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b88bbcb5fa427b9964aba27d69ece29ba998713a
409
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Zhang Jason
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b88bbcb5fa |
[AMD][gfx950] Add ds_read_tr16_b64 / ds_read_tr8_b64 support for gfx950 LDS transpose reads (#2085)
* add gfx950 ds_read_tr support * update with format checking |
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Lei Wang
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6fee85002e |
[Release] Bump version into 0.1.9 (#2060)
* bump version into 0.1.9 * remove legacy test |
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Lei Wang
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15309f5cba |
[CUDA] Improve int4 GEMM lowering and packed codegen support (#2073)
* [Cleanup] Remove redundant int4 MMA code paths * [CI] Apply format.sh updates * lint fix * fix * Refactor FP8 type determination to utilize `T` module constants Updated the `determine_fp8_type` function to return constants from the `T` module instead of string literals for FP8 formats. This change enhances consistency and aligns with recent updates in type handling. Adjusted return statements for both HIP and CUDA scenarios to use `T.float8_*` constants. * Refactor FP8 type usage in tests to directly call `determine_fp8_type` Updated test files to replace `T.dtype(determine_fp8_type())` with direct calls to `determine_fp8_type()` for improved clarity and consistency in FP8 type handling. This change aligns with recent refactoring efforts in type determination. * Remove print statements from `assert_tl_matmul_correctness` and `test_assert_tl_matmul` functions to clean up test output. This change enhances the clarity of test results by eliminating unnecessary console output. * Enhance CI workflow for CuTeDSL examples with Python 3.12 and CUDA-12.8 - Added a step to list generated files and clean up Python cache directories. - Introduced a new job for running CuTeDSL examples on self-hosted NVIDIA runners. - Set up environment variables for CUDA and Python, including caching configurations. - Implemented error handling to clear cache on setup failure. - Updated steps for installing dependencies and running examples, ensuring compatibility with the latest Python and CUDA versions. |
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Chenhao Xu
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64bd742071 |
[Bugfix] Fix reduce layout (#2074)
* fix: fix reduce layout * test: add targeting test |
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Lei Wang
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9c95a42dfb |
[Refactor] Phaseout legacy util map_torch_type with T.dtype.as_torch (#2075)
* Remove redundant `map_torch_type` in favor of `T.dtype.as_torch()` `map_torch_type` was a standalone function that converted dtype strings or TVM DataType objects to `torch.dtype`. This was redundant since `T.dtype.as_torch()` (and `T.dtype(x).as_torch()`) already provides the same functionality with better handling (HIP/CUDA awareness, proper dtype lookup tables, etc.). Changes: - Remove `map_torch_type` from `tilelang/utils/tensor.py` and its re-export - Make `T.dtype()` idempotent for existing dtype objects (pass-through) - Add backward-compat for `"e4m3fnuz_float8"` TVM internal name in `as_torch()` - Replace all usages across production code, tests, and examples - Rebuild Cython extension to reflect the import change * Refactor output and accumulation data types in GEMM FP8 example Updated the `out_dtype` and `accum_dtype` variables in the `example_tilelang_gemm_fp8_intrinsic.py` file to use `T.float32` instead of string literals. This change enhances type consistency and aligns with recent updates in type handling. |
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Lei WangandClaude Opus 4.6
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046b1bdc69 |
[FFI] Remove upper version bound on apache-tvm-ffi (#2071)
* ugrade tvm
* Remove upper version bound on apache-tvm-ffi
The <0.1.10 pin was introduced to avoid a derived_object regression,
which has since been resolved. Removing the cap allows compatibility
with newer versions of apache-tvm-ffi.
* Enhance ptxas usage level handling in CUDA compilation
Added type conversion for ptxas_usage_level in both tilelang_callback_cuda_compile and LibraryGenerator classes to ensure it is treated as an integer when specified. This change improves the robustness of the configuration handling for CUDA compilation.
* Fix IR string representation issues from tvm-ffi upgrade
The new tvm-ffi version changes str() output for IR nodes from compact
script format to verbose repr format. This caused two issues:
1. T.call_packed("name") is now tir.tvm_call_packed with value="name"
2. tir.Var str() now produces "tir.Var(span=None, ...)" instead of name
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Simplify TMA descriptor globalAddress handling in cutedsl wrapper
Since global_address is now converted to string by pythonic_expr_func
in parse_tma_descriptor_args, remove the unnecessary tir.Var type check
and .name extraction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Refactor CUDA test for vectorization and update IR string representation
Updated the test for vectorization in CUDA by replacing the direct function definition with a JIT-compiled kernel. Additionally, modified the string representation of IR modules in multiple tests to use the script format instead of the default string format, ensuring consistency and clarity in assertions.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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Lei Wang
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99ee74b343 |
[CUDA] Support int4 T.gemm (#2063)
* Support int4 T.gemm and direct packed cp.async lowering * Keep cp.async vectorization as a late fallback * Change tl.ptx_cp_async to use num_elems semantics * fix test |
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Lei Wang
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e1d63880f6 |
[Cache] Refactor cache namespace layout (#2057)
* Refactor cache namespace layout * Update atomic cache tests for namespaced paths * Ensure namespaced cache dirs exist before save * Use auto backend in grouped gemm ptr example * Keep grouped gemm ptr example off cutedsl * Always use auto backend for grouped gemm ptr * Skip grouped gemm examples in cutedsl CI * Ignore grouped gemm examples in cutedsl workflow |
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TerminusAkiviliandLeiWang1999
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04468a3425 |
[Bugfix] Fix stage-expanded annotated-layout aliases in LayoutInference (#2031)
* Fix annotated layout inference for dtype-changing views * lint fix --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
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Tong WU
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9c5fe44c7a |
[BugFix] Keep shared-prelude local vars in producer-consumer WS (#2055)
* [BugFix] Keep shared-prelude local vars in producer-consumer WS Preserve prelude definitions needed by shared LetStmt values during warp-specialized pipeline rewriting, and avoid cloning local.var buffers into the producer branch. This keeps grouped GEMM batch index calculations shared and fixes incorrect TMA batch selection. * add test * lint |
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Chaofan Lin
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77cbe6da87 |
[Feature] Introduce T.CUDASourceCodeKernel (#1970)
* [WIP][Feature] Introduce CUDARunner * refactor * refactor to T.SourceCodeKernel api * remove streamk change * remove runner * remove prim func params * enhance API * add entry_name * add IsCodeBlockKey * remove misc file * cleanup code * move parse logic to python through callback * fix * comments |
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27f1f81c61 |
[Feature][Example] Introduce CLC tile schedule and add example for sm100 GEMM (#2029)
* [Feature] Add Blackwell Cluster Launch Control (CLC) primitives
Expose cluster launch control query operations (try_cancel,
try_cancel_multicast, is_canceled, get_first_ctaid_{x,y,z}) in both
the C++ codegen path and the Python tilelang.language API, plus a new
mbarrier_arrive_expect_tx builtin used with CLC multicast completion.
Enables writing persistent SM100 kernels that use the hardware scheduler
to fetch the next tile coordinate.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [Example] Add SM100 persistent 2-CTA GEMM example using CLC scheduling
Adds examples/gemm_sm100/gemm_tcgen5mma_ws_clc.py, a warp-specialized
persistent GEMM for Blackwell (sm_100) that uses tcgen05 2-CTA MMA
with cluster launch control tile scheduling. The kernel is split into
producer/MMA/scheduler/epilogue warp groups, with the scheduler warp
running on both CTAs and feeding next-tile coordinates via the CLC
hardware multicast into schedule_arrived directly (no intermediate
barrier). Storage sync is disabled and all syncs are placed manually.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* lint
* typo
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* remove unnecessary test
* Add CLC query intrinsics and async fence
* Refactor CLC response handling by introducing CLCResponseDecode struct. Update clc_get_first_ctaid_x/y/z and clc_is_canceled functions to utilize the new decode response method for improved clarity and maintainability.
* Enhance CLC scheduling in persistent GEMM by introducing shared variables for schedule validity and tile ID. Update cancellation checks to utilize the new shared variables, improving clarity and maintainability of the scheduling logic.
* test fix
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
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Lei WangandFreebase6912
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d098f51b8f |
[Enhancement] Use atomic directory rename for cache writes (#1982)
* [Enhancement] Use atomic directory rename for cache writes Replace per-file os.replace with write-to-staging + os.rename to ensure other processes never see an incomplete cache directory. Also add cleanup of stale staging dirs on init. * [Bugfix] Fix atomic cache save: abort on critical file failure and fix staging cleanup - Let kernel lib and params save failures propagate instead of swallowing them, so the staging directory is cleaned up rather than renamed as an incomplete cache entry. - Move autotuner staging directory to TILELANG_CACHE_DIR so that _cleanup_stale_staging_dirs() can find and remove stale entries. * Fix atomic cache save recovery and error handling * Remove unnecessary blank lines in _FakeAdapter and _FakeKernel classes in autotune and cache test files. * Tighten atomic cache completeness checks * Simplify atomic cache save error handling * submodule update * remove legacy test --------- Co-authored-by: Freebase6912 <amid-gauze-racing@duck.com> |
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Lei WangandFreebase6912
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891109ea87 |
[Transform] Add InjectTcgen05Fence pass (#2003)
* [Transform] Add InjectTcgen05Fence pass for Blackwell (SM100+)
On Blackwell GPUs, the tcgen05 accumulator (TMEM) resides in a separate
address space that is not synchronized by regular thread barriers like
__syncthreads() or mbarrier. Two PTX fence instructions are required to
ensure cross-thread visibility of TMEM state:
tcgen05.fence::before_thread_sync -- flush TMEM before barrier
tcgen05.fence::after_thread_sync -- pull TMEM after barrier
This commit introduces the `InjectTcgen05Fence` TIR pass that
automatically wraps every `tvm_storage_sync("shared")` call with the
fence pair when the target is SM100+ and the function uses tcgen05/TMEM
operations.
