db05d61136
=?UTF-8?q?[CK=20TILE]=20Unification=20Work=20=E2=80=93=20?= =?UTF-8?q?More=20accurate=20tests=20for=20MmaPipelines=20(#6212)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Motivation This PR solves several issues: #### More accurate tests for MmaPipelines The current tests for the MmaPipelines (test_amdgcn_sparse_mma, test_amdgcn_wavewise_mma) use explicit input fragment vectors filled with 1s, and only check the output of a single lane. We should have tests that actually use the MmaPipelines with non-trivial input matrices and verify the complete output. Some other aspects of the current MmaPipelines tests that I noticed and deserve some attention: 1. There is sometimes iteration over K outside of the pipeline, which is then included in WaveTileK or FragK, which is not correct. We should remove it, move K iteration inside of the pipeline, or be more clear about this outer-K loop size and how it propagates downwards. 2. There is very tight coupling between the kernel, gtest code, and test_pipeline helper, requiring a lot of information and functions to be passed back and forth. 3. The test_pipeline helper is doing a bunch of register-related logic on the host (related to point 1) 4. Without this register logic the only thing it does is check the device, call the kernel, and check the output, but with a lot of boilerplate. #### Test helper for detecting target arch at HOST runtime There is a really apparent issue we faced while writing tests: Scenario: 1. Compile a test that supports both gfx950 and gfx1201 for gfx950 2. Run the test on a server that only has gfx1201 GPU Actual: Segmentation fault Expected: The test can correctly detect from HOST runtime that the DEVICE target_id was different and skips the test. Notes: The only way of detecting the COMPILER_TARGET_ID in the existing "arch" framework is launching a kernel and calling `get_compiler_target()` (so, from a DEVICE code). This will create a segmentation fault if the current arch differs from the target arch. To cope with this issue, we propose to export the compiler target(s) (note they can be many) through `projects/composablekernel/test/ck_tile/core/arch/CMakeLists.txt` and define a test helper to deal with such cases. #### Add composition support to Transforms We have a small number of Transforms which act on MmaOp input and output data, before and after the MmaOp call respectively. These are currently implemented to work on an MmaTile level, but in theory they are also supposed to work at a WaveTile level, i.e. after composition of multiple MmaTiles to create larger effective MNK dimensions. Currently the composed MmaTiles look like 2D C-style arrays of the individual MmaTile level register vectors (see WaveWiseMmaPipeline). The transforms should be able to take these and perform the proper transforms to the whole WaveTile at once. This might allow for better performing transformations. Note: This PR handles the SparseTransform case and if we don't end up doing scale as a transformation, there isn't really much left to do. If we end up having only the sparse transform as a non-trivial transform, then we could also consider removing the Transform framework.
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.