[CK_TILE] Add support and tests for V6 pipeline in conv fwd
(#4357)
Added support for conv v6 pipeline in ck tile's convolution forward
kernel. CK Tile v6 pipeline is the equivalent to old ck's V5 pipeline
and should be faster than other pipelines for some cases. This PR also
adds tests inside profiler that's currently inside experimental
directory, so now we should be able to detect regressions easier.
* initial poc
* factor out common parts in operator()
* cv4
* rest of the universal gemm pipelines
* fix test
* remove boilerplate from tile engine
* fix example
* fix example
* format
* fix tests build for gemm
* remove base pipeline codegen from gemm instance builder
* unify v3 logic with the rest of universal gemm pipelines
* fix build for multi abd test
* fix test gemm multi d
* fix build for weight preshuffle
* fix grouped gemm test
* fix grouped gemm multi d test
* fix grouped gemm preshuffle
* fix grouped gemm example except for quant
* fix gemm preshuffle
* fix splitk 2 stage example
* fix batched gemm example
* fix multid example
* fix multiabd example
* fix batched gemm test
* fixup
* fix examples build
* fix grouped gemm test build
* fix smoke builder
* hacky poc
* fix tile engine
* kill the lambda
* maybe fix test build
* more fixes
* clang-format
* save temp
* clang-format
* mostly fix examples
* clang-format
* remove dead code
* more cleanup
* fix fmha bwd build (default epilogue set/add appears to be broken)
* fix default epilogue tests but not correctness
* clang-format
* fix bquant
* clang-format
* cleanup dead code
* rearrange make windows for readability
* restore changes to IsSupportedArgument
* fix smoke-builder
* clang-format
* fixup rename class
* build fixes
* clang-format
* fix builder
* fixup
* remove set from builder tests
* fix test
* clang-format
* re-refactor the kernels
* clang-format
* fix header license
* remove memory operation from conv bwd test
* clang-format
* clang-format example,include
* clang-format test
* build fixes
* clang-format
* solve compilation error
* fix the CI
* solve compilation error
* clang format
* solve merge conflict
* solve merge conflict
* solve the gfx11 error
* solve test error
* moar build fixes
* remove AtomicAddRequiresKBatchGreaterThanOne test since the property is removed from the kernel scope
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* First version of split-K autodeduction.
* Fix circular dependency and kernel construction.
* Fix tolerance calculation for bwd weight example.
* Simplify kernel construction.
* Fix kernel launching bug for split-K autodeduce.
* Add split-K autodeduction support for the two stage example.
* Fix a corner case.
* Fix clang-format.
* Fix clang-format for inc files.
* Add missing header.
* Prevent too large split-K values.
* Fix formatting.
* Add unit tests for IsSupportedArgument in grouped bwd conv.
* clang-format.
* Fix merge conflicts.
* Address feedback from code review.
* clang-format
* Fix new tests after merge.
---------
Co-authored-by: Ville Pietilä <>
* initial poc
* factor out common parts in operator()
* cv4
* rest of the universal gemm pipelines
* fix test
* remove boilerplate from tile engine
* fix example
* fix example
* format
* fix tests build for gemm
* remove base pipeline codegen from gemm instance builder
* unify v3 logic with the rest of universal gemm pipelines
* fix build for multi abd test
* fix test gemm multi d
* fix build for weight preshuffle
* fix grouped gemm test
* fix grouped gemm multi d test
* fix grouped gemm preshuffle
* fix grouped gemm example except for quant
* fix gemm preshuffle
* fix splitk 2 stage example
* fix batched gemm example
* fix multid example
* fix multiabd example
* fix batched gemm test
* fixup
* fix examples build
* fix grouped gemm test build
* fix smoke builder
* LWPCK-4043: Add GPU reference implementations for CK Tile convolution
This commit implements GPU-based reference kernels for CK Tile convolution
operations to enable faster verification of optimized kernels, especially
for large tensors (>2GB).
Changes:
- Add naive_grouped_conv_fwd.hpp: GPU reference for forward convolution
- Add naive_grouped_conv_bwd_data.hpp: GPU reference for backward data
- Add naive_grouped_conv_bwd_weight.hpp: GPU reference for backward weight
- Integrate GPU references with test infrastructure (replace -v=2 error)
- Support for 1D, 2D, and 3D convolutions
- Generic data type support (FP16, BF16, FP32)
- Grid-stride loop pattern for scalability
The GPU references use a simple, readable implementation that prioritizes
correctness over performance. They accumulate in float32 and handle
padding, stride, and dilation correctly.
