Go back to passing RCR 2D tensor layouts to gridwise gemm, and use CRC for the CTranspose case. Also remove the special convolution version of checkValidity(). It seems like no matter what 2D tensor layouts you pass to the gridwise gemm, and no matter if you are using extraMN, and no matter if you are using the convolution version of checkvalidity, the results of all tests are the same.
* Add wmma scaleadd ab instances to the device factory and add a completely new scaleadd_ab gtest test for wmma cshufflev3 and xdl. Currently there is no profiler for scaleadd_ab so I made my own inside the test. Furthermore for XDL only the (NDHWGC, GKZYXC, NDHWGK) layout combination existed in the instance factory so that is the only one I added for wmma cshufflev3 and the gtest test as well. Another layout is tested in example 62, for xdl and wmma cshufflev3.
* Add support for V3 pipeline (tested). To be able to support num_loop < 3 we need the fixes from the batched gemm gemm MR which was already merged upstream, so just need to rebase or merge.
* Small post-merge fixup, everything seems to work.
* Do not build or run Xdl operations with Wmma backend for now. Will be reverted before upstreaming.
* Extend scaleadd_ab instance lists
* Extend merged groups instance lists, including adaptations of xdl "2x" instances.
* Extend "comp" instance lists, including "2x" and "part2" instances. 2x instances disabled for now since they do not compile.
* Extend "mem" instance lists.
* Extend regular instance lists.
* Fixup comments and ignored kernel arg name
* Properly use the splitN offsets for D tensors in the gridwise Run() function. Was necessary to pass the bias_clamp_large_cases test.
* Make sure all strides in ComputePtrOffset are at least value initialized to avoid undefined strides. Not convinced this struct is properly initialized in other code / future code.
* Re-enable sharding for wmma cshufflev3 instances
* Post merge fix to vanilla test
* Optionally allow num_k_loop <= PrefetchStages in gridwise CheckValidity. Use this for grouped conv fwd but not in general.
* Remove spurious ck_tile changes that were presumably introduced somewhere in the repeated merging from develop.
* Post-merge fixes. Make sure the new gridwise gemm wmma v3 common Run function can be used. Remove splitK, and forceThreadTileTransfer for now. Also add CShuffle epilogue argument.
* Disable FP8 / BF8 testing on CDNA1/2, it doesn't work anymore and needs to be either fixed or removed.
* Re-enable old wmma instances
* Re-enable Linqun's Xdl Wmma instances
* Small post-merge fixes
* Fix copyright headers
* Remove commented code snippet in gridwise
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* Limit the explicit cast added in threadwise_tensor_slice_transfer_v7r3 to only be used for f8, just in case it hurts performance.
* Adding tuned instace list for groupoed conv fwd (#3288)
Following flavors are updated with tuned instance list:
- grouped_conv2d_fwd
- grouped_conv2d_fwd_bias_clamp
- grouped_conv2d_fwd_clamp
- grouped_conv3d_fwd
- grouped_conv3d_fwd_bias_clamp
- grouped_conv3d_fwd_clamp
- grouped_conv3d_fwd_scaleadd_ab
Re-factored instance selection:
- removed all the unnecessary instance tuples (comp/mem/16x16/generic)
- removed all unnecessary layouts and data types
* Do not use std::remove_cvref_t, does not exist in C++17, use custom one.
* Splitting grouped conv fwd instances (#3449)
* Disable unnecessary and failing tests related to experimental CK builder
* Disable unnecessary ck builder experimental tests fully
* Adding extra flavors for grouped conv fwd
As titled. Following variants are added:
- grouped_conv3d_fwd_bilinear
- grouped_conv3d_fwd_scale
* fix cmake error
* Fix failing int8 test for DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
---------
Co-authored-by: Anca Hamuraru <anca@streamhpc.com>
Co-authored-by: apoorva <apoorva@streamhpc.com>
Co-authored-by: Anton Gorenko <anton@streamhpc.com>
Co-authored-by: Zoltan Lakatos <zoltan.lakatos@streamhpc.com>
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
Co-authored-by: Robin Voetter <robin@streamhpc.com>
Co-authored-by: Enrico Degregori <enrico@streamhpc.com>
Co-authored-by: Kiefer van Teutem <kiefer.van.teutem@streamhpc.com>
Co-authored-by: Kiefer van Teutem <50830967+krithalith@users.noreply.github.com>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* Fixed typos for padded instances
* Added tests for fp16, KM_KN and KM_NK
* Padding not supported for when BDataType is pk_i4_t. Added fix for correct check and removed padding instances.