Changes:
- Define two new TIR intrinsic Ops: `tcgen05_before_thread_sync` and
`tcgen05_after_thread_sync` (builtin.h/cc)
- Add codegen support to emit `tl::tcgen05_before_thread_sync()` and
`tl::tcgen05_after_thread_sync()` (codegen_cuda.cc)
- Implement the `InjectTcgen05Fence` pass (inject_tcgen05_fence.cc)
- Register the pass in the Python transform module (__init__.py)
- Insert the pass in OptimizeForTarget after ThreadSync (phase.py)
* fix
* Add conservative tcgen05 fence injection
Extend InjectTcgen05Fence to cover shared storage sync and linear wait/use, use/arrive handoffs on SM100+ kernels, update the surrounding docs, and add transform coverage. Also lower tcgen05 ld/st copies through dedicated intrinsics instead of call_extern strings, with matching CUDA and CuTeDSL codegen support.
* Use tcgen05 fence injection in SM100 examples
Remove the hand-written tcgen05 before/after thread-sync fences from the ws and persistent GEMM examples now that InjectTcgen05Fence covers these linear wait/use and use/arrive handoffs.
* lint fix
* Drop tcgen05 call_extern fence compatibility
Simplify InjectTcgen05Fence to recognize only the lowered tcgen05 intrinsics seen after LowerTileOp, and update the transform tests to construct tcgen05_ld directly instead of relying on legacy call_extern strings.
---------
Co-authored-by: Freebase6912 <amid-gauze-racing@duck.com>
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Nguyen Huy HoangandLeiWang1999
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235ad7eda4 |
Add regression test for 1D TMA load compilation and execution (#1989)
* refactor: add regression test for 1d tma load compilation and execution Signed-off-by: Nguyen Huy Hoang <181364121+huyhoang171106@users.noreply.github.com> * lint fix --------- Signed-off-by: Nguyen Huy Hoang <181364121+huyhoang171106@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
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KurisandClaude Opus 4.6
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f309d8147e |
[Bugfix][Subtype] Fix scalar fp4 store/load codegen for non-packed buffers (#2037)
* [Bugfix][Subtype] Fix scalar fp4 load/store codegen and dynamic stride inference for sub-byte types The scalar fp4 buffer access path in codegen_cuda.cc used GetBufferRef which applies a /2 index division (mapping two fp4 elements to one byte), then performed a plain byte assignment — destroying the neighboring nibble. This caused incorrect results whenever fp4 stores couldn't be vectorized (e.g. with dynamic/non-constant strides in StridedTensor). Fix: use tl_fp4_packed_load/tl_fp4_packed_store with (fp4_e2_2_t*) cast for scalar fp4 accesses to non-packed buffers, which correctly handles nibble-level read-modify-write via set_x/set_y. Also add stride_scale factor to _process_dynamic_symbolic in the cython and tvm_ffi adapters, so torch strides (in storage units) are correctly converted to logical element strides for sub-byte dtypes. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * [Test][Subtype] Add correctness tests for fp4 dynamic-stride store codegen Add regression test that scatters fp4 rows into a non-contiguous StridedTensor with dynamic strides and verifies byte-for-byte match against a static-stride reference. Parametrized over block_size. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * [Enhancement][Subtype] Inject stride divisibility assume for sub-byte dtypes and add scalar fp4 store codegen test - Extend InjectAssumes pass to emit `assume(stride % pack_factor == 0)` for non-last-dimension strides of sub-byte buffers (e.g. fp4 with pack_factor=2). This provides the TIR analyzer with divisibility information before LetStmt inlining. - Add `test_subtype_fp4_scalar_store_codegen`: scatter fp4 elements via indirection to force the scalar (non-vectorized) store codegen path and verify nibble-level correctness. - Convert new tests to eager-style @tilelang.jit. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * style: apply clang-format and ruff formatting Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix lint error * fix: unpack 4-tuple in cython_wrapper dynamic_symbolic_map lookup --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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74fc9806fb |
[Refactor] Refactor DecoupleTypeCast Pass (#2026)
* draft
* fix lint
* fix lint
* Improve pass comments
* treat loop-invariant memory access as scalar in vectorization
* Add unit test
* fix lint
* fix b[i] = a[i] + a[i+32] case
* Minor fix
* Tighten DecoupleTypeCast cast detection and add RMW regression test
Skip BufferLoad/BufferStore indices when searching for Cast nodes so an
index-type conversion does not spuriously trigger the decoupling
transformation. Clarify the load-replacement table in visit_for_ — store
entries must feed into it so RMW loads map to the store-side cast buffer —
and cover the a[i] = a[i] + a[i+32] case with a regression test.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Keep loop-invariant stores in memory bucket during vectorize planning
Commit
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Lei Wang
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b3d5981b00 |
[API] Default warp-lane mask to 0xFFFFFFFF for warp-sync builtins (#2039)
Reorder the `mask` parameter to the end with a default of `_FULL_WARP_MASK` for shfl_xor/down/up/sync, any_sync, all_sync, ballot_sync, and match_any/all_sync. Full-warp is the overwhelmingly common case, so making mask an optional trailing kwarg removes boilerplate at call sites and lets old `(value, delta)`-style callers keep working. Update the test suite and docs/instructions.md to match the new signatures. |
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19236b44c0 |
[Feature] Add TIR builtins for warp-level vote and block-level predicate sync (#1858)
* [Feature] Add TIR builtins for warp-level vote and block-level predicate sync * [Refactor] Lower warp-level intrinsics through tl.* ops Convert the warp-vote, warp-shuffle, predicated block-sync, and warp-match builtins from raw `tir.call_extern` wrappers to proper TIR ops registered under `tl.*` and lowered in `codegen_cuda` / `codegen_hip`. This removes Python-side `_IS_HIP_AVAILABLE` branching and pushes the CUDA/HIP split into codegen, where it belongs. * Register new tl ops: any_sync, all_sync, ballot_sync, ballot, activemask, syncthreads_count/and/or, shfl_sync, shfl_xor_sync, shfl_down_sync, shfl_up_sync, match_any_sync, match_all_sync. * Codegen lowering on both CUDA and HIP. uint32->uint64 zero-extension for ballot/activemask now happens in codegen. HIP drops the mask argument for shfl/vote and emits LOG(FATAL) for match_*_sync. * `__match_all_sync`'s int* pred output is hidden behind an immediately-invoked lambda so the wrapper stays expression-form. * Python wrappers normalize Python int masks to uint32 TIR consts via `_as_uint32_mask`, so the emitted source prints `(uint)0xFFFFFFFF` instead of `(int64_t)4294967295`. * Unify `shfl_xor/down/up` signatures to `(mask, value, delta, width=32)`, matching CUDA convention and `shfl_sync`. No in-tree callers existed. * Add tests for match_any_sync and match_all_sync. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8243f7e089 |
Nan propagation option for bf16 and half16 (#1958)
* register op, and add to build file * update semantics of NaN for Max and Min ops in Reduce * lower Max op to cutlass fastmax in general case, _hmax for bf16 and _hmax_nan for hf16 * update semantics of NaN for Max and Min ops in Reduce in reduce.h * develope codes transforming ReduceOpNode in TIR and handling CallNode in CUDA code generator's runtime template * test file for reduce maxmin nan * refine test case message and explanation“ ” ‘ “ * invert the senamtics of global config TL_REDUCE_MAXMIN_NAN_PROPAGATE * lint fix * refactor reduce maxmin NaN propagate into per-call annotation Replace the global tl.reduce_maxmin_nan_propagate pass-config with a per-op nan_propagate flag carried on the ReduceOp annotation map. Frontend exposes it as a kwarg on T.reduce_max/min/absmax (default False, preserving prior behavior). The CUDA codegen MinNode/MaxNode visitors are reverted to plain __hmin/__hmax; only the new tl::max_nan /tl::min_nan CallNode handler emits __hmax_nan/__hmin_nan, so non-reduce Min/Max ops are no longer silently rewritten. ReduceOp::Lower now errors early with a clear message if nan_propagate=True is requested for fp16 /bf16 on a non-CUDA target, instead of emitting an undefined symbol on HIP/CPU. Tests rewritten to parametrize on the kwarg and assert runtime NaN-propagation behavior, not just generated source substrings. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Haoran <chaoran@umich.edu> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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Lei Wang
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5d729eeebc |
[Refactor] Remove GEMM v1 and promote gemm_py to be the canonical gemm op (#2033)
* [Refactor] Remove GEMM v1 and merge gemm_py into the canonical gemm op
GEMM v2 has been the default for a while and no tests or examples still
depend on v1. This removes the legacy C++ GemmNode path entirely and
promotes the former GemmPyNode (Python-delegated lowering) to be the one
and only GEMM tile op.
