Commit Graph

12 Commits

Author SHA1 Message Date
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
d85f065b15 chore(copyright): update copyright header for example directory (#3273)
* chore(copyright): update copyright header for codegen directory

* chore(copyright): update copyright header for example directory
2025-11-24 18:02:41 -08:00
John Shumway
ad57f6ef0b [CK_BUILDER] Put global CK functions in an the CK namespace (#3232)
* Wrap ck host utitlies in CK namespace.

The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.

Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.

There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate  kernels from either template library.

* Add using declarations to test code.

After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.

* Add using declarations to client examples.
2025-11-19 11:23:02 +01:00
yinglu
2a73eb3bc0 Simulate TF32 with BF16x3 (#3142)
* tf32:bf16x3:use bf16x3 emulate tf32 gemm

* change blockwiseGemm to demo bf16x3

* temp push

* self review

* self review

* fix multi-device compile error

* bug fix

* code refactor

* limit to gfx950

* enhance gemm gfx942 threshold

* lower change from blockwise to warpwise

* refact codes

* refact codes

* error fix

* change threshold

* bug fix

* fix threshold error

* change host reference implement to same as device

* bug fix

* bug fix

* code refact

* fix clang-format fail

* code refine
2025-11-13 16:21:09 -08:00
yinglu
dd7af118d7 TF32 POC in Conv3d on MI30x platform #2763 (second attempt) (#2852)
* 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
2025-09-17 14:50:15 -07:00
Illia Silin
03b59f8c76 Revert "feature:tf32:add initial conv3d fwd kernel support (#2763)" (#2848)
This reverts commit c51102144f.
2025-09-15 08:27:04 -07:00
lym
c51102144f feature:tf32:add initial conv3d fwd kernel support (#2763) 2025-09-15 21:03:00 +08:00
Rostyslav Geyyer
e626d5202a Add instances for conv_scale with fp8 in/out (#1193)
* Add fp8 conv instances and client example

* Format

* Add example

* Update cmakelists

* Add profiler mode

* Format

* Fix copyright headers
2024-03-15 09:50:03 -07:00
Illia Silin
b94fd0b227 update copyright headers (#726) 2023-05-31 18:46:57 -05:00
Po Yen Chen
4a2a56c22f Rangify constructor of HostTensorDescriptor & Tensor<> (#445)
* Rangify STL algorithms

This commit adapts rangified std::copy(), std::fill() & std::transform()

* Rangify check_err()

By rangifying check_err(), we can not only compare values between
std::vector<>s, but also compare any ranges which have same value
type.

* Allow constructing Tensor<> like a HostTensorDescriptor

* Simplify Tensor<> object construction logics

* Remove more unnecessary 'HostTensorDescriptor' objects

* Re-format example code

* Re-write more HostTensorDescriptor ctor call
2022-11-11 11:36:01 -06:00
Chao Liu
204ef976ca add more datatype to gemm+gemm and conv+conv example (#397)
* refactor

* refactor

* adding int4/int8/fp16/bf16 for conv+conv and gemm+gemm

* adding int4/int8/fp16/bf16 for conv+conv and gemm+gemm

* clean
2022-09-01 09:31:17 -05:00
Chao Liu
500fa99512 Clean up conv example, Instances, profiler and test (#324)
* convnd_fwd fp16 example

* update example

* update example

* update instance

* updating refernce conv

* update reference conv

* update conv fwd profiler

* update conv 1d and 3d instance

* update include path

* clean

* update profiler for conv bwd data and weight

* update conv bwd weight

* clean

* update conv example

* update profiler for conv bwd weight

* update ckprofiler for conv bwd data

* fix reference conv bwd data bug; update conv bwd data test

* update examples

* fix initialization issue

* update test for conv fwd

* clean

* clean

* remove test case too sensitive to error threshhold

* fix test

* clean

* fix build

* adding conv multiple d

* adding conv multiple D

* add matrix padder

* add gemm padding to convnd

* adding group conv

* update gemm multi-d

* refactor

* refactor

* refactor

* clean

* clean

* refactor

* refactor

* reorg

* add ds

* add bias

* clean

* add G

* adding group

* adding group

* adding group

* update Tensor

* clean

* update example

* update DeviceGemmMultipleD_Xdl_CShuffle

* update conv bwd-data and bwd-weight

* upate contraction example

* update gemm and batch gemm with e permute

* fix example build

* instance for grouped conv1d

* update example

* adding group conv instance

* update gemm bilinear instance

* update gemm+add+add+fastgelu instance

* update profiler

* update profiler

* update test

* update test and client example

* clean

* add grouped conv into profiler

* update profiler

* clean

* add test grouped conv, update all conv test to gtest

* update test
2022-07-29 18:19:25 -05:00