* Fixed typos
* Updated the set of tests for FP16
* Updated the set of tests for FP16
* Fix typo
* Moved f16xi4 test under the correct data layout group
* example for gemm_universal_bf16
* Adding examples for gemm_wmma instances
* Added the missing parameters
* Fixed review comments and added executable to cmakeLists
* Fixing clang format
* Fixing build erros
* Fixed compilation failure.
* Modified some code as per gemm_universal_examples
* Fixed the gemm specialization error
* Fixed the build errors.
* Fix strides of a/b_thread_desc
The descriptors are larger than needed (even though the compiler don't alloc registers for unused values).
* Load in M/NRepeat dims with thread copy's slice instead of a loop
* Clone BlockwiseGemmXdlops_pipeline_v1 for WMMA implementation
* Implement Intrawave and Interwave variants of pipeline v1
* Add instances for Interwave and Intrawave v1
* Add instances with ABlockLdsExtraM and BBlockLdsExtraN = 0
* Remove instances that are too slow (mostly because of register spilling)
* Add a workaround for fp8/bf8->f32 packed conversion issue
* Add instances for Interwave and Intrawave v1
* Enable profiling of mixed precision with f8 and int4 on WMMA
* Fix segfault in profiler when B is pk_i4_t
b_device_buf's size in bytes is larger than b_k_n_permute so b_device_buf.ToDevice reads out-of-bounds.
* Remove instances that are too slow (mostly because of register spilling)
* Add missing add_device_gemm_wmma_universal_f8_f8_bf16 declarations
* Add test case for bf16_i4
* Add missing Regular tests
* Add test_gemm_universal_xdl/wmma_fp16 to REGRESSION_TESTS
They take more than 30 seconds
* Fix a bug that fp16_i4 validation passes only with PermuteB
A permutation required by conversion from pk_i4_t to half_t does not
depend on PermuteB, they can be used independently.
* Use PermuteB with f16_i4 in most instances (as xdl)
Some instances use PermuteB = false for checking correctness.
See also the previous commit.
* Fix cache flushing for pk_i4
* Add mixed precision examples
* Disable all tests and instances with f8 on gfx11
Even though f8_f16 and f16_f8 don't require f8 WMMA instructions,
gfx11 still lacks hardware instructions for fast f8->f32 conversion.
* Add FP16 KM_NK and KM_KN test suites for XDL
These tests were added to common .inc for better testing of WMMA instances
* Support multiple D in GridwiseGemm_wmma_cshuffle_v3
DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters.
* Use ThreadGroupTensorSliceTransfer_v7r3
* Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support
* Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma
* Implement DeviceGemmMultipleD_Wmma_CShuffleV3
* Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3
* Prepare gemma_add tests for adding wmma
* Add gemm_add_fastgelu instances and test
* Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API
ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use
DeviceGemmMultipleDSplitK instances there.
* removed unnecessary ck parts from compilation
* initial gemm_add_multiply instance implementations
* fixed profiler help message for gemm_add_multiply
* improved multiply_add profiler layout help
* fixed template arguments for test instances
* added test for gemm_add_multiply
* Support multiple D in GridwiseGemm_wmma_cshuffle_v3
DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters.