* update gpu reference for ck tile grouped conv
* correct c++ 18 format
* Add GPU Reference Implementations for Old CK Convolution
This commit implements GPU-based reference kernels for Old CK convolution
operations to enable faster verification of optimized kernels.
Changes:
- Fixed old CK forward GPU reference (naive_conv_fwd.hpp)
* Fixed BF16 NaN issue (use type_convert instead of static_cast)
* Fixed FP8/BF8 arithmetic (accumulate in float)
* Fixed uninitialized variables
* All 9 data types now working (FP16/32/64, BF16, INT8, FP8, BF8, mixed)
- Created backward data GPU reference (naive_conv_bwd_data.hpp)
* Implements input gradient computation
* Verified equal to CPU reference
* Handles 1D, 2D, 3D convolutions
- Created backward weight GPU reference (naive_conv_bwd_weight.hpp)
* Implements weight gradient computation
* Verified equal to CPU reference
* Handles 1D, 2D, 3D convolutions
- Integrated with old CK examples
* Forward: 10 XDL examples now support do_verification=2
* Backward data: Integrated with example/17_convnd_bwd_data/
* Backward weight: Integrated with example/20_grouped_conv_bwd_weight/ (G=1 only)
* Updated parameter from boolean to int (0=no, 1=CPU, 2=GPU)
Testing:
- 50 comprehensive tests created
- 42/42 tests passing (100% success rate)
- CPU and GPU verification produce identical results
- Verified across multiple dimensions, sizes, and data types
Limitations:
- GPU references support standard convolution only (G=1)
- Fused operations (DL variants) not supported
- Some tests blocked by optimized kernel size constraints
Result: Old CK GPU references can replace CPU references for verification
with 50-100x performance improvement for large tensors.
* Apply clang-format to old CK GPU reference files
* Fix C++17 compatibility: use brace initialization for aggregate types
* add get_rtol, get_atl and consistency cout message
* Use triple bracket syntax for kernel launch per review feedback
Changed hipLaunchKernelGGL to <<<...>>> syntax as suggested by @aosewski.
This is more idiomatic HIP/CUDA style and equally correct.
All tests still passing after this change.
* Address review feedback: Use HIP_CHECK_ERROR and add v=3 mode
- Replace manual error checking with HIP_CHECK_ERROR macro
- Add v=3 verification mode (GPU ref vs CPU ref direct comparison)
- Consistent output format across all examples
- All tests passing (7/7 v=3 tests pass for FP16)
* Use ConvDims structure to simplify GPU reference kernels
Replace 24 individual parameters with ConvDims structure per review feedback.
- Add conv_common.hpp with ConvDims and helper function
- Update kernel signatures: 24 params → 1 structure
- Remove duplicate extraction code from host files
* Use get_block_id() and get_thread_id() helpers in CK Tile
Replace manual blockIdx.x/threadIdx.x arithmetic with helper functions.
Updated 3 CK Tile GPU reference kernels per review feedback.
* Use std::array for spatial parameters in CK Tile GPU references
Replace raw pointers with std::array for type safety per review feedback.
- Add conv_common.hpp with vector-to-array helper functions
- Update kernel signatures: pointers → std::array references
- Remove DeviceMem allocations for spatial parameters
* Use NDimSpatial+3 for stride array sizes
Replace hardcoded [10] with [NDimSpatial+3] per review feedback.
Array sizes now correctly reflect actual dimensions needed.
* Use #pragma once instead of include guards
Replace traditional include guards with #pragma once per review feedback.
Updated 3 Old CK GPU reference headers.
* Fix element-wise operation output in Old CK GPU references
Write transformed value (out_val/in_val/wei_val) instead of untransformed
result per Copilot feedback.
This ensures element-wise operations are correctly applied to output.
* Initialize element-wise operation variables
Initialize in_val, wei_val, out_val to avoid undefined behavior
per Copilot feedback.
Updated backward data and backward weight kernels.
* Use explicit zero initialization for element-wise variables
Change TIn{} to TIn{0} for consistency per Copilot feedback.
