e62710e461
* ck_tile kernel for gemm with groupwise quantized A or B tensor. This change introduces new pipelines with Intrawave scheduler and block gemm primitives that loads the scale tensor to registers to perform dequantization post MFMA on C tensor in registers. Scale tensor data, AQ/BQ is spliced across threads in registers and not stored in LDS. Current support is for the following combinations, but it should be fairly straightforward to extend support to more formats. 1. fp8, fp8 -> f32 2. bf8, bf8 -> f32 3. i4, fp8 -> f32 4. i4, bf8 -> f32 Group size can go down to as low as K length of underlying WarpGemm primitive. For Gemm problems with quantized B tensor, this change also introduces preliminary support for flatmm pipeline which loads B tensor directly into registers. * [Block Scale Gemm] Only run gemm quant examples on __gfx94__ - Only run gemm quant examples on __gfx94__ for usage of `v_cvt_pk_fp8_f32` - Format the code * [Block Scale Gemm] Remove Bquant Gemm BlockScale This cleanup is in preparation for future development of bquant. By isolating Aquant-related code, we can streamline the codebase and make it easier to add and maintain bquant functionality in subsequent updates. * [Block Scale Gemm] Format code with clang-format-12 The latest clang-format (v19) in ROCm 7.0 generate different result than clang-format-12 which is used in CK CI. Format code with clang-format-12 for consistency. * [Block Scale Gemm] Split the k direction loop - Split the k direction loop in block_universal_gemm_as_quant_bs_cr.hpp to make the logic clearer. - Disable C transposition. * [Block Scale Gemm] Move block scale gemm example to 38_block_scale_gemm * [Block Scale Gemm] Update copyright * test * Add TailHandler * Move TileDistributionEncodingPatternAQ * Refactor * refactor * fix bug * fix bug * help solve the PR comment * Format the code * [Block Scale Gemm] Add unit tests * [Block Scale Gemm] Add support to 16x16x32 MFMA - Add support to 16x16x32 MFMA - Fix a bug when exchange data crossing lanes --------- Co-authored-by: Vijay Krishnamoorthy <vjkrish@meta.com> Co-authored-by: Cong MA <congma13@ctr2-alola-ctrl-01.amd.com> Co-authored-by: ThomasNing <thomas.ning@amd.com>
Composable Kernel Tile
concept
ck_tile provides a programming model with templated abstractions to enable users to implement performance-critical kernels for machine learning workloads. introduces following basic concepts to help users building your own operator
- tensor coordinate transformation, this is the core concept of layout/index transform abstraction in both compiler time and run time.
- tile-based programming model, including tile-level api and the concept of distributed tensor.
ck_tile is independently from the old ck, located under /include/ck_tile. You don't need to include anything from old CK, ck_tile has similiar (indeed almost the same) implementations for users to build operators. We will have a transition period to pull everything from old ck into ck_tile, stay tuned.
component
ck_tile is splitted into several componenets including core, host, ops/gemm, ops/fmha... each component you only need to include a single header (e.g #include "ck_tile/core.hpp", #include "ck_tile/ops/fmha.hpp") then you are able to use the function/structure inside (different from old ck)
[core]
ck_tile/core contains all the basic data structure and function to build the kernel, you can only include this header and build your own operators that utilizing all the basic building blocks introduced in ck.
core/container
- array, store runtime variables with fixed length (tensor index, register buffer, etc...)
- tuple, same as std::tuple, hold different type of data, and one of the solution to achieve multiple buffer.
- sequence, compile time integer sequence used to build various internal structures, or to describe tile size
- other convenient structure build on top of above 3
core/numeric
- gpu data type like
fp16_t,bf16_t,fp8_t... and the conversion between each other - constexpr integer similiar to std::integral_constant to be used as compile time integer.
- math functions and numeric utilities
core/algorithm
- coordinate transformation system, used to build tensor transform and compile time indexing. This is the core idea introduced in old
ckto describe how a tensor is build by several basic transform primitives likemerge/unmerge/embedetc... and how we indexing into a ND tensor that finally mapped to 1D memory offset.
core/tensor
- tensor descriptor, to describe how a ND tensor
- distributed tensor, describe the storage of this tensor, and the distribution of how a collection of threads collaborately work for this tensor.
- tile level API, including
load_tile,store_tile,shuffle_tile,slice_tile, etc...
[host]
ck_tile/host contains all the host side utilities to launch a kernel, create the device buffer, and some reference implementations. This can be used to create examples (like that under ck_tile example folder) and simple executable to invoke this kernel, so if you only need ck_tile to build your own device library then it's OK to not include this. Based on this, it is recommended to include the specific header you needed under this folder to avoid including unwanted headers (e.g, only include ck_tile/host/kernel_launch.hpp), unless you are writing a host executable.
[ops/gemm, ops/fmha, ops/reduce...]
our implementation of different device operators.
- warp, warp tile level operator
- block, block tile level operator
- pipeline, pipeline that can achieve a customized tile level mainloop (or epilogue). By switching different pipeline to the kernel template you can have different kind of pipeline optimizations.
- kernel, template interface for users to instantiate a particular kernel
[ops/epilogue]
epilogue part of our kernel. We may extend this epilogue part to let users to build their own cutomized epilogues.
[ref]
reference implementation of cpu or gpu. This folder is supposed to include a specific header on demand.
examples
currently we put all ck_tile related example under /example/ck_tile folder. Please check each example's subfolder.