C++:
- Merge src/op/gemm_py.{h,cc} into src/op/gemm.{h,cc}. GemmPyNode becomes
GemmNode, GemmPy becomes Gemm, FFI type key "tl.GemmPy" becomes "tl.Gemm",
and the op names "tl.tileop.{gemm_py,wgmma_gemm_py,tcgen05_gemm_py}"
collapse to "tl.tileop.{gemm,wgmma_gemm,tcgen05_gemm}". The shared
infrastructure (GemmWarpPolicyNode, GemmInst enum,
GemmWarpPolicyNode::computeWarpPartition) is preserved in place so
GemmSP/GemmSPPy keep compiling unchanged.
- Delete the tl_gemm builtin, its codegen dispatch in
codegen_{cuda,hip,cutedsl}.cc, and its matching branches in
inject_fence_proxy.cc and merge_shared_memory_allocations.cc. tl_gemm_sp
is kept as the remaining async-proxy gemm builtin.
- Drop #include "gemm_py.h" and GemmPyNode/GemmPy references from
inject_pipeline.cc, producer_consumer_ws.cc, and lower_blackwell_2sm.cc.
Python:
- tileop/gemm: rename GemmPy -> Gemm, register as "tl.Gemm", and rename
the FFI callbacks tl.gemm_py.{lower,infer_layout} -> tl.gemm.{lower,
infer_layout}. _ffi_api.GemmPyGemmInst becomes GemmGetGemmInst.
- language/gemm_op.py: delete gemm_v1/gemm_v2 wrappers. gemm() now emits
tl.tileop.gemm directly; wgmma_gemm/tcgen05_gemm drop the _py suffix.
- env.py: remove TILELANG_USE_GEMM_V1 and the use_gemm_v1() helper.
- ir.py: drop the empty v1-era `class Gemm(Node, Scriptable): ...`
placeholder that was colliding with the real tileop.gemm.Gemm wrapper
under the "tl.Gemm" FFI type key.
- contrib/cutedsl: delete cutedsl/gemm_v1.py (runtime helper for the v1
cutedsl lowering path) and its re-export in cutedsl/__init__.py.
Tests / maint:
- testing/python/tilelibrary/test_tilelang_tilelibrary_gemm.py switches
from T.gemm_v2 to T.gemm.
- testing/python/transform/test_tilelang_transform_inject_fence_proxy.py::
test_lower_fence_proxy is rewritten to exercise tl.tl_gemm_sp instead
of the removed tl.tl_gemm, preserving the fence-proxy assertion.
- Delete maint/gemm_v2/ (v1-vs-v2 benchmarking scripts) and drop the
v1-specific comment from maint/scripts/run_local_ci_test.sh.
C++ headers under src/tl_templates/{cuda,hip,cpp}/gemm*.h are left in
place as reference implementations; they are no longer reachable from
the generated code path since the only consumer (tl_gemm builtin) is
gone, but keeping them avoids a large, hard-to-reverse deletion.
* [Fix] Restore cutedsl/gemm_v1.py (Gemm_SM80 / Gemm_SM90 helpers)
The previous commit deleted cutedsl/gemm_v1.py on the assumption that it
was the CuteDSL lowering helper for the removed T.gemm_v1 path. It is
actually a runtime helper module that defines SM80/SM90 CUTE DSL
primitives (make_smem_layout_AB, get_tma_atom, etc.) which are emitted
into generated cutedsl kernel code via
`import tilelang.contrib.cutedsl as tl; tl.Gemm_SM90.get_tma_atom(...)`
(see jit/adapter/cutedsl/wrapper.py CUBIN_TMA_ATOM_INIT_TEMPLATE).
The "v1" in the filename refers to the CuteDSL helper version, not to
the deleted T.gemm_v1 op, so removing it broke the cutedsl code path for
attention_sink, blocksparse_attention, blocksparse_gemm, and deepgemm
examples with `module 'tilelang.contrib.cutedsl' has no attribute
'Gemm_SM90'`. Restore the file and its `from .gemm_v1 import *` wildcard
re-export.
* [Refactor] Restore tl_gemm builtin as a call_intrin path to tl:: templates
The previous commit in this PR deleted the `tl_gemm` builtin entirely on
the argument that no code emitted it anymore. That was too aggressive:
keeping the builtin preserves a deliberate extension point for any
Python-side gemm lowering backend (for example a future
GemmLegacyExtern) to delegate into the existing C++ templates in
src/tl_templates/<target>/gemm*.h via
T.call_intrin("handle", tir.op.Op.get("tl.tl_gemm"),
op_instance_str, A_ptr, B_ptr, C_ptr)
without any extra C++ plumbing. It is also the only thing that keeps
those tl_templates headers reachable rather than dead code.
Restore:
- tl_gemm builtin declaration in src/op/builtin.h (with an updated
docstring describing the call_intrin entry point) and its
TIR_DEFINE_TL_BUILTIN registration in src/op/builtin.cc.
- Codegen dispatch branches in codegen_cuda.cc, codegen_hip.cc, and
codegen_cutedsl.cc that turn a tl_gemm Call into a PrintCallExtern
emission of `tl::gemm_xx<...>(A, B, C)`.
- SMEM alignment and async-proxy fence handling for tl_gemm in
merge_shared_memory_allocations.cc and inject_fence_proxy.cc. These
passes are tied to the builtin's semantics (not to the removed v1
GemmNode) and are still correct the moment anything emits tl_gemm.
- test_lower_fence_proxy in test_tilelang_transform_inject_fence_proxy
reverts to exercising `tl.tl_gemm` instead of the temporary
`tl.tl_gemm_sp` substitution introduced when the builtin was gone.
Note that the current sole gemm lowering path (the merged GemmNode that
delegates to Python via tl.gemm.lower) still emits inline mma/wgmma/
tcgen5mma via the Python macro generators, not tl_gemm. So restoring
the builtin is a no-op for the default path - it only re-enables the
"call into v1 templates from Python" option for anyone who wants it.
* [Fix] Default HIP execution backend to tvm_ffi, not cython
resolve_execution_backend() only promoted cuda and metal to tvm_ffi for
the "auto" path, so hip fell into the else branch and picked cython. But
allowed_backends_for_target() already lists tvm_ffi as an allowed
backend for hip, and the cuda default is tvm_ffi for good reasons:
- The cython SourceWrapper path in src/tilelang/jit/adapter/wrapper.py
has known gaps (for example T.alloc_global is not handled in
create_dispatch_func, which breaks any test that uses an internally
allocated global scratch buffer).
- libgen.py appends user-supplied compile_flags verbatim to the
hipcc/nvcc command. nvcc accepts --use_fast_math, clang++/hipcc does
not, so a CUDA-style flag leaks into HIP compilation.
Neither of these leak through the tvm_ffi backend. Aligning hip's
default with cuda makes the two paths symmetric and unbreaks
test_alloc_global and test_all_attrs_together_lazy on ROCm without
having to mark the tests skip or rewrite the cython wrapper.
cython is still available on hip via execution_backend='cython' for
anyone who explicitly wants it.
* [Test] Gate CUDA-only language tests with requires_cuda
Three language tests hardcode target='cuda' (or rely on CUDA-only
codegen behavior) but lacked a CUDA availability marker, so they failed
on the ROCm CI runner with "Cannot find global function
target.build.tilelang_cuda" once the earlier pre-existing failures got
past --maxfail=3. Add @tilelang.testing.requires_cuda to:
- test_tilelang_if_range
- test_annotate_min_blocks_per_sm_launch_bounds
- test_tilelang_transpose and test_tilelang_transpose_square
Also replace the hand-rolled __main__ block in test_tilelang_transpose
with tilelang.testing.main() so the skip markers are honored when the
file is run directly.
* [Test] Delete tile library gemm test
Remove testing/python/tilelibrary/test_tilelang_tilelibrary_gemm.py.
|
||
|
Lei WangandClaude Opus 4.6
|
853e805b2a |
[Codegen] Add lexical_alloc_scope for scoped local variable lifetime (#2023)
* [Codegen] Add lexical_alloc_scope for scoped local variable lifetime
Introduce a `lexical_alloc_scope` AttrStmt that generates `{ ... }` in
C/CUDA codegen, giving the underlying compiler accurate variable lifetime
information for better register allocation.
- Define `tl::attr::kLexicalAllocScope` constant
- LowerOpaqueBlock wraps block-local allocations in the new AttrStmt
- StorageRewrite treats it as a scope boundary with proper thread_scope_
save/restore so allocations are not hoisted past the boundary
- CUDA and HIP codegen emit scoped `{ }` blocks
- Add tests for IR insertion, StorageRewrite preservation, and codegen output
* lint fix
* [Codegen] Refine lexical_alloc_scope: skip top-level blocks and decouple from thread_scope
Two improvements to the lexical_alloc_scope mechanism:
1. LowerOpaqueBlock: only insert lexical_alloc_scope for blocks inside
loops (inside_loop_ > 0). Top-level blocks already have function-body
lifetime, so the extra `{ }` in codegen is pointless.
2. StorageRewrite: introduce a separate `lexical_scope_` / `effective_scope()`
instead of overriding `thread_scope_`. This avoids breaking
PlanNewScope's toggle protocol when lexical_alloc_scope is nested
inside thread_extent, fixing a `ICHECK(thread_scope_ == op)` crash.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [Transform] Fix alloc-scope placement regressions
* [Infra] Remove clang-tidy integration
* Remove unused local descriptor allocation pass and related tests; update example to disable main execution and print kernel source for debugging.