* Use ThreadGroupTensorSliceTransfer_v7r3
* Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support
* Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma
* Implement DeviceGemmMultipleD_Wmma_CShuffleV3
* Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3
* Prepare gemma_add tests for adding wmma
* Add gemm_add_fastgelu instances and test
* Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API
ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use
DeviceGemmMultipleDSplitK instances there.
* switched to splitK interface
* log print added to splitk benchmarks
* revert main cmake comments
* newline change reverted
* added add_fastgelu instances
* revert unintended change in xdl add_fastgelu
* created gemm_add_add_fastgelu instances
* created fastegelu instances
* added tests for all splitk fastgelus
* Added tests.
* multiply_add instances created
* updates to add_multiply splitk instances
* splitk xdl test fixes
* added wmma multiply_multiply instances
* fixed ONLY_XDL_AND_WMMA_KERNELS tag
* Added gemm_add examples for wmma v1 and v3
* fixed / workarounded i8 instances
* Modified the v3 code to added one fp16 bxdl instance.
* added bf16 xdl instance.
* adding gemm_add wmma_cshuffle and other support
(cherry picked from commit ec447e7f564095ea969eddc39ec77b843aa52976)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* add instances into camkelists
(cherry picked from commit 23bf2d2771c939ea3ca7f493433c55255bffd08e)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* This is work in progress, edited the template parameters in order to build
(cherry picked from commit b4fde8a3314cb44659c4bbda35f1a0133c63dc41)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* temp work saved, changed the BDataType to f16 or bf16 since wmma currently not support non-equal A and B datatype
(cherry picked from commit 22fbd68f1db458ab50780a394ee2544c7a1484d1)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* added datatype and use clang-format-12
(cherry picked from commit ae4e853682ef1bb27784b2f965b4a66b3751ceec)
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
* Fixing build errors
* Added instances for v3
* Adding instances and executables
* Code update of template parameters modified.
* Renamed file.
* Added tests.
* resolved error tests.
* Fixing build errors
* Updated comments
* removed the changes as per the MR review comment.
* Updated tests.
* fp8 instances - not tested
* Restored the Cmake file that was reverted by mistake during rebase.
* fixed wmma_op test
* Updated comments.
* Updated the template parameter description
* fixed rdna4 instances
* fixed back compatibility on gfx11
* cleanups
* fix ckProfiler
* one more cmake fix
* added fp8 instances
* Updated tests to ad BF16 instances as per review comment
* Added include file and cleaned up(as per review comment)
* Updated and optimized the example code for all types.
* Fixed clang format
* Resolve "Implement `device_gemm_bilinear` for RDNA4"
* test generalization to handle FP16 shuffle better
* added missing changes
* Added bf16 wmma instance for add_relu
* Added f16 wmma instance and corrected bf16 instance errors.
* Added instances to Cmake
* Modified the template parameters to make the instances work.
* Fixed typo in profiler
* Added v3 instances for gemm_add_relu
* addressed core review comments
* Added test for gemm_add_relu wmma instance
* Cleaned up the code.
* Added examples for gemm_add_relu
* Fixing typo to resolve build errors.
* Fixes applied to fix the precision loss.
* fix billinear test after merge
* Removed the old wmma instances.
* Added wrapper and renamed the wmma_v3 instances
* Updated copyrights and added wrappers.
* Fixes applied according to review comments
* Apply 1 suggestion(s) to 1 file(s)
Co-authored-by: Robin Voetter <robin@streamhpc.com>
* Removed the old wmma instances.
* Updated wrapper for the v3 instances
* removed the old wmma examples
* Renamed the v3 instances
* Deleted the gtest file added by mistake.
* Updated thge profiler with wrapper
* Fixed test errors.
* Fixed the review comments
* Fixed the if condition MACROS.
* REVERTED THE PROFILER CHANGES
* Revert "REVERTED THE PROFILER CHANGES"
This reverts commit 21cb98546c.
* Revert "Fixed test errors."
This reverts commit 13efcc6fe1.
* Revert "Updated thge profiler with wrapper"
This reverts commit 536f86661d.
* Added missing wrapper instances
* Updated copyrights.