All 3 kernels now use consistent zero initialization.
* Fix copyright headers to match existing style
- Old CK: Use standard format without year
- CK Tile: Add 2018- prefix to year range
Addresses consistency feedback.
* Rename GPU reference files: add _gpu suffix
* Refactor index calculations: use std::array and extract to helper functions
* Remove v=3 option: redundant as v=1 and v=2 comparison validates equivalence
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
* Merge fwd conv groups in CK Tile.
* Fix building CK fwd convs.
* Add number of merged groups to conv fwd kernel name.
* Get number of merged groups from conv config.
* Rename GemmConfig to ConvConfig.
* Clean-up TODOs.
* Check that number of conv groups must be divisible by the number of merged groups.
* Improve error handling in the conv fwd example.
* Fix clang-format.
* Fix group offsets.
* Fix merge problem.
* Address feedback from code review.
* Fix clang-formatting.
This renames the typeToStr struct in the common utilities to DataTypeTraits and removes all duplication of DataTypeTraits across files in CK Tile.
Co-authored-by: Christopher Millette <63608002+cgmillette@users.noreply.github.com>
* remove EXCLUDE_FROM_ALL from ck-tile examples
-> +15 min build time w/ 64 threads for a single arch
* fix cpp17 compile error in the ck-tile examples
---------
Co-authored-by: khuagarw <khuagarw@amd.com>
Co-authored-by: Ding, Yi <yi.ding@amd.com>
This change replaces pipeline macros like CK_TILE_PIPELINE_COMPUTE_V3,
CK_TILE_PIPELINE_MEMORY, etc in the CK Tile examples with a common enum
called GemmPipeline to reduce code duplication.
* Refactor split-image implementation: simplify code and remove redundant variables
* Add padding debug output to split-image implementation
- Added debug prints for padding calculations in transform_conv_fwd_to_gemm.hpp
- Verified padding works correctly with all tests passing
* Fix sign comparison warning after rebase with origin/develop
- Cast blockIdX from unsigned to signed index_t for comparisons
- Integrated with new GetOutputTileIndex logic from upstream
- Updated to use amd_wave_read_first_lane instead of __builtin_amdgcn_readfirstlane
* Fix Split-N with groups bug and clean up unused parameters
- Fixed batch stride calculation to include G dimension for grouped convolutions
- When moving between batches in NHWGC/NWGC/NDHWGC layouts, need to account for all groups
- Removed unused multi-split parameters (we only support 2-way split)
- All tests now pass: G=1 with Split-N, G>1 with Split-N, G>1 without Split-N
* Implement recursive queue-based split-image detection and calculation
- Add LaunchKernelWithSplitIfNeeded() helper method in transform_conv_fwd_to_gemm.hpp
- Implement recursive binary splitting algorithm (10GB→5GB+5GB→...)
- Correctly handle odd dimensions (61→30+31)
- Calculate proper offsets for each split piece
- Update invoker to use split-image helper
Note: Split detection and calculation work correctly but kernel launching
for individual pieces requires kernel modification to handle different
spatial dimensions (unlike Split-N which uses blockIdx.z).
* WIP: Split-Image investigation - found architecture mismatch
- Split-N modifies N_ directly in transformer constructor
- Split-Image needs different approach due to varying dimensions
- Added split calculation logic for 1D and 2D convolutions
- Still facing memory issues when creating piece transformers
Key finding: Split-N uses blockIdx.z for parallel execution,
while Split-Image needs sequential execution of non-uniform pieces.
* Add 1D split-image implementation for grouped convolution (N=1 working)
Implements split-image for 1D convolution to handle large tensors that
exceed memory thresholds. This is a critical milestone with N=1 fully
working and tested.
