[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_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.
* Split-K autodeduction for DeviceGroupedConvBwdWeight_Xdl_CShuffle and DeviceGroupedConvBwdWeight_Xdl_CShuffleV3.
* Split-K autodeduction for DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle.
* Use simple best occupancy model to calculate the split-K.
* Handle split-K autodeduction in explicit gemm conv.
* Add unit tests for split-K autodeduction.
* Remove oversubscription.
* Small fixes.
* Added split-K autodeduction for DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle.
* Run clang formatting.
* Fix error handling in the conv profiler.
* Add missing documentation for the autodeducted split-K values.
* Add split-K autodeduction to DeviceGroupedConvBwdWeight_Explicit_Xdl solver.
* Fix clang formatting and split-K profiler documentation.
* Rename max_occupancy value variable.
* Calculate grid size for split-K autodeduction directly from input array shapes and template params.
---------
Co-authored-by: Ville Pietilä <>
* Extend support for contraction up to 5D
* Extend contraction bilinear instances
* Fix interface test
* Add 6d support, remove 3d,4d,5d
* Fixes
* Fix readme
* Make defualt dim for contraction instances
* Add support for mixed precision in contraction scale and bilinear (#936)
* Extract common functionality to separate files
* Reference contraction: Remove incorrect consts from type_converts
* Reference contraction: Add missing type_convert for dst value
* Reference contraction: Fix incorrect order of B matrix dimensions
* Add support for mixed precision in contraction scale and bilinear
* Move using statements from instances to a common file
* Move using statements from examples to a common file
* Fix the order of B matrix dimensions across examples and profiler
* Fix the computation of error threshold
* Make ComputeDataType an optional argument
* Include possible DataType -> ComputeDataType casting error in the threshold
* Remove commented code
* Make the ComputeDataType an optional argument in instance
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
* Extract common functionality to separate files
* Reference contraction: Remove incorrect consts from type_converts
* Reference contraction: Add missing type_convert for dst value
* Reference contraction: Fix incorrect order of B matrix dimensions
* Add support for mixed precision in contraction scale and bilinear
* Move using statements from instances to a common file
* Move using statements from examples to a common file
* Fix the order of B matrix dimensions across examples and profiler
* Fix the computation of error threshold
* Make ComputeDataType an optional argument
* Include possible DataType -> ComputeDataType casting error in the threshold
* Remove commented code
* Add column to image kernel
* Minor fixes for dtypes and client examples
* Disable tests for disabled dtypes
* Disable add instances functions for disabled data types
* Minor stylistic fixes
* Revert "Disable add instances functions for disabled data types"
This reverts commit 728b869563.
* Instances reduction
* Add comments in device_column_to_image_impl
* Update changelog and Copyrights
* Improve changelog
* Add image to column kernel
* Add instances, tests, profiler, example
* Add client example
* Several fixes of image to column
* Fix variable name in device_image_to_column_impl
* Several fixes of image to column profiler
* Fix num_btype calculation
* Make new mesaurements for correct bytes calculation
* Add contraction profiler and tests
* Build and style fixes
* Allow to use any elementwise operator for ref_contraction
* Introduce profile_contraction_scale and profile_contraction_bilinear
* Make ref_contraction generic and extend interface tests
* Stylistic minor fixes
* Extend test_contraction_interface