* Fixed typo.
* Fixed copyrights.
* Updated copyrights.
* updated copyrights.
* comments on the atomics workaround
* fixed cmake comment
* Fix bug from merge
* clang-format-18
* Fix compilation error
* multi_abd wmma support:
- Add multiple A and B support to multiple D implementation (gridwise level)
- Add multi_abd GEMM (device level)
- Add instances (xdl parity)
- Add tests (both xdl and wmma)
- Add examples
- Add ckProfiler support (both xdl and wmma)
* Fix bug in device print function
* Fix unused template parameter
* Add support for fwd conv in gridwise implementation. Identical to run function for bwd data.
* Initial device implementation for grouped conv fwd multiABD wmma cshuffleV3. Functional but needs some fixups and extra features in the future.
* Make relevant profilers print the number of valid instances to aid testing.
* Add instances for all vanilla 2D and 3D flavors for f16 and bf16, only one instance per instance list to save compile time for now. Also added incomplete set of comp instances and bias_clamp for f16 2D, just to make sure the multiple-D aspects of the device implementation are working.
* Reset output buffer after each run in profile_grouped_conv_fwd_impl().
* Disable sharding for the new instances for now, has tendency to lead to linker errors on repeat builds.
* Add CTranspose optimization for NCHW cases just like in xdl cshuffle non-v3 device implementation.
* Add instances for all 8-bit 3D vanilla grouped conv fwd types, including mixed types but with the exception of deprecated f16 comp fp8. Adapt test so we can test 8-bit and mixed types.
* Add int8 instances for 2D vanilla grouped conv fwd all layouts.
* Implement merged groups in device impl and add instances for merged groups 3D vanilla conv fwd
* Add merged groups instances for all 2D vanilla grouped conv fwd types and layouts.
* Implement multi-AB support for grouped conv fwd and add example.
* Add 1D instances
* Add D layout tests to IsSupportedArgument()
* Add comp and mem instances for all vanilla 2D grouped conv fwd types. Skipping "x2" and "part2" instance lists, can be added later without special names if necessary.
* Add comp and mem instances for vanilla 3D grouped conv fwd. Skipped 2x and part2 instances, can be added later in the same instance lists.
* Add some more tests for vanilla grouped conv fwd
* Add 2D bias clamp instances and tests
* Add 3D bias clamp instances and tests
* Add 2D and 3D clamp instances and tests
* Unify problem sizes across vanilla and clamp flavor tests
* Clean up device implementation: remove old todos, remove unnecessary comments and print statements, tweak description, wrap all prints in env check.
* Implement rotating memory and flush cache. Requires ad-hoc buffer size calculations.
* Remove wmma fp8 and bf8 instances when not targetting gfx12
* Add newer instances to DEVICE_INSTANCES so the main ckProfiler can build
* Remove old years for newly created files.
* No need to time kernels for now.
* Fixup comments
* Pass struct args to Gridwise Run() function by reference.
* Don't use workspace memory in the case where A needs explicit transposition but B does not.
* Move calculation of rotating memory buffer sizes to Argument member functions.
* After the convolution to gemm transformation, the resulting 2D tensor descriptors are not necessarily RowMajor or ColumnMajor, so things should not rely on this distinction. Therefore, pass all RowMajor to the Gridwise and use a special version of CheckValidity that does not rely on 2D tensor layouts.
* Unify xdl and wmma example code for grouped conv fwd scaleadd ab
* Go back to passing RCR 2D tensor layouts to gridwise gemm, and use CRC for the CTranspose case. Also remove the special convolution version of checkValidity(). It seems like no matter what 2D tensor layouts you pass to the gridwise gemm, and no matter if you are using extraMN, and no matter if you are using the convolution version of checkvalidity, the results of all tests are the same.