Key Changes:
- Invoker: Add split-image logic that splits W dimension in half
- Transformer: Add SplitConvProblem helper for recursive splitting
- Calculate offsets for LEFT and RIGHT pieces
- Launch two kernels sequentially (LEFT then RIGHT)
Implementation Details:
- Binary split: divides W dimension by 2
- LEFT piece: W=0 to W/2, keeps left padding, removes right padding
- RIGHT piece: W/2 to W, removes left padding, keeps right padding
- Offset calculation accounts for stride, dilation, and padding
- Physical memory offset (no padding in memory)
Test Results (N=1):
✅ 94/94 tests passing
- Comprehensive tests: 36/36 (channels, padding, stride, dilation, filters, groups)
- Edge case tests: 31/31 (odd dimensions, extreme parameters, boundaries)
- Stress tests: 27/27 (maximum dimensions, up to 91.4 TFlops)
Known Limitations:
- Only works with N=1 (single batch)
- N>1 fails when split-image triggers (offset calculation issue with Split-N)
- Root cause: Split-N modifies N in transformer, but offset calculated in invoker
- Solution planned: Move offset calculation to transformer (next phase)
Files Modified:
- grouped_convolution_forward_invoker.hpp: Add split-image logic
- transform_conv_fwd_to_gemm.hpp: Add SplitConvProblem helper
This commit represents a stable, tested 1D split-image implementation
for N=1 cases. It's an important milestone before extending to N>1
and multi-dimensional splits.
* Add basic split-image implementation for 1D/2D/3D grouped convolution
This is a working baseline implementation that splits large spatial
dimensions to handle memory constraints.
Implementation:
- 1D: W-split for NWGC layout (36/36 tests passing)
- 2D: H-split for NHWGC layout (20/20 tests passing)
- 3D: D-split for NDHWGC layout (verified working)
Features:
- Binary split of outermost spatial dimension
- Sequential LEFT/RIGHT kernel launches
- Proper padding adjustment at split boundaries
- Offset calculation for pointer arithmetic
- Debug output for verification
Threshold: 100KB (configurable in transformer)
Known limitations:
- No safety checks for edge cases (to be added)
- Offset calculated before Split-N (incompatible with N>1, to be fixed)
- No recursive splitting for very large tensors
Next steps:
- Add safety checks (is_possible_to_split_*)
- Move offset calculation to transformer (after Split-N)
- Test with N>1 + split-image combination
* Refactor split-image to unified structure for 1D/2D/3D
Unified the three separate dimension-specific blocks into a single
common implementation with dimension-specific stride calculations.
Benefits:
- Reduced code from 636 → 348 lines (45% reduction)
- Eliminated code duplication
- Easier to maintain and extend
- Single source of truth for split logic
Implementation:
- Common: Binary split, offset calc, padding adjustment, kernel launch
- Dimension-specific: Stride calculation only
- 1D: stride = G * C
- 2D: stride = W_in * G * C
- 3D: stride = H_in * W_in * G * C
Test results (all passing):
- 1D: 36/36 tests ✅
- 2D: 20/20 tests ✅
- 3D: 28/28 tests ✅
- Total: 84/84 (100%)
All test scenarios verified:
- Varying channels, padding, stride, dilation
- Filter sizes (1x1 pointwise to 7x7)
- Multiple groups (G=1,2,4)
- Odd dimensions
- Complex combinations
* Add safety checks for split-image in all dimensions
Added is_possible_to_split safety checks to prevent crashes when
splitting is not feasible.
Safety checks verify:
1. Output dimension > 1 (can't split single element)
2. RIGHT piece starts after left padding
3. LEFT piece ends within input bounds
If checks fail, falls back to normal kernel launch.
Verified for all dimensions:
- 1D (W-split): Wo=1 case triggers fallback
- 2D (H-split): Ho=1 case triggers fallback
- 3D (D-split): Do=1 case triggers fallback
Original 84 tests still pass - they use normal configurations
that naturally satisfy safety conditions.
Safety checks protect against pathological edge cases with:
- Very small spatial dimensions
- Extreme stride/dilation combinations
- Invalid padding configurations
* Fix Split-N + Split-Image compatibility issue
Fixed critical bug where Split-N and Split-Image working together
caused ~50% incorrect results due to wrong batch stride calculation.
