Commit Graph

7 Commits

Author SHA1 Message Date
Aviral Goel
4dcc3e59c1 chore: update copyright header for misc files (#3402)
* chore: update copyright header for misc files

* fix: typo in kernel resulting in ci failure
2025-12-11 08:25:29 -08:00
JH-Leon-KIM-AMD
4baa4c9fae [CK, CK_TILE] Add GPU Reference Implementations for Grouped Convolution (#3216)
* 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>
2025-12-03 21:14:21 +02:00
Aviral Goel
de6466481f chore(copyright): update copyright header for include directory (#3293) 2025-11-26 11:00:05 -07:00
linqunAMD
9fcc1ee9fd Support Wave32 in CK_TILE - Part 1 (#2594)
* Support wave32/wave64 in CK_TILE - Part 1

* remove blocksize in kernel launch

* fix build error

* fix clang format

* fix clang format 2

* fix clang format 3

* fix fmha build error

* fix fmha build 2

* fix fmha build 3

* fix build error 4

* address review comment

* update change log

* replace KernelBlockSize with kBlockSize

* fix CI fail

* fix clang format

* address review comment and rebase code.

* fix universal test fail

---------

Co-authored-by: Lin, Qun <Quentin.Lin+amdeng@amd.com>
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
2025-08-18 10:08:31 -07:00
Illia Silin
504b101da3 upgrade from clang-format-12 to clang-format-18 (#2568)
* upgrade to clang-format-18

* update to clang-format-18 in pre-commit-config
2025-07-28 11:34:07 -07:00
carlushuang
6df5fe2ad8 [CK_TILE]naive attn support FP8 KVCache quant (#1747)
* quant

* fix bug

* simple smoothquant after softmax

* update kv-quant

* update stride

* fix fp8-pertoken-kvcache

* update int8/fp8 quant support

---------

Co-authored-by: so <a.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
2025-01-03 18:43:07 +08:00
carlushuang
77a38e0211 [CK_TILE] naive attn (#1708)
* add reference attention fwd

* refactor addresser

* update

* paged, and i8 reflect-quant

* lets call it forward-quant

* fix error in decode variation

* update naive-attn

* fix page table

* fix build err
2024-12-12 11:54:03 +08:00