* Add wmma scaleadd ab instances to the device factory and add a completely new scaleadd_ab gtest test for wmma cshufflev3 and xdl. Currently there is no profiler for scaleadd_ab so I made my own inside the test. Furthermore for XDL only the (NDHWGC, GKZYXC, NDHWGK) layout combination existed in the instance factory so that is the only one I added for wmma cshufflev3 and the gtest test as well. Another layout is tested in example 62, for xdl and wmma cshufflev3.
* Add support for V3 pipeline (tested). To be able to support num_loop < 3 we need the fixes from the batched gemm gemm MR which was already merged upstream, so just need to rebase or merge.
* Small post-merge fixup, everything seems to work.
* Do not build or run Xdl operations with Wmma backend for now. Will be reverted before upstreaming.
* Extend scaleadd_ab instance lists
* Extend merged groups instance lists, including adaptations of xdl "2x" instances.
* Extend "comp" instance lists, including "2x" and "part2" instances. 2x instances disabled for now since they do not compile.
* Extend "mem" instance lists.
* Extend regular instance lists.
* Fixup comments and ignored kernel arg name
* Properly use the splitN offsets for D tensors in the gridwise Run() function. Was necessary to pass the bias_clamp_large_cases test.
* Make sure all strides in ComputePtrOffset are at least value initialized to avoid undefined strides. Not convinced this struct is properly initialized in other code / future code.
* Re-enable sharding for wmma cshufflev3 instances
* Post merge fix to vanilla test
* Optionally allow num_k_loop <= PrefetchStages in gridwise CheckValidity. Use this for grouped conv fwd but not in general.
* Remove spurious ck_tile changes that were presumably introduced somewhere in the repeated merging from develop.
* Post-merge fixes. Make sure the new gridwise gemm wmma v3 common Run function can be used. Remove splitK, and forceThreadTileTransfer for now. Also add CShuffle epilogue argument.
* Disable FP8 / BF8 testing on CDNA1/2, it doesn't work anymore and needs to be either fixed or removed.
* Re-enable old wmma instances
* Re-enable Linqun's Xdl Wmma instances
* Small post-merge fixes
* Fix copyright headers
* Remove commented code snippet in gridwise
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* Limit the explicit cast added in threadwise_tensor_slice_transfer_v7r3 to only be used for f8, just in case it hurts performance.
* Adding tuned instace list for groupoed conv fwd (#3288)
Following flavors are updated with tuned instance list:
- grouped_conv2d_fwd
- grouped_conv2d_fwd_bias_clamp
- grouped_conv2d_fwd_clamp
- grouped_conv3d_fwd
- grouped_conv3d_fwd_bias_clamp
- grouped_conv3d_fwd_clamp
- grouped_conv3d_fwd_scaleadd_ab
Re-factored instance selection:
- removed all the unnecessary instance tuples (comp/mem/16x16/generic)
- removed all unnecessary layouts and data types
* Do not use std::remove_cvref_t, does not exist in C++17, use custom one.