Problem:
- Batch stride was calculated using MODIFIED spatial dimensions
(e.g., W=50000 after split) instead of ORIGINAL dimensions (W=100000)
- Spatial offset was applied globally in invoker, not per-batch in kernel
- Each batch (blockIdx.z) got wrong memory offset
Solution:
1. Store spatial offset in kargs (don't apply to pointer in invoker)
2. Copy correct batch_stride from temp_kargs to left/right kargs
3. Apply formula in operator(): ptr = base + (batch × stride) + spatial_offset
Changes:
- grouped_convolution_forward_kernel.hpp:
* Added spatial_offset_in/out fields to KernelArgs
* Apply batch + spatial offset in operator()
- grouped_convolution_forward_invoker.hpp:
* Keep base pointer, store spatial offset in kargs
* Copy batch_stride from temp_kargs (has original dimensions)
- transform_conv_fwd_to_gemm.hpp:
* Add debug output for split-image calculation
Results:
- N=1 tests: 84/84 passing (100%)
- N>1 tests: Now all passing (previously ~50% errors)
- Tested: 1D, 2D, 3D with N=1,2,4,8,16,20
* Implement unified threshold for Split-N and Split-Image
This commit consolidates threshold management for both Split-N and
Split-Image operations into a single source of truth, eliminating
code duplication and fixing offset calculation issues.
Key Changes:
============
1. Transformer (transform_conv_fwd_to_gemm.hpp):
- Moved TwoGB constant to public section for unified access
- CalculateSplitImage() now takes no parameters
- Uses internal threshold: TwoGB / sizeof(CDataType)
- Calculates offsets using N_ (after Split-N) for correctness
2. Kernel (grouped_convolution_forward_kernel.hpp):
- GetSplitImageInfo() simplified to take no parameters
- Forwards to transformer's CalculateSplitImage()
- Clean interface with unified threshold internally
3. Invoker (grouped_convolution_forward_invoker.hpp):
- Removed redundant threshold calculation
- Simplified to call kargs.GetSplitImageInfo() with no params
- Clean early-return pattern (no unnecessary else blocks)
- Removed duplicate/dead code paths
Benefits:
=========
- Single source of truth: TwoGB defined once in transformer
- No parameter passing for threshold between components
- Correct offset calculation using N_ (post-Split-N)
- Cleaner code with no duplication
- All tests passing: 1D/2D/3D with various N values
Testing:
========
- Split-Image only (N=1, large spatial): PASS
- Split-N only (N>1, small spatial): PASS
- Both splits active (N>1, large spatial): PASS
- No splits (N=1, small spatial): PASS
- CPU verification correct for all scenarios
* Comment out outdated split-image code (SplitConvProblem/LaunchKernelWithSplitIfNeeded)
The old recursive queue-based implementation has been replaced by the
new CalculateSplitImage() method which is simpler and correctly handles
Split-N + Split-Image interaction.
Changes:
- Wrapped lines 381-1078 in #if 0...#endif
- Old methods: SplitConvProblem() and LaunchKernelWithSplitIfNeeded()
- Preserved for reference but disabled from compilation
- No functional changes - all tests still pass
The new implementation (CalculateSplitImage at line ~2163) provides:
- Correct offset calculation using N_ (after Split-N)
- Simpler binary split logic
- Better integration with unified threshold approach
* Implement recursive split-image with depth limit (MAX_DEPTH=10)
Changes:
- Add depth tracking to SplitPiece struct
- Implement two stopping conditions:
1. Piece size below threshold (optimal case)
2. Depth >= MAX_DEPTH (prevents infinite recursion)
- Remove MAX_PIECES limit in favor of depth-based control
- Support up to 2^10 = 1024 pieces with depth 10
This allows handling extreme tensor sizes while ensuring termination.
Pieces larger than threshold will still launch correctly if depth limit reached.
Tested with H=100 (4 levels), H=2000 (6 levels), H=4000 (9 levels) - all pass CPU verification.
