7a710c3542
feat(ck): Extend and optimize Quant Gemm Kernel for Aiter a8w8 (#8423) ## Motivation JIRA ID : ROCM-20362 JIRA ID : ROCM-26313 The main purpose of this PR is to allow using CK and CK Tile GEMM for Aiter a8w8 (row-col quantization) and improve its performance. ## Technical Details ### Multiple D for Aiter a8w8 with bias * Support multiple D (bias) in Quant GEMM kernel * Extend CShuffleEpilogue * support int8 -> int32 WarpGemms with row-col quantization * allow shuffling in fp32 before applying multiple D to prevent precision loss ### Large tensors support * Support large tensors in the Quant GEMM kernel by offsetting pointers of matrices A, D and C. This feature can be used when M is large, N and K are relatively small and layout is RCR, it's currently enabled only row-col quantization. * Allow broadcasting of D column vectors in the old CK's `DeviceGemmMultiD_Xdl_CShuffle_V3` with large tensors, this case is used to implement row-col quant scales in Aiter. ### Optimization and workarounds * Use literal 0 as scales for unscaled 16x16x128 and 32x32x64 mfma: llvm uses v_mfma for `__builtin_amdgcn_mfma_scale_f32_..._f8f6f4` instead of v_mfma_scale only if scales are literal 0 values. These instruction don't need loading scales and save vector registers. See https://github.com/ROCm/llvm-project/blob/therock-7.13/llvm/lib/Target/AMDGPU/SIInstrInfo.td#L317-L327 * Add workaround for inefficient buffer_load to lds on 7.2 The 3rd argument of buffer_load_dwordx4 is a scalar register. But the compiler generates a waterfall loop as if lanes can have a different value, even though the original values comes from as scalar register. * Use buffer_store_dwordx4 to store 8 bf16 values in epilogue instead of two buffer_store_dwordx2 * Optimize eight waves pipeline: * Improve instruction scheduling * Remove unneeded barriers * Use nontemporal store/load for C and D matrices in the Quant GEMM kernel (they are used once per block but may consume cache that can be better used for matrices A and B) * Use more efficient padding in epilogue with CTransposed ## Test Plan A new test is added for multiple D Quant Gemm (`TestCkTileGemmRowColQuantMultiD/*.RowColQuantMultiDTest`): ``` ninja test_tile_gemm_quant_rowcol && bin/test_tile_gemm_quant_rowcol ``` Testing the large tensor support is not feasible with the current testing infrastructure because of reference calculations on CPU - it takes several minutes to run a single test case. Such cases are tested manually in Aiter. ## Test Result <!-- Briefly summarize test outcomes. --> ## Submission Checklist - [ ] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
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.