* Preserve lexical alloc scopes for nested register buffers
* Limit lexical alloc scopes to local storage
* refactor
* Unify lexical alloc scope annotations
* Clean up dead code and fix unsafe cast
- Remove unused block_nesting_ member from OpaqueBlockLower
- Use static_cast instead of reinterpret_cast in ResolveAllocationSite
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
|
||
|
Lei WangandClaude Opus 4.6
|
3ee0988c86 |
[Refactor][Pipeline] Run pipeline rewriting before layout inference and stabilize tiled WS (#2002)
* Refactor optimization pipeline in phase.py to streamline barrier handling
- Consolidated the handling of shared barriers and pipeline planning by removing redundant conditional checks.
- Ensured that `LowerSharedBarrier`, `PipelinePlanning`, and `InjectSoftwarePipeline` are consistently applied, enhancing the clarity and efficiency of the optimization process.
This change improves the maintainability of the code while preserving existing functionality.
* Enhance tile operation handling with instruction annotation and refactor gemm.h
- Added a new `InstructionAnnotation` pass to annotate tile operations with their instruction kind before layout inference, improving the optimization pipeline's ability to reason about instruction mixes.
- Refactored `gemm.h` to move the `allowTcgen5Mma` and `allowWgmma` methods under the private section, enhancing code organization and encapsulation.
These changes improve the clarity and maintainability of the code while preserving existing functionality.
* Enhance multi-version buffer handling and introduce tile-level warp specialization
- Updated `multi_version_buffer_rewriter.cc` to improve read/write access detection for tile operations by analyzing `tl.tileop.region` calls, ensuring accurate buffer access tracking.
- Modified `phase.py` to integrate `ProducerConsumerWarpSpecializedTiled` before layout inference, allowing for high-level tile-op IR transformations that enhance producer/consumer splits.
- Added a new `ProducerConsumerWarpSpecializedTiled` function in `__init__.py` to facilitate tile-level warp specialization, improving the optimization pipeline's efficiency.
These changes enhance the handling of multi-version buffers and optimize the transformation process for tiled operations.
* Enable tiled WS for stage-1 pipelines
* Move Hopper pipeline planning before layout inference
* Run pipeline rewriting before layout inference
* Add pipeline refactor WIP: subtree_modified_ guard, debug prints, and plan
- inject_pipeline.cc: guard reads/writes recalculation with subtree_modified_
flag to prevent local.var buffer promotion to kernel parameters
- phase.py: add temporary debug prints after PipelinePlanning and
InjectSoftwarePipeline (to be removed during CI fix work)
- example_group_per_split_token_cast_to_fp8.py: add disable_cache for debugging
- docs/plan.md: implementation plan for fixing CI test failures
- draft.md: original design draft
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Remove debug artifacts: prints in phase.py, disable_cache in example
- Remove print("After PipelinePlanning"), print(mod),
print("After InjectSoftwarePipeline"), print(mod) from
LowerAndLegalize in tilelang/engine/phase.py
- Remove tilelang.disable_cache() from
examples/cast/example_group_per_split_token_cast_to_fp8.py
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix LayoutInference crash on multi-versioned pipeline buffers
After InjectSoftwarePipeline, multi-versioned buffers share the same
data Var as the original but have an extra leading dimension (num_stages).
LayoutInference's alias propagation and annotation handling tried to
Reshape layouts between these buffers, which failed because the total
element counts differ.
Guard three Reshape call sites in layout_inference.cc to skip sibling
buffers whose total storage size is incompatible with the source layout.
This lets multi-versioned buffers get their own layout inference instead
of inheriting an incompatible layout from the original buffer.
Fixes compilation failures in dequantize_gemm, GDN, and other kernels
that use software pipelining with shared memory buffers.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Strip pipeline annotations on WS fallback for TMA kernels
When ProducerConsumerWarpSpecializedTiled identifies a TMA kernel as a
warp-specialization candidate but the tiled rewriter cannot handle it
(e.g., conditional loop bodies like sparse block masks), the fallback
previously returned the original function with num_stages annotations
intact. PipelinePlanning and InjectSoftwarePipeline would then generate
non-WS TMA pipeline code with broken barrier phase tracking for
conditional pipeline bodies (barrier waits outside conditionals cause
deadlocks when the condition is false).
Fix: on WS fallback, strip num_stages annotations from pipeline loops
so that the pipeline passes skip the function. The kernel runs
unpipelined but correctly.
Fixes CUDA_ERROR_LAUNCH_FAILED in blocksparse_gemm and related TMA
kernels with conditional loop bodies.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Add guarded phase-counter support to tiled WS pass
Port PhaseCounter and StageExprReplacer from the legacy
ProducerConsumerWarpSpecialized pass into the tiled WS pass to handle
conditional loop bodies (e.g., sparse block masks).
When the pipeline loop body is wrapped in an IfThenElse without else:
1. Unwrap the condition before classifying statements
2. Create separate producer/consumer PhaseCounters (local int32 buffers)
3. Use counter-based stage/parity expressions instead of loop-variable
4. Wrap producer and consumer bodies in the original condition
5. Increment counters at end of each guarded iteration
6. Rewrite shared-buffer stage indices via StageExprReplacer
This ensures barrier parity stays correct when iterations are
conditionally skipped, fixing CUDA_ERROR_LAUNCH_FAILED in
blocksparse_gemm and related TMA kernels with conditional execution.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Add GemmSPNode support to pipeline planning and injection
Add GemmSPNode handling to:
- inject_pipeline.cc AddReadsWritesForTileOp: model A, E, B as reads
and C as write (E is the sparse metadata buffer)
- pipeline_planning.cc: same access model for dependency analysis
This makes sparse GEMM visible to the pipeline machinery for correct
stage assignment and buffer multi-versioning. However, the tile-op's
consumer-side buffer accesses still don't get stage-indexed because
the pipeline body rewriter can't rewrite high-level tile-op Call
arguments (an architectural limitation of running InjectSoftwarePipeline
before LowerTileOp).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix GemmSPNode::Lower to use MakeAccessPtrFromRegion
Replace whole-buffer access_ptr(1) calls with MakeAccessPtrFromRegion
for A, B, C, and E buffers in sparse GEMM lowering. This preserves
stage-specific region offsets from pipeline multi-versioning, matching
the dense GemmNode::Lower pattern.
CUDA output now shows correct stage-indexed consumer accesses:
gemm_sp_ss(..., (k%3)*8192, (k%3)*8192+27648, C_local, (k%3)*2048+49152)
instead of always using stage-0 offsets.
Note: gemm_sp still produces incorrect results because the kernel needs
warp specialization but TiledWSCandidate::Check doesn't recognize it
as a TMA candidate. The non-WS pipeline path generates structurally
different code from the reference WS path.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix gemm_sp and seer_attention regressions
Three changes in producer_consumer_ws_tiled.cc:
1. Require num_stages >= 2 for WS candidacy (was >= 1). Single-stage
kernels like seer_attention don't need WS and the transformation
produces incorrect results for them.
2. Add HasTmaPipeline() check to detect TMA kernels with pipeline
annotations that are rejected by the full WS candidate check (e.g.,
kernels with manual layout annotations like gemm_sp).
3. Strip num_stages annotations for rejected TMA pipeline kernels to
prevent InjectSoftwarePipeline from generating broken non-WS TMA
pipeline code.
One change in gemm_sp.cc:
- Use MakeAccessPtrFromRegion for A, B, C, E buffer access pointers
instead of whole-buffer access_ptr. This preserves stage-specific
region offsets from pipeline multi-versioning.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix WS conditional body unwrapping for complex loop bodies
Only unwrap IfThenElse wrapper when the then-branch is a simple flat
sequence of tile-op Evaluate calls. Skip unwrapping for complex bodies
with LetStmt, For, or other control flow that could break variable
scoping when split into producer/consumer for WS.
Fixes variable-used-before-definition error in
blocksparse_attention sparse_gqa_decode_varlen_indice, which has a
conditional loop body containing LetStmt bindings.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Revert num_stages >= 2 to >= 1 for WS candidacy
The >= 2 threshold broke test_num_stages_one_pure_tma_keeps_auto_warp_specialize.
Pure TMA kernels with num_stages=1 should still be WS candidates.
The seer_attention issue (num_stages=1 with manual layout) is handled
by the has_manual_layout_ check, not the num_stages threshold.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Propagate manual layouts onto MVB-expanded buffers via Layout::Expand
When LayoutInference encounters an MVB-expanded buffer (with leading
stage dimensions) whose trailing dimensions match the original layout,
use Layout::Expand to propagate the manual layout instead of rejecting
or skipping the buffer. Applied to all 3 layout propagation paths:
annotated layout map, alias propagation, and finalization.
Also remove the blanket !has_manual_layout_ WS candidate rejection
since manual layouts now survive onto versioned shared buffers via
Layout::Expand.
Fixes test_sparse_ws_regular_metadata_copy_stays_in_producer.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Extend conditional body unwrapping for LetStmt chains and restore
has_manual_layout_ guard
1. LetStmt chain peeling: when IfThenElse then_case starts with
LetStmt bindings, peel them and append to let_bindings before
checking the simple-body guard. This allows WS for conditional
bodies with variable definitions (e.g., sparse attention patterns).
