0f10e6d921
* Change the return type of run_gemm_combinations in the basic tests * Change the return type of run_gemm_combinations in the universal tests * Add universal GEMM tests for bf16 x pk_i4 and fp16 x pk_i4 * Add universal GEMM test for fp8 x pk_i4 * Add basic GEMM tests for bf16 x pk_i4, fp16 x pk_i4 and fp8 x pk_i4. * Add missing GemmTypeConfig<ck_tile::fp8_t, ck_tile::pk_int4_t, ck_tile::half_t> * Add missing GemmTypeConfig<ck_tile::bf16_t, ck_tile::pk_int4_t, ck_tile::bf16_t> * No need for utility in test_ck_tile_elementwise_1d * Fix conversion from pk_int4x4_t to bf16x8_t in PassThroughPack8 * Avoid union-based type punning in float_to_bf16_truc_raw to make it constexpr compliant * For consistency also make float_to_bf16_truc_nan_raw constexpr compliant by removing the union * Use a static_cast to bfloat16_t only when CK_TILE_USE_LLVM_BUILTIN_BF16 is enforced * Convert from float to bf16 during compilation rather than using magic values * Fix conversion from pk_int4x4_t to fp8x8_t in PassThroughPack8 * Comment out the basic test for fp16 x pk_i4 as it does not pass * Add missing GemmTypeConfig<ck_tile::bf8_t, ck_tile::pk_int4_t, ck_tile::half_t> * Fix conversion from pk_int4x4_t to bf8x8_t in PassThroughPack8 * Add basic and universal GEMM tests for bf8 x pk_i4 * Switch back to amd_assembly_i4_to_fp8x8 in PassThroughPack8 as it works now * Switch back to amd_assembly_i4_to_bf8x8 in PassThroughPack8 as it works now * Remove the inefficient fallbacks for fp8 and bf8 in elementwise/unary_element_wise_operation.hpp * Use explicit macros for enabling and disabling the the constexpr lookup based converters * Fix two failing tests * Avoid union-based type punning in float_to_bf16_rtn_raw to make it constexpr compliant * Use float_to_bf16_rtn_raw instead of float_to_bf16 to create the bf16 lookup table for use in conversions from pk_int4 to bf16 * On ROCm 7.0.1 we need an explicit cast to from uint16_t to bf16_t
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.