* Summary of recursive split-image implementation:
- Recursive queue-based splitting with depth limit (MAX_DEPTH=10, up to 1024 pieces)
- Two stopping conditions: size below threshold OR max depth reached
- Cumulative offset tracking through all recursion levels
- LEFT piece inherits parent offset, RIGHT accumulates (parent + local)
- Per-batch spatial offset application in kernel operator()
- Batch stride uses original dimensions (before split)
- Works with Split-N: split-N first, then recursive split-image
- Handles odd dimensions, padding, stride, dilation correctly
- All 1D/2D/3D tests pass with CPU verification
* Add comment explaining MAX_DEPTH capacity for 2GB threshold
* Refactor: move recursive split-image logic to transformer
- Move LaunchWithRecursiveSplit() from invoker to transform_conv_fwd_to_gemm.hpp
- Simplify invoker from ~250 lines to ~140 lines (removed 110 lines of inline logic)
- Encapsulate SplitPiece struct and BFS splitting algorithm in transformer
- Remove unused includes (queue, vector) from invoker
- Add documentation comment for AreDescriptorsSmallerThan2GB()
- Improve code organization and reusability
- No performance overhead (static template function, compiler inlines)
- All tests passing with 2GB production threshold
* Apply clang-format-18 formatting
- Format invoker and transformer files with clang-format-18
- Fix brace placement and alignment
- No functional changes
* Fix clang-format-18 issues in forward kernel
- Remove extra blank lines
- Fix line wrapping for template calls
- Consolidate GetSplitImageInfo() to single line
* Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Split-Image implementation with temporary fixed divider
- Implemented spatial dimension splitting (Split-Image) for large tensors
- Added piece-based coordinate transformation for 1D/2D/3D convolutions
- Integrated Split-N (batch splitting) with automatic threshold detection
- Fixed M dimension calculation to include batch: M = N × spatial_size
- Added spatial offset support in kernel arguments
- Verified 20/20 test cases passing for Split-Image alone
- Known issue: Split-N + Split-Image combination needs coordinate fix
Implementation Details:
- Split factors: 4 (1D), 4×4 (2D), 4×4×4 (3D) - temporary fixed values
- Batch strides properly calculated for NWGC/NHWGC/NDHWGC layouts
- Piece descriptors track spatial boundaries and block ranges
- No performance overhead for N=1 cases
* Fix 1D split-image padding issue with per-piece dimensions
- Store actual size per piece to handle non-uniform splits
- Remove dead code from transform utils
* Fix 2D/3D split-image with independent split factors per dimension
Problem: Single split factor caused non-uniform pieces when dimensions
didn't divide evenly. Result: 18/25 (72%) 2D padding combinations failed.
Solution: Independent split factor selection for W, H, D dimensions.
Each dimension gets optimal factor based on its own size.
Test Results:
- 1D: 42/42 pass (100%)
- 2D: 25/25 pass (100%)
- Total: 67/67 combinations verified
* Remove unused split-image struct fields
Cleanup of split-image implementation:
- Removed unused piece_d, piece_h, piece_w fields from SplitImageInfo struct
- These fields were declared but never used in the kernel
- Per-piece dimensions are already stored in pieces[] array
- Reduces struct size and improves code clarity
Tested: 1D/2D/3D convolutions with split-image, padding, stride all pass
* Refactor split-image invoker code for improved readability
- Extract piece calculation logic into calculate_piece lambda helper
- Extract kernel args population into populate_split_image_kargs lambda
- Use aggregate initialization for cleaner struct population
- Reduce nesting depth and improve maintainability
- Fix outdated comment about split-image implementation status
* Refactor split-image code and remove debug prints
- Extract GPU kernel helper lambdas for better readability
- Remove all split-image debug print statements
- Set memory threshold to 2GB for production
- All tests pass with CPU verification
* Add split-image safety constraints and refactor to utils
- Add MAX_TOTAL_PIECES=64 limit to prevent segfault
- Move calculate_spatial_piece to library utils
- Add layout validation (NWGC, NHWGC, NDHWGC only)
- Fix hierarchical splitting to respect piece limits
- Add proper documentation and formatting
* Change split-image from runtime to compile-time branching
Response to @bartekxk review comment:
Convert 'if(kargs.num_spatial_pieces > 1)' to 'if constexpr(EnableSplitImage)'
Changes:
- Add EnableSplitImage template parameter to kernel
- Change runtime if to compile-time if constexpr
- Update invoker to instantiate kernel variants with true/false
Benefits:
- Eliminates runtime branching in GPU kernel
- Dead code elimination (each variant is smaller)
- Better compiler optimization
Files modified: 2
Lines changed: 20 total (6 in kernel, 14 in invoker)
Tests: 27/27 passed (100%)
Performance: No regression
* Add split-image example as separate binary
- Create grouped_convolution_forward_split_image example
- Add grouped_convolution_forward_split_image_invoker.hpp
- Update CMakeLists.txt to build split_image binary
* Replace linear search with binary search in find_piece_id
- Change O(n) to O(log n) for finding piece ownership
- Matches reference implementation in large_tensor_cshuffle
* Simplify split-image code and fix integer overflow
- Extract lambda functions to static helper methods
- Pre-calculate constants in invoker
- Fix integer overflow in tensor size calculation for large tensors
* Trigger CI rerun - fix merge conflicts
* Fix merge conflict markers
* Fix clang-format: remove space before {}
* Fix clang-format: comment wrapping and Swish constructor
* Rename split_image to large_tensor for clarity
- Renamed grouped_convolution_forward_split_image.cpp -> grouped_convolution_forward_large_tensor.cpp
- Renamed grouped_convolution_forward_split_image_invoker.hpp -> grouped_convolution_forward_large_tensor_invoker.hpp
- Updated CMakeLists.txt target name: tile_example_grouped_conv_fwd_split_image -> tile_example_grouped_conv_fwd_large_tensor
- Updated comments to refer to 'large tensor' instead of 'split-image'
* Update comments and include in large_tensor example
- Updated header comments to use 'large tensor' terminology
- Fixed include path to use large_tensor_invoker.hpp
* Remove test code, restore 2GB threshold
* Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix build errors after develop merge and complete rename to large_tensor
This commit addresses compilation errors from the develop merge and
completes the rename from split_image to large_tensor.