2. Restore !has_manual_layout_ in WS candidacy check: removing it
caused dequant_groupedgemm_bf16_mxfp4_hopper to fail because
MXFP4 layouts don't survive MVB expansion. The Layout::Expand fix
handles sparse metadata layouts but not all manual layout types.
3. Layout::Expand propagation (from Round 6) remains in place for
future use when MVB learns to handle all manual layout types.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Remove is_simple_body guard from conditional WS unwrapping
The simple-body guard only accepted flat Evaluate sequences inside
IfThenElse, blocking legitimate WS for complex conditional bodies
like sparse flash attention (T.clear, T.reduce_max, T.gemm inside
the guard). The LetStmt peeling already handles variable scoping.
Fixes test_pure_tma_consumer_local_init_does_not_leak_into_producer
and test_sparse_ws_regular_metadata_copy_stays_in_producer.
The remaining test_mixed_tma_cp_async_shared_stage_barriers failure
is a pre-existing issue on the original branch: the tiled WS pass
produces SIMT copies instead of cp.async because LowerPTXAsyncCopy
is not in the pass pipeline (the comment at phase.py:308 says it
runs earlier but no actual call exists on either branch or reference).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Preserve pipeline context for WS producer cp.async injection
Wrap the WS producer loop body in a kPipelineContextNumStages AttrStmt
so that LowerTileOp's pipelined_depth_ is > 0 when processing SIMT
producer copies. This enables InjectPTXAsyncCopy to generate cp.async
for global-to-shared copies in the WS producer branch.
Without this, the WS rewriter strips all pipeline annotations from
the rewritten loops, causing LowerTileOp to skip cp.async injection
for SIMT producers. The consumer loop stays annotation-free since it
doesn't need cp.async.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix mixed barrier protocol, consumer-init sinking, restore manual-layout guard
1. Mixed TMA+cp.async barrier: use ptx_cp_async_barrier_noinc for
forward barrier arrival in mixed producer groups, matching the
reference producer_consumer_ws.cc protocol.
2. Consumer-only pre-loop init sinking: in ReplacePipelineLoopInStmt,
guard pre-loop siblings as consumer-only when they're not classified
as producer (TMA/SIMT/cp.async). Fragment init (T.fill, T.clear)
and local buffer init are placed in the consumer branch instead of
the shared prelude.
3. Restored blanket has_manual_layout_ guard: the dtype-based heuristic
to distinguish sparse metadata from MXFP4 layouts doesn't work
because both use uint8. Dequant_groupedgemm_bf16_mxfp4_hopper
requires the guard to prevent broken WS.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Narrow consumer-init sinking, targeted manual-layout check, per-group barrier
1. Consumer-init sinking: only sink pre-loop stmts that are FillNode
writing to fragment/local buffers. Keep block_mask setup and shared
state in the shared prelude.
2. Manual-layout: attempt targeted check that only rejects when TMA
copy destinations match layout_map entries. Collect layout_map vars
and compare against TMA copy destinations.
3. Per-group cp.async barrier: use group-level cp.async flag (single
group for now) instead of function-wide boolean.
The 3 WS issue tests still fail because the layout_map annotation
parsing falls through to the conservative rejection path.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix layout_map parsing with Var→Buffer mapping for manual-layout check
The layout_map annotation uses Map<Var, Layout> before LayoutInference
(not Map<Buffer, Layout>). Parse as Map<ObjectRef, ObjectRef> and
handle both key types: Buffer (post-inference) and Var (pre-inference).
For Var keys, look up the corresponding alloc_buffer by data Var match.
Compare collected manual-layout buffers against pipeline copy
destinations: reject only when a manually-laid-out buffer is also
a producer copy target (TMA/SIMT/cp.async) inside the pipeline.
This allows sparse metadata (E_shared, SIMT-copied) onto the WS path
while rejecting dequant MXFP4 (B_shared, SIMT-copied with swizzle).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix SEGFAULT in manual-layout check, use DetectSwizzleMode
1. Fixed SEGFAULT: removed dangling layout_map_layouts_ vector that
was never populated, causing OOB access. Now stores Buffer+Layout
pairs in layout_map_entries_.
2. Use DetectSwizzleMode to distinguish swizzled layouts (MXFP4,
incompatible with MVB) from non-swizzled (sparse metadata, safe).
Swizzled layouts reject WS candidacy; non-swizzled layouts allow it.
3. Removed debug LOG(WARNING) from hot path.
4. Parse layout_map annotation keys as both Buffer and Var (via
Map<ObjectRef, ObjectRef>), resolving Var keys to alloc_buffers.
Results: sparse metadata WS test PASSES, dequant PASSES (protected).
Down to 2 failures: mixed barrier pattern + consumer init sinking.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Simplify consumer-init sinking to Evaluate-only classification
Sink pre-loop Evaluate nodes classified as kConsumer into consumer
branch. Keep For loops (block_mask setup), producer copies, and other
control flow in the shared prelude. This is simpler and safer than
the FillNode scope check approach.
The test_pure_tma_consumer_local_init test still fails because the
T.fill statements are at a different structural level than the
SeqStmt where the pipeline loop lives. Fixing this requires deeper
IR structure analysis.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Sink consumer-only pre-loop init into WS consumer branch
Extract consumer-only pre-loop Evaluate statements (T.fill on
fragments) from the shared prelude and prepend them to the consumer
branch inside the WS if/else structure. This ensures fragment init
like acc_o, logsum, scores_max appears only in the consumer branch,
not in the shared prelude or producer branch.
Uses a two-pass approach: first ReplacePipelineLoopInStmt extracts
consumer-only stmts into extracted_consumer_init_, then the WS body
is rebuilt with the extracted stmts prepended to the consumer branch.
Fixes test_pure_tma_consumer_local_init_does_not_leak_into_producer.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Finalize pipeline refactor fixes
* Refactor CopyNode stride checks for TMA bulk load/store
- Extracted the global stride validation logic into a new static method `CheckGlobalStrides` for better readability and reusability.
- Updated `CheckBulkLoad` and `CheckBulkStore` methods to utilize the new stride checking function, improving code clarity and maintainability.
- Enhanced documentation for the new method to clarify its purpose and requirements.
* Remove redundant copy direction notes from documentation in `copy.h` for stride checks. This simplifies the comments while maintaining clarity on TMA requirements.
* refactor
* layout related fix
* refactor mbarrier with software pipeline.
* remove legacy pass
* Enhance TMA barrier handling and merging logic
- Updated `CopyToTmaCopyRewriter` to conditionally emit arrive barriers based on the last TMA copy.
- Refactored barrier creation to allow merging of TMA barriers when conditions are met, reducing the number of barriers needed.
- Adjusted consumer and producer logic to accommodate merged barriers, ensuring correct synchronization across TMA copies.
- Improved documentation and comments for clarity on the new barrier merging behavior.
* Update example_mhc_post.py to disable main execution and add test function call
- Commented out the main function call in `example_mhc_post.py`.
- Added a call to `tilelang.disable_cache()` and a new `test(n=4096, h=2560)` function for testing purposes.
* Enhance pipeline barrier handling and TMA copy detection
- Updated `RewritePipelineTmaBarriers` to accept additional parameters for loop variable and stage count, improving barrier synchronization.
- Modified `HandleTileOp` to ensure only user-defined T.copy patterns are marked for pipeline barriers, preventing interference with T.tma_copy.
- Adjusted test cases to enforce specific CUDA compute version requirements for better compatibility and consistency.
* lint fix
* Enhance convolution example and TMA handling
- Added kernel source printing in `example_convolution.py` for debugging.
- Disabled the main function call and invoked `run_regression_perf()` to streamline execution.
- Introduced `MakeTmaLeaderCondition` function in `copy.cc` to improve TMA leader-thread condition handling.
- Updated `LowerBulkCopy` and `LowerBulkCopy1D` methods to utilize the new TMA leader condition for better thread management.
- Enhanced `Conv2DIm2ColOpNode` to support barrier annotations, improving synchronization in TMA operations.
* enhance
* Remove example_dequant_groupedgemm_bf16_mxfp4_hopper.py and clean up phase.py by removing unnecessary whitespace. Enhance TMA handling in producer_consumer_ws_tiled.cc to improve barrier synchronization and streamline TMA copy operations.
* fix
* fix
* Eliminate MVB(barrier_only=true) late fixup from OptimizeForTarget
Move pipeline barrier ownership into InjectSoftwarePipeline: create
pipeline_mbar[num_stages] at final expanded size instead of
pipeline_mbar[1] that required late MVB expansion.
Key changes:
- RewritePipelineTmaBarriers creates barriers at num_stages size
- Barrier indices use FloorMod(loop_var - loop_min, num_stages)
- barrier_init has num_stages entries (one per slot, arrive_count=1)
- CopyToTmaCopyRewriter accepts PrimExpr barrier_id (was int)
- phase.py: remove MVB(barrier_only=True), unify both paths under
PlanAndUpdateBufferAllocationLocation
- Fix pre-existing test expectation for tma_copies annotation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix Codex review issues: num_stages=1 regression, im2col pipeline barriers, test fixes
1. Fix num_stages=1 regression: use tl_pipelined_num_stages annotation
for barrier sizing instead of max_stage+1. Gate barrier creation on
pipeline_depth > 1 so num_stages=1 kernels don't get multi-versioned
pipeline barriers.
