1f69421434
The shared-SRD buffer_load_dword_lds path that K_mem_load / V_mem_load use
wraps the per-lane voffset (int32 bytes) once
num_blocks * page_size * row_stride * sizeof(T) > INT32_MAX,
silently returning wrong data on large paged-KV pools (e.g. >4 GB caches).
Add a second path, async_load_tile_raw_long, that issues the same load via
__builtin_amdgcn_global_load_lds with per-lane 64-bit base pointers, lifting
both 4 GB limits (SRD size + voffset). Per-issue LDS pointers are computed
explicitly because the intrinsic sets m0 itself, so the old m0_set / m0_inc
bookkeeping doesn't apply. The path also clamps lane_elem_off to the live
buffer range to mimic the original SRD's hardware OOB behaviour.
Dispatch is a wave-uniform runtime branch on a new
cache_ptr_int32_overflow_possible flag plumbed from
unified_attention_args through MakeKargs into the pipeline operator().
Small caches keep the original buffer_load throughput; only the (rare)
>4 GB cache pays the global_load_lds cost.
k_page_offsets / v_page_offsets are widened to long_index_t. The original
buffer_load path implicitly narrows back to int32 when forwarding through
async_get_vectorized_elements_raw, which is intentional and safe whenever
the overflow flag is false.
For diagnostics, also derive a constexpr KWaveSpanInN =
(LaneGroups - 1) * NumWarps + 1 inside the pipeline; when this exceeds
page_size a single buffer_load spans multiple random pages, so the
per-issue SRD-rebase optimisation (not implemented yet) would not apply
even on a sub-4 GB cache. Informational only today.
Test: ua-test-scripts correctness sweep (245/245 pass), plus
test_single_shape.py -b 32 -sq 8192 -sk 120000 -hq 64 -hk 8 -d 64 \
--num-blocks 1200000 --block-size 16 --test
which previously returned wrong data due to the int32 wrap and now passes
with max abs diff 1.22e-04 vs Triton.
Co-authored-by: Cursor <cursoragent@cursor.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.