Changes:
1. Fix CDEElementWise typo in grouped_convolution_forward_invoker.hpp
2. Fix template parameter order in large_tensor_invoker.hpp
- TransformConvFwdToGemm signature changed in develop
- NumGroupsToMerge and SplitN parameters swapped positions
3. Fix missing template parameter in GroupedConvFwdHostArgs
4. Fix EpiloguePipeline scope in kernel (merge conflict)
5. Update binary name references in test scripts
* Restore 2GB threshold for split-image
Changed threshold from 100MB (testing) back to 2GB for production use.
* Fix const-correctness in ds_ptr cast
* Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Apply clang-format-18
* update c++ 18 format
* Apply clang-format-18 to transform_conv_fwd_to_gemm.hpp
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix compilation of the grouped conv examples.
* Fix grouped conv bwd weight example output in CK Tile.
* Add number of groups to merge to ck tile grouped gemm example.
* Initial set of tests for TransformConvBwdWeightToGemm.
* Added unit tests for TransformConvBwdWeightToGemm conv groups are merged.
* WIP: Tensor transformations.
* Add unit tests for coordinate transforms.
* Fully working conv group merging for TransformConvBwdWeightToGemm.
* WIP: Merged conv groups offset calculation.
* Adde unit tests for tensor view.
* WIP: Merged conv groups epilogue.
* Enable running multiple conv groups per batch.
* Add tests for tile_distribution_encoding.
* Change example to match optimally depthwise convolution with merged groups.
* Add more tests for tensor view.
* Integration test for reading diagonal blocks from grouped distributed tensor.
* Improved integration test.
* Improve test for accessing diagonal blocks.
* Added integration test for cshuffle epilogue LDS tile distribution.
* Add more logging.
* Increase the max number of reported errors.
* WIP: merged conv groups GEMM epilogue changes.
* LDS to global memory copy.
* Fix tile window size for c block.
* Integration test for CShuffle epilogue.
* Improved CShuffle test.
* WIP: Separate epilogue for merged conv groups.
* Tile example parameters changes to match depthwise conv.
* Offset fixes.
* Epilogue fixes.
* Working baseline for depthwise covolution with merged conv groups.
* Fix build.
* Initial unit tests for tensor descriptor.
* Add one more unit test for tensor view.
* WIP: LDS to global mem transfer using CK tile tensor descriptor and tile distribution encoding.
* Fully functional LDS to global mem transfer using tensor descriptor and tile distribution encoding.
* Add more comments, disable debug code.
* Remove debug and other dead code.
* Code clean-up for bwd tensor transformations.
* Enable running multiple GEMM batches of merged conv groups.
* Add compile check for assumed row-mjor layout.
* Fix strides in 1D conv to gemm transformation.
* WIP: Simplify conv to gemm transformations and handle K > 1 and C > 1 cases.
* Fix case k > 1 and c=1.
* Remove debug code.
* Make MPerGroup and NPerGroup template parameters.
* Add additional check for non-supported c > 1 case.
* WIP: Put back the generic tensor descriptors for convolutions.
* Fix tensor descriptors.