2. Extend RewritePipelineTmaBarriers to handle c2d_im2col: annotate
im2col calls with pipeline barrier in CopyToTmaCopyRewriter. Add
im2col TMA recognition to PipelinePlanning. Fix im2col Lower() to
respect emit_arrive annotation from pipeline barriers.
3. Fix test_simple_pipeline: use annotation-checking approach instead of
structural equality with hardcoded tma_copies annotation that fails
on non-Hopper targets.
4. Remove dead kPipelineMVBStageExpr/kPipelineMVBParityExpr/
kPipelineMVBContextNumStages emission from inject_pipeline.cc since
pipeline barriers are now created at final size.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix depth-1 barrier ownership, add regression tests, remove dead kPipelineMVB*
1. Fix depth-1 ownership gap: always create shared pipeline barrier for
TMA copies even when pipeline_depth=1. This prevents LowerTileOp from
allocating separate per-copy internal barriers, keeping num_stages=1
kernels at pipeline_mbar[1] (single slot).
2. Add checked-in lowering regression tests:
- non-WS num_stages=3 TMA GEMM → asserts pipeline_mbar[3], no fallback
- non-WS num_stages=1 TMA GEMM → asserts pipeline_mbar[1], no multi-slot
- non-WS num_stages=3 im2col → asserts pipeline_mbar[3] feeds tma_load_im2col
3. Remove dead kPipelineMVB* definitions from pipeline_utils.h and
consumption code from multi_version_buffer_rewriter.cc (stacks,
explicit parity/version index logic, attr stripping).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix GemmWMMA.lower() signature and autotuner cache backwards compatibility
[P1] Add missing mbar_phase_expr parameter to GemmWMMA.lower() to match
the interface expected by GemmPy.lower() dispatch. Without this, any
RDNA kernel using the WMMA path would fail with TypeError.
[P2] Make out_idx.json loading optional in autotuner load_from_disk().
Older cache directories don't have this file; fallback to
compile_args.out_idx when the file is absent.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Add .humanize to gitignore
* Fix HIP swizzle codegen and CPU scalar GEMM region handling
[P2] Update codegen_hip.cc to parse tvm_tuple(device_func, panel_size)
format for threadblock_swizzle_pattern, matching the annotation format
now emitted by T.use_swizzle(). Previously expected StringImmNode which
would ICHECK-fail.
[P2] Fix gemm_scalar.py: clear only the output tile region (not the
whole buffer), and use the last two dimensions from regions to handle
rank>2 buffers with leading singleton dimensions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix HIP codegen to parse tvm_tuple swizzle annotation format
Update codegen_hip.cc threadblock_swizzle_pattern handler to parse
tvm_tuple(device_func, panel_size) format, matching CUDA and cutedsl
codegens. Previously expected StringImmNode which ICHECK-fails with
the current annotation format.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Restore kPipelineMVB* annotations — still needed by WS tiled full MVB
The kPipelineMVBStageExpr/ParityExpr/ContextNumStages annotations and
their MVB consumption code cannot be removed: they are emitted by
EmitImpl for ALL pipelines and consumed by the full MVB call inside
ProducerConsumerWarpSpecializedTiled. Removing them broke non-TMA
pipeline kernels like mhc_post that go through the WS path.
Restores: annotation emission in inject_pipeline.cc, constant
definitions in pipeline_utils.h, and consumption code in
multi_version_buffer_rewriter.cc.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Unify barrier multi-versioning in InjectSoftwarePipeline (Direction A)
Move all pipeline barrier multi-versioning into InjectSoftwarePipeline
via ExpandPipelineBarriers, eliminating the need for late
MVB(barrier_only=true) in OptimizeForTarget.
ExpandPipelineBarriers runs before BuildPipeline and handles:
- ISP-created pipeline_mbar (for non-WS TMA pipelines)
- User-written T.alloc_barrier (for manual WS pipelines like softpipe)
Key design: only barriers with explicit ptx_arrive_barrier calls OR
ISP-created local barriers are expanded. Barriers whose arrival is
managed internally by tile-ops (e.g., tcgen05 MMA arrive) are left
unchanged. This distinguishes pipeline sync barriers from hardware-
managed barriers.
Buffer expansion: barrier[N] -> barrier[N * num_stages]
Index rewriting: barrier[idx] -> barrier[stage_expr * N + idx]
Parity rewriting: user_parity -> (iteration_block + offset) % 2
barrier_init replication: [c0,c1] -> [c0,c1,c0,c1] for num_stages=2
Expanded buffers propagate to outer blocks via pending_buffer_remap_.
barrier_init annotations are replicated in VisitStmt_(BlockNode).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix Python 3.9 compat: replace star expression in index with tuple concat
* fix
* fix
* revert changes
* fix
* enhance pipeline
* enhance pipeline
* implement descriptor reuse pass
* Refactor pipeline management and enhance async copy handling
- Updated `example_mhc_pre.py` to replace the main function call with a test function for better testing flexibility.
- Modified `example_gqa_decode.py` to disable argument parsing and added latency measurement for regression performance.
- Enhanced `copy.cc` and `copy.h` with new checks for pipeline-managed cp.async synchronization and improved async copy handling.
- Updated `inject_pipeline.cc` and `pipeline_planning.cc` to refine the handling of tile operations and global/shared buffer checks.
- Added tests to ensure correct behavior of async pipeline and descriptor allocation reuse.
This commit improves the overall pipeline management and async copy capabilities, ensuring better performance and flexibility in the codebase.
* enhance pipeline
* enhance pipeline
* enhance pipeline
* enhance pipeline
* enhance pipeline
* Revert "enhance pipeline"
This reverts commit
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Lei Wang
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37c1c0c688 |
[codex] Reduce slow pytest runtime in testing/python (#2018)
Optimize bitwise reduce test runtime |
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Lei WangandZhiwen Mo
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cabdb86f25 |
[codex] Fuse packed x2 mul-add into fma2 in CUDA codegen (#2017)
* fuse packed x2 mul-add into fma2 * document packed x2 fma2 fusion rationale --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei Wang
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4f75940612 |
[BugFix] Enhance CUDA vectorization for binary operations (#2015)
Enhance CUDA vectorization for binary operations by supporting wider tensor dimensions. Update kernel generation to accommodate variable width, improving auto-vectorization for float32 types. Add tests for width-8 scenarios to ensure correct emission of packed operations. |
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6fc3afa2a3 |
[BugFix] Fix auto vectorization for binary operations after wider copy instructions (#1986)
* fix f32x2 vectorize for wider shape * clean code * fix: use string::operator+= to satisfy clang-tidy performance check Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Freebase6912 <amid-gauze-racing@duck.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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KurisandClaude Opus 4.6
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5f70374c48 |
[Test] Add 1D TMA regression test for issue #1842 (#2005)
Add a regression test covering 1D single-dimension tensor TMA copy (global -> shared -> global) with warp specialization disabled. The underlying bug was fixed in #1840, but the test suite only covered 2D descriptor-based TMA paths. This test ensures the 1D bulk copy path (cp.async.bulk) also works correctly with proper mbarrier allocation. Closes #1842 Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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KurisandClaude Opus 4.6
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e45ecf7dc4 |
[Feature] Support T.annotate_compile_flags, T.annotate_pass_configs, and out_idx as PrimFunc attrs (#2006)
Allow configuring pass configs, compile flags, and out_idx directly inside function bodies using T.annotate_compile_flags(), T.annotate_pass_configs(), and T.empty()+return. These are stored as proper PrimFunc attrs (tilelang_compile_flags, tilelang_pass_configs, tilelang_out_idx) instead of monkey-patching, and merged at compile time. Annotations can be placed before or after tensor type annotations. Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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WilliamandLeiWang1999
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a82fa719b8 |
fix: fix copy+cast vectorize loop to use wider vector load/store instrcution (#2004)
* fix copy+cast vectorize loop to use wider vector load/store instrcution * clean test * fix test * fix format * test fix --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
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KurisandClaude Opus 4.6
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8c3b043076 |
[Bugfix] Fix CuTeDSL autotune cache invalid ELF header (#1967) (#1972)
* [Bugfix] Fix CuTeDSL autotune cache saving .py as .so (#1967) The autotune cache had no CuTeDSL-specific branch, causing it to save the Python source file (kernel.py) as kernel_lib.so. On reload, importlib treated the .so extension as a native extension module and failed with "invalid ELF header". Fix: add cutedsl branches in _save_kernel_to_disk and _load_kernel_from_disk to use KERNEL_PY_PATH ("kernel.py") instead of KERNEL_LIB_PATH ("kernel_lib.so"). Also saves launcher .so and cubin artifacts when present. Closes #1967 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * [BugFix] Change _all_dtypes from set to list for deterministic order set has non-deterministic iteration order across processes, causing pytest-xdist workers to collect test parameters in different orders and fail with "Different tests were collected between gw3 and gw2". Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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Lei Wang
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1c561f6d46 |
[Bugfix] Raise error on zero grid dimension instead of silent clamp (#1994)
* [Bugfix] Raise error on zero grid dimension instead of silent clamp (#1993) Fix ThreadWorkLoad::Extract() silently clamping zero grid dims to 1, which caused either CUDA_ERROR_ILLEGAL_ADDRESS crashes (dynamic case) or silent wrong results (static case). Closes #1993 * lint fix |
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Lei WangandClaude Opus 4.6
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1ede0bd748 |
Introduce T.deallocate_tmem and T.transpose (#1971)
* Fix tcgen05 barrier allocation planning regression * Add explicit TMEM deallocation and shared transpose copy * Add transpose operation to documentation and update implementation in copy_op.py * LINT FIX * Refactor: extract transpose from CopyNode into standalone TransposeNode op Remove the transpose annotation logic from CopyNode (GetTranspose, MakeIndices transpose branch, MakePredicate transpose branch, and GetCopyInst early return). Transpose is now handled by the independent TransposeNode registered as tl.tileop.transpose. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Refactor CopyNode to remove transpose handling from index generation and predicate creation. Simplify MakeIndices and MakePredicate methods by eliminating unnecessary transpose checks and related logic. Update associated checks to ensure index consistency. Clean up unused GetTranspose method in copy.h. * lint fix --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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Lei Wang
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bdf436d4c3 |
