* 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 <>
* 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.
* 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>
* Improve the grouped conv kernel name generation in CK Tile.
* Fix building CShuffle epilogue tests.
---------
Co-authored-by: Bartłomiej Kocot <barkocot@amd.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
* Have a workable version for SGPR
* have a workable version for atomic add
* Revert "have a workable version for atomic add"
This reverts commit 792377a590c26cfff9c8f545d9a9e8484a7422eb.
* substitute with the new sgpr read api
* update the CHANGELOG
* have a workable version for atomic add
* Revert "have a workable version for atomic add"
This reverts commit 792377a590c26cfff9c8f545d9a9e8484a7422eb.
* change to static for logic
* have a workable version for atomic add
* Revert "have a workable version for atomic add"
This reverts commit 792377a590c26cfff9c8f545d9a9e8484a7422eb.
## What's New
Add Split-N support for grouped convolution forward to handle tensors >2GB by splitting the batch dimension.
## Bug Fix
Fixed 32-bit integer overflow that caused crashes with 6+ splits:
- Use `long_index_t` for batch offset calculations
- Remove redundant GemmM initialization in constructors
## How It Works
- Automatically splits batch dimension when tensor exceeds 2GB
- Uses grid.z dimension for parallel processing of splits
- Each split processes a subset of batches independently
## Testing
Verified with tile_example_grouped_conv_fwd:
- n=3000 (6 splits) ✓
- n=3500 (7 splits) ✓
- n=10480 (40 splits) ✓
* 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>