* Remove the obsolete template parameters.
* Add more instances.
* Fix bugs in merged conv groups tensor descriptors.
* Fix tensor descriptors for merged conv groups when K > 1.
* Remove debug output.
* Remove dead code.
* Fix merge conflicts.
* Code clean-up.
* Remove unused code.
* Run clang-formatting.
* Remove debug prints and obsolete tests.
* Check that number of convolution groups is multiple of merged groups.
* Fix build after removing obsolete functionality.
* Remove obsolete enumeration.
* Fix new unit projects.
* Remove unnecessary includes.
* Fix passing the number of merged groups.
* Remove unrelated tests.
* Fix IsSupportedArgument for bwd weight conv kernel.
* Fix clang formatting.
* Fix the bwd weight conv to gemm mapping for num merged groups > 1.
* GEMM config for conv group merging.
* Fix clang-formatting.
* Remove obsolete comment.
* Fix typos in comment strings.
* Increase the max number of reported errors when testing against reference implementation.
* Rename gemm_config to conv_config.
* Rename GemmConfig to ConvConfig and move NumGroupsToMerge into ConvConfig.
* Change num_groups_to_merge to a boolean flag in the ck tile grouped conv example.
* Run clang-format.
* Add number of merged groups into kernel name string.
* Remove group merging flag from CK Tile grouped conv example.
* Implement argument passing to element-wise functions for fwd convolution
* Add files for fwd + bias + clamp example
* Implement Bias
* Implement Clamp
* Elementwise function composition
* Composition unit test
* Implement fwd + bias + clamp example
* Simplify argument passing and composition
* elfunc -> bias_and_clamp
* Rename function to specify example
* Move element-wise function instantiation to kernel
* Make bias a runtime tensor
* No ugly namespace aliasing
* Initialize element-wise function on host
* Remove function initialization helper, simplify Compose initialization
* Remove unintended LSP compatibility patch
* Clean up includes and unused code
* Switch names in cshuffle epilogue
* Move CDElementwise to conv traits
* Re-add required include
* Initialize bias in same way as other tensors
* Better type specification for ds pointer
* Disable 1D convolution
* Add warning for non-group-constant bias
* Adding RapidJson Library
* Adding Json Dumps in all CK_Tile Examples
Not verified yet
* Adding json to cktile Batched Transpose
* adding json dumps to layernorm2d_fwd
* Adding json dump to flatmm_basic
* Adding RapidJson Library
* Adding Json Dumps in all CK_Tile Examples
Not verified yet
* Adding json to cktile Batched Transpose
* adding json dumps to layernorm2d_fwd
* Adding json dump to flatmm_basic
* Adding json in 03_gemm
* Add json dump to 16_batched_gemm
* Add json dump to gemm_multi_d_fp16
* Add json dump to grouped_gemm
* fix fmha_bwd/fwd
* Fix clang-format errors
exclude include/rapidjson in jenkins as its a third-party library
* Saparating function and defination.
* Update Documentation of 03_gemm
* Refactoring as per code review
* Disable fp8 instances on unsupported targets (#2592)
* Restrict building of gemm_universal_preshuffle_f8 instances to specific targets in CMakeLists.txt
* Add condition to skip gemm_xdl_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt
* Add conditions to skip unsupported targets for gemm_universal_preshuffle_f8 and gemm_xdl_universal_preshuffle_f8 instances in CMakeLists.txt
* Refine conditions to exclude gemm_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt
---------
Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>
* fix clang format
* remove duplicate lines of code from library/src/tensor_operation_instance/gpu/CMakeLists.txt
* Fixing Readme and unifying jsondumps
* adding moe_smoothquant
* adding fused_moe
* Fixing Readme for batched_gemm
* Fixing Readme for grouped_gemm
* adding flatmm
* adding gemm_multi_d_fp16
* adding elementwise
* adding File name when json is dumped
* Fixing Reduce after merge
* adding batched_transpose
* Adding Warptile in Gemm
* Fixing Clang Format
---------
Co-authored-by: Aviral Goel <aviral.goel@amd.com>
Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* base working version for single groupped conv bwd data
* Fix 2d descriptor
* fix groups
* Add 3d support
* fixes
* fixes
* fixes
---------
Co-authored-by: Jakub Piasecki <jakpia21@gmail.com>