[Feature] Support TMA store in T.tma_copy() (#1981)
* [Feature] Support TMA store in T.tma_copy() with user-managed synchronization T.tma_copy() previously only supported TMA loads (global -> shared) with user-managed barrier synchronization. This extends it to also support TMA stores (shared -> global), where: - The barrier parameter is now optional (not needed for stores) - For stores, only tma_store + tma_store_arrive is emitted (no wait) - Users call T.tma_store_wait() explicitly to synchronize * Refine tma_store_wait to accept count parameter and clean up builtin signatures - Wire tma_store_wait count parameter through the full stack (Python -> C++ builtin registration -> CUDA/CuTeDSL codegen) - Replace vague *args with proper signatures for fence_proxy_async() and tma_store_arrive() (both take no arguments) - Improve docstrings for internal TMA APIs (create_tma_descriptor, tma_load, tma_load_2sm) |
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KurisandClaude Opus 4.6
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dc60ab8f04 |
[Bugfix] Fix alloc_var re-bind warning when assigned with comparison ops (#1974)
* [Bugfix] Fix alloc_var re-bind warning when assigned with comparison ops EqualOp/NotEqualOp (returned by BufferLoad == / !=) are not PrimExpr subclasses, so `bind()` missed the alloc_var store path and fell through to `bind_immutable`, triggering a spurious "Immutable value is re-bound" warning. Convert them to PrimExpr via `.asobject()` before the store check. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Fix pre-commit lint: double quotes, remove redundant parens Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Fix ruff format Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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Chenhao Xu
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c31ff58a5f |
Unified packed x2 intrinsics with multi-dtype support and bug fixes (#1978)
* fix: bits product to have lanes included * fix: fix T.view to view non vectorized type to vectorized type with same total bytes * feat: bfloat16 vec type along with all vectorized ops * fix: fix tests |
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Tong WU
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88049820fe |
[Feature] Introduce annotation for minBlocksPerMultiprocessor in __launch_bounds__ (#1979)
* Add annotate_min_blocks_per_sm for __launch_bounds__(..., minBlocksPerSM) - Python: T.annotate_min_blocks_per_sm(n) sets tl.min_blocks_per_sm on the prim_func - Codegen: emit second launch_bounds argument from attr; default remains 1 when unset - Export from tilelang.language; document attr key in builtin.h Made-with: Cursor * test * lint |
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Lei Wang
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e2c88335c6 |
Fix wrapped pre-loop TMA prefixes in producer-consumer WS (#1975)
* Support wrapped pre-loop TMA prefixes in producer-consumer WS * Format producer-consumer WS changes * Keep pure-TMA barrier grouping unchanged |
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a2d6e01866 |
[Bugfix] Fix double buffer versioning when TMA is used without warp specialization (#1962)
* [Bugfix] Fix double buffer versioning when TMA is used without warp specialization When TMA is enabled but warp specialization is disabled, both MultiVersionBuffer and InjectSoftwarePipeline independently added version dimensions to shared data buffers, resulting in 4x buffer allocation instead of the intended 2x. Add a `barrier_only` mode to MultiVersionBuffer so that in the no-WS path it only versions barrier buffers (for mbarrier parity rewriting), while data buffer versioning is left to InjectSoftwarePipeline. Also flatten the if/else branches in OptimizeForTarget into a single linear flow with a three-way branch (WS / TMA-noWS / noTMA), removing the duplicated LowerOpaqueBlock call. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * [Enhancement] Introduce TMA attribute handling in LowerTileOp and optimization pipeline - Added a new PrimFunc attribute `tl.has_tma` to indicate whether TMA operations were generated during the lowering process. - Updated `OptimizeForTarget` to utilize the `tl.has_tma` attribute for determining the appropriate optimization path, enhancing clarity and efficiency in the pipeline. - Refactored the `module_has_tma` function to check for TMA operations across all functions in the module, streamlining the decision-making process for TMA-related optimizations. This change improves the maintainability of the code and ensures that TMA operations are correctly accounted for in the optimization pipeline. * fix * remove barrier related test for cutedsl. --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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Chaofan Lin
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d0090b428e |
[Feature] Support alloc global workspace (#1940)
* add docstring * add hoist global allocation pass * fix rebase * fix * fix tvm * fix |
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Lei WangandZhiwen Mo
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715ca303da |
[Bugfix] Handle int64 offsets in ThreadSync for tvm_access_ptr (#1952)
Updated the ThreadSync implementation to support int64 offsets in tvm_access_ptr, preventing crashes during index reconstruction. Added a regression test to ensure correct behavior with shared/shared.dyn pointers. Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei Wang
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b5bd642479 |
[Refactor] Separate gemm into explicit wgmma_gemm and tcgen05_gemm functions (#1949)
* Refactor GEMM operations to use explicit WGMMA and TCGEN5MMA functions - Replaced instances of T.gemm with T.wgmma_gemm and T.tcgen05_gemm in various example scripts to enhance performance and control over asynchronous execution. - Updated related documentation to reflect the changes in GEMM function usage, ensuring clarity on the new explicit interfaces. - Removed unnecessary wg_wait parameters from GEMM calls, aligning with the new function signatures. This refactor improves the efficiency of matrix multiplication operations in the TileLang framework, particularly for Hopper and Blackwell architectures. * lint fix * Refactor GEMM operations to unify function signatures - Replaced deprecated GEMM function variants (gemm_v1, gemm_v2, tcgen05_gemm_v1, tcgen05_gemm_v2) with a single unified function (gemm, tcgen05_gemm) across examples and tests. - Updated test cases to reflect the new function signatures, ensuring consistency and clarity in GEMM operation usage. - This change simplifies the API and enhances maintainability within the TileLang framework. * Refactor GEMM initialization to improve annotation checks - Simplified the handling of "is_wgmma" and "is_tcgen05" annotations by removing redundant checks and adding explicit error messages for type validation. - Enhanced the validation for the "mbar" argument to ensure it is a BufferLoadNode, improving error handling and code clarity. - These changes streamline the initialization process for GEMM operations, enhancing maintainability and robustness in the TileLang framework. |
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Lei WangandZhiwen Mo
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5a538f3b37 |
Support packed subtype views during layout reshape (#1947)
* Support packed subtype views in layout reshape * lint fix --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei WangandClaude Opus 4.6
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9246b82b24 |
[Refactor] Replace create_list_of_mbarrier with buffer-based T.alloc_barrier (#1944)
* [Refactor] Replace create_list_of_mbarrier/get_mbarrier with buffer-based T.alloc_barrier Unify the two mbarrier representation paths into a single buffer-based approach using shared.barrier scope buffers with barrier_init block annotations. This eliminates the create_list_of_mbarrier and get_mbarrier intermediate intrinsics, making the IR cleaner and the barrier handling consistent across user-explicit (T.alloc_barrier) and compiler-generated (T.copy/T.tma_copy) paths. Key changes: - LowerTileOp now injects a shared.barrier buffer + barrier_init annotation into the root block instead of emitting create_list_of_mbarrier statements - copy.cc uses BufferLoad on the barrier buffer instead of get_mbarrier - WarpSpecializedRewriter and ProducerConsumerWS create barrier buffers with annotations instead of create_list_of_mbarrier calls - LowerSharedBarrier runs a second time after WS passes to process newly created barriers - LowerHopperIntrin no longer handles mbarrier allocation/init (fully delegated to LowerSharedBarrier) - Removed create_list_of_mbarrier and get_mbarrier op definitions from builtin.h/cc - Removed get_mbarrier codegen handling from CUDA, CuTeDSL, HIP backends - Updated WarpSpecializedDetector, inject_tma_barrier, and eliminate_storage_sync passes to detect barriers via shared.barrier scope buffers instead of the removed intrinsics Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * style: apply clang-format and ruff-format Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Refactor mbarrier handling in copy operations Updated the handling of mbarriers in the `LowerBulkCopy` and `LowerBulkCopy1D` methods to allocate a single barrier slot instead of multiple for each pipeline stage. This change simplifies the barrier management by allowing the `MultiVersionBuffer` to expand the barrier for pipelining stages. Additionally, modified the `LowerArgs` structure to use a pointer for the `mbarrier_buffer`, ensuring compatibility with lazy buffer creation. Key changes: - Simplified mbarrier allocation logic in `copy.cc`. - Updated `LowerArgs` to use a pointer for `mbarrier_buffer`. - Adjusted related comments for clarity. This refactor enhances the consistency and readability of the code while maintaining functionality. * Remove deprecated TMA barrier and warp specialization files This commit deletes the `inject_tma_barrier.cc`, `warp_specialized_rewriter.cc`, and associated test files as part of a cleanup effort. These files are no longer necessary following recent refactoring and updates to the TMA pipeline and warp specialization logic. The removal simplifies the codebase and eliminates outdated implementations that have been superseded by newer approaches. Key changes: - Deleted `inject_tma_barrier.cc` and `warp_specialized_rewriter.cc`. - Removed related test file `test_tilelang_transform_warp_specialized.py`. - Updated `phase.py` and `__init__.py` to reflect the removal of the WarpSpecialized and InjectTmaBarrier functionalities. This cleanup enhances maintainability and clarity in the transformation pipeline. --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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Lei WangandZhiwen Mo
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f8dc61c02c |
test: reduce CI runtime for slow Python suites (#1932)
* test: trim CI runtime for slow Python suites * remove useless test * Update TVM submodule to latest commit and remove manual device assert test from CI. Replace with a call to tilelang.testing.main(). * lint fix * fix * revert test for eager jit --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei WangandClaude Opus 4.6
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ded6a99221 |
[Feature] Add Producer-Consumer Warp Specialization and T.tma_copy() API (#1909)
* [Feature] Add Producer-Consumer Warp Specialization and T.tma_copy() API
This PR introduces Producer-Consumer Warp Specialization for sm90+ TMA
pipelines and a new T.tma_copy() API for explicit mbarrier management.