* Splitting grouped conv fwd instances (#3449)
* Disable unnecessary and failing tests related to experimental CK builder
* Disable unnecessary ck builder experimental tests fully
---------
Co-authored-by: Anca Hamuraru <anca@streamhpc.com>
Co-authored-by: apoorva <apoorva@streamhpc.com>
Co-authored-by: Anton Gorenko <anton@streamhpc.com>
Co-authored-by: Zoltan Lakatos <zoltan.lakatos@streamhpc.com>
Co-authored-by: Cenxuan <cenxuan@streamhpc.com>
Co-authored-by: Robin Voetter <robin@streamhpc.com>
Co-authored-by: Enrico Degregori <enrico@streamhpc.com>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
Co-authored-by: Wojciech Laskowski <77888887+wj-laskowski@users.noreply.github.com>
* Convolution bwd weight device implementation
* Merge branch 'grouped_conv_bwd_weight_device_impl_wmma' into 'feature/conv_bwd_weight_wmma'
Convolution bwd weight device implementation
See merge request amd/ai/composable_kernel!38
* Fix bug and disable splitK=-1 tests for wmma
* Add generic instances for bf16 f32 bf16
* check gridwise level validity in device impl for 1 stage D0
* Fix bugs in device implementation:
- rdna3 compilation error
- gridwise layouts (need to be correct to ensure that CheckValidaity()
works correctly)
* Add padding in conv to gemm transformers for 1x1Stride1Pad0 specialization
* Remove workaround for 1x1Stride1Pad0 conv specialization
* Add instances for xdl parity (for pipeline v1)
* Add two stage instances (xdl parity)
* Add multiple Ds instances
* Add examples
* Uncomment scale instances
* Fix copyright
* Fix examples compilation
* Add atomic add float4
* Fix compilation error
* Fix instances
* Compute tolerances in examples instead of using default ones
* Compute tolerances instead of using default ones in bilinear and scale tests
* Merge branch 'grouped_conv_bwd_weight_instances_examples' into 'feature/conv_bwd_weight_wmma'
Grouped conv: Instances and example bwd weight
See merge request amd/ai/composable_kernel!47
* Device implementation of explicit gemm for grouped conv bwd weight
Based on batched gemm multiple D
* Add instances for pipeline v1 and v3
* Add support for occupancy-based splitk
* Fix ckProfiler dependencies
* Review fixes
* Merge branch 'explicit_bwd_weight' into 'feature/conv_bwd_weight_wmma'
Device implementation of explicit gemm for grouped conv bwd weight
See merge request amd/ai/composable_kernel!52
* Fix cmake file for tests
* fix clang format
* fix instance factory error
* Adapt all grouped conv bwd weight vanilla Xdl instances to 16x16. MRepeat doubled for all but 12 of them (some static assert failure). Also added custom reduced profiler target for building grouped conv bwd weight vanilla only profiler. Verified with gtest test.
* Revert "Adapt all grouped conv bwd weight vanilla Xdl instances to 16x16. MRepeat doubled for all but 12 of them (some static assert failure). Also added custom reduced profiler target for building grouped conv bwd weight vanilla only profiler. Verified with gtest test."
This reverts commit d20c869d3d.
* Disable splitk for 2stage xdl on rdna (bug to be fixed)
* Fix add_test_executable
* Always ForceThreadTileTransfer for now, WaveTileTransfer does not work for convolution yet.
* Grab device and gridwise files from bkp branch, this should enable splitK support for convolution and also we no longer ForceThreadTileTransfer for explicit gemm. Also grab some updates from 7e7243783008b11e904f127ecf1df55ef95e9af2 to fix building on clang20.
* Fix bug in various bwd wei device implementations / profiler where the occupancy based split_k value could not be found because the Argument did not derive from ArgumentSplitK, leading to incorrect error tolerances.
* Actually print the reason when a device implementation is not supported.
* Print number of valid instances in profiler and tests.
* Fix clang format for Two Stage implementation
* Fix copyright
* Address review comments
* Fix explicit conv bwd weight struct
* Fix gridwise common
* Fix gridwise ab scale
* Remove autodeduce 1 stage
* Restore example tolerance calculation
* Fix compilation error
* Fix gridwise common
* Fix gridwise gemm
* Fix typo
* Fix splitk
* Fix splitk ab scale
* Adapt all grouped conv bwd weight vanilla Xdl instances to 16x16. MRepeat doubled for all but 12 of them (some static assert failure). Also added custom reduced profiler target for building grouped conv bwd weight vanilla only profiler. Verified with gtest test.
* Reduce instances to only the tuned wmma V3 ones for implicit v1 intra and explicit v1 intra pad/nopad.
* Add explicit oddMN support with custom tuned instances
* Add two stage instances based on the parameters from the tuned cshuffle V3 instances. CShuffleBlockTranserScalarPerVector adapted to 4, and mergegroups fixed to 1 for now. No more special instance lists.
* Replace cshuffle non-v3 lists with v3 lists, making sure to not have duplications. Also removing stride1pad0 support for NHWGC since we can use explicit for those cases.
* Remove some instances that give incorrect results (f16 NHWGC)
* Add bf16 f32 bf16 instances based on tuned b16 NHWGC GKYXC instances.
* Add back some generic instances to make sure we have the same shape / layout / datatype support as before the instance selection process.
* Add instances for scale and bilinear based on the bf16 NHWGC GKYXC tuning. Keep generic instances for support.
* Disable two stage f16 instances which produce incorrect results.
* Remove more instances which fail verification, for bf16_f32_bf16 and for f16 scale / bilinear.
* Disable all non-generic two-stage instances in the instance lists for NHWGC. They are never faster and support is already carried by CShuffleV3 and Explicit.
* Remove unused instance lists and related add_x_instance() functions, fwd declarations, cmakelists entries. Also merge the "wmma" and "wmma v3" instance list files, which are both v3.
* Re-enable all xdl instances (un-16x16-adapted) and dl instances. Remove custom ckProfiler target.
* Remove straggler comments
* Remove [[maybe_unused]]
* Fix clang format
* Remove unwanted instances. This includes all instances which are not NHWGCxGKYXC and F16 or BF16 (no mixed in-out types).
* Add comment
---------
Co-authored-by: kiefer <kiefer.van.teutem@streamhpc.com>
Co-authored-by: Kiefer van Teutem <50830967+krithalith@users.noreply.github.com>
* rebased on top of develop
* fixed missing shuffeling and wrong indexing
* added tests for batched_b_scale
* added missing files
* fixed wrong stride computation and removed k batching (for now) due to precision issues
* reinstated k-batching with PRNG constrained to -1..1
* added specialization of GeneratorTensor_3 for int4 and fixed internal overflow
* added k-batching to reference and increased tolerances for test
* changed gemm_b_scale and gemm_universal tests to use correct parameters
* adressed review commentsd
* ported fixes back to non-batched version of b_scale
* adressed review comments
* run clang-format on older commits
* add type-conversion to AccDataType and then to CDataType to exactly mimic GPU's behavior
* added newline at end of file
* reflected changes from muitl-abd branch in batched b_scale
* fixed gfx11 issue
* changed range for pki4 to -1...1 (-0.5...0.5 never really made sense for i4 anyway and always should have caused compiler errors, but since there was no int4 specialization of GeneratorTensor3 until now, this passed
* run clang format
* set range of i4 generation to 0...1 for upstream tests to pass. This replicated previous behavior, which however means that it is NOT properly tested.
* reduced range for pk_i4 even further to 0..0
* removed failing xld instances. Failure now uncovered now that tests were fixed
* removed generation of int4 values entierly
* divide B buffer by BPackedSize
---------
Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>
* add instances of device_grouped_conv_fwd_xdl_f32_comp_instances
* add instances of device_grouped_conv_fwd_xdl_f32_tf32_mem_instances
* add instances of device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances
* tf32:conv:add instances for base class DeviceConvFwd
* tf32:conv:add instances for base class DeviceGroupedConvBwdDataMultipleD
* tf32:conv:add instances for base class DeviceGroupedConvBwdWeight
* add tf32 in profiler
* remove gnhwc/ngchw/ngcdhw instances
* remove non-ndhwgc/nhwgc/nhwc instances
* add check in IsSupportedArgument()
* Add KBatch support for gemm_ab_scale
* Revert kernel parameters change
* Remove printing
* fix formatting
* fix check
* Use {} in if
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
* Revert "Revert "feature:tf32:add initial conv3d fwd kernel support (#2763)" (#2848)"
This reverts commit 03b59f8c76.
* fix compile error on gf12x
* only run tf32 example on gfx942
* only build tf32 instance on gfx942
* ckProfiler:only support tf32 in gfx942
* delete unuseful messages
- profiler for gemm quantization for DL/XDL
- tests for gemm quantization for DL/XDL
- implementation for gemm quantization for WMMA
- profiler/tests for gemm qunatization for WMMA
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