Key changes:
1. **ProducerConsumerWarpSpecialized pass** (new):
Splits pipelined TMA loops into producer (TMA loads) and consumer
(compute) warp groups with back-pressure barriers for buffer reuse.
Works with num_stages >= 1. Producer warp (128 threads) handles
arrive_expect_tx + tma_load; consumer warps handle compute + arrive
on back-pressure barriers.
2. **T.tma_copy() API** (new):
Fire-and-forget TMA copy with a required `barrier` parameter. Unlike
T.copy() which emits producer+wait pairs, T.tma_copy() emits only
arrive_expect_tx + tma_load. User manages synchronization via
T.mbarrier_wait_parity().
3. **MultiVersionBuffer barrier expansion**:
Extended to handle `shared.barrier` scope buffers, expanding them for
pipelining (1D size multiplication instead of prepending a dimension).
Auto-computes mbarrier parity as `(k // num_stages) % 2`.
4. **Pass ordering** (phase.py):
LowerSharedBarrier now runs after MultiVersionBuffer in the TMA path
so barrier buffers retain their `shared.barrier` scope during expansion.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix for annotate reg alloc
* Enhance debugging and performance measurement in examples
- Added print statements to output kernel source for both forward and backward sparse MLA examples, aiding in debugging.
- Updated `sparse_mla_fwd_pipelined.py` to run performance regression tests and print average latency, improving performance tracking.
- Disabled auto cache in `example_mha_sink_fwd_bhsd.py` for better control during testing.
* fix
* Enhance debugging and performance tracking in examples
- Added print statements to output kernel source in various example scripts, including `example_gqa_sink_bwd_bhsd.py`, `example_blocksparse_gemm.py`, and `example_dequant_groupedgemm_bf16_mxfp4_hopper.py`, aiding in debugging.
- Updated default `window_size` in `example_gqa_sink_bwd_bhsd.py` for improved configuration.
- Disabled auto cache in `example_gemm_schedule.py` to enhance control during testing.
- Introduced a new test for preserving protected auto-injected wait groups in the async copy optimization process.
* clean
* Remove unnecessary print statements for kernel source in example scripts
- Eliminated print statements from `example_gqa_sink_bwd_bhsd.py`, `example_blocksparse_gemm.py`, and `example_dequant_groupedgemm_bf16_mxfp4_hopper.py` to clean up output and improve readability.
- These changes streamline the examples while maintaining functionality.
* clean
* lint fix
* Refactor T.tma_copy() and barrier synchronization in examples and tests
- Updated `example_warp_specialize_gemm_copy_1_gemm_0.py` to use `T.tma_copy()` for optimized memory copying with explicit barrier management.
- Enhanced test cases in `test_tilelang_issue_tma_no_ws.py` and `test_tilelang_language_tma_copy.py` to reflect changes in barrier synchronization, ensuring proper functionality with the new T.tma_copy() API.
- Removed unnecessary print statements and adjusted barrier allocations for clarity and performance.
- Cleaned up the `test_tilelang_transform_inject_tma_barrier.py` file by deleting it, as it was no longer needed.
* Add buffer data mapping for remapped buffers in multi_version_buffer_rewriter
- Introduced a check to ensure that the data variable of a buffer is discoverable, allowing the barrier_init annotation update to find the remapped buffer correctly.
- This change enhances the functionality of the buffer remapping process, ensuring proper synchronization in the transformation pipeline.
* Implement TMA im2col load synchronization with mbarrier allocation
- Added support for allocating mbarriers to synchronize TMA im2col loads, enhancing the pipeline's barrier management.
- Introduced logic to handle mbarrier creation and integration into the TMA copy process, ensuring proper synchronization and data transfer.
- Updated the handling of the mbarrier in the TMA copy statement to improve performance and maintain functionality within the thread-gated block.
* Implement TMA im2col load synchronization with mbarrier allocation
- Added support for allocating mbarriers to synchronize TMA im2col loads, enhancing the pipeline's barrier management.
- Introduced logic to handle mbarrier initialization and integration within the TMA copy process, ensuring proper synchronization during memory operations.
- Updated the handling of the mbarrier in the im2col operation to improve performance and maintain consistency in multi-threaded environments.
* disable tma lower for test_cutedsl_barrier
* Refactor code formatting in test_tilelang_jit_cutedsl.py
- Improved readability by adjusting the formatting of the `tilelang.compile` function call.
- Enabled the main testing function in the script to run without being commented out.
* Enhance sparse_mla_fwd.py and thread_storage_sync.cc with code cleanup and new functionality
- Added a call to disable cache in the main execution block of `sparse_mla_fwd.py` for improved performance.
- Commented out unused benchmarking code in `test_sparse_mla_fwd` to streamline testing.
- Introduced new utility functions in `thread_storage_sync.cc` to analyze and optimize shared memory access patterns, enhancing synchronization management.
- Improved readability and organization of the code by restructuring and commenting on key sections.
* Add thread storage synchronization utilities and optimizations
- Introduced functions to identify shared-like scopes, buffers, and pointer variables, enhancing memory management.
- Implemented a `BarrierTransparentStmtAnalyzer` to analyze statement transparency concerning shared memory.
- Developed a `ThreadStorageSyncOptimizer` to optimize sequences of statements by removing unnecessary synchronization calls, improving performance in multi-threaded environments.
- Enhanced handling of shared memory access in various call types, ensuring proper synchronization during memory operations.
* remove optimize thread_storage_sync
* Refactor pipeline planning to enhance cp.async group scheduling
- Introduced a last-use index for cp.async groups to improve scheduling accuracy.
- Removed unnecessary tracking of first consumers, simplifying the scheduling logic.
- Updated the handling of cp.async and commit statements to ensure proper group ordering based on last-use.
- Added a new test to verify that cp.async groups are ordered correctly by their last-use index.
* lint fix
* Add CUDA requirements to TMA copy tests and update main execution call
- Added decorators to require CUDA and a minimum compute version for the `test_tma_copy_pipeline_2_stages` and `test_tma_copy_pipeline_3_stages` functions.
- Updated the main execution block to call `tilelang.testing.main()` instead of directly invoking the test functions, improving test execution management.
* Refactor attention sink examples by removing pipelined implementations
- Updated imports in `benchmark_gqa_sink_fwd.py` and `benchmark_mha_sink_fwd.py` to use the non-pipelined versions of the attention sink functions.
- Deleted the pipelined implementations of `example_gqa_sink_fwd_bhsd_wgmma_pipelined.py` and `example_mha_sink_fwd_bhsd_wgmma_pipelined.py`.
- Cleaned up regression and test files to remove references to the deleted pipelined examples, ensuring consistency across the codebase.
* performance fix
* Enhance example_blocksparse_gemm.py and inject_pipeline.cc with debugging and utility improvements
- Added a print statement to output the kernel source in `example_blocksparse_gemm.py` for debugging purposes.
- Updated `inject_pipeline.cc` to include a new utility function for stripping TMA copy write buffer attributes, improving code clarity and functionality.
- Modified `producer_consumer_ws.cc` to utilize the new utility function, enhancing the handling of TMA barriers.
- Expanded the `_compile_tvm_ffi` function in the test file to accept additional keyword arguments, improving flexibility for test configurations.
- Introduced a new test to verify that pure TMA warp specialization correctly emits barriers, ensuring compliance with expected behavior.
* lint fix
* Refactor guard variable declarations in producer_consumer_ws.cc for clarity
- Changed guard variable declarations from non-const to const references to improve performance and readability.
- This minor adjustment enhances the handling of producer guards in the context of TMA barriers.
* lint fix
* fix
* lint fix
* perf fix
* Add print statement for kernel source and update main execution in example_dequant_gemm_w4a8.py
- Introduced a print statement to output the kernel source for debugging purposes.
- Updated the main execution block to call `run_regression_perf()` and print the latency, enhancing performance measurement capabilities.
* fix
* Add debugging output and update execution flow in example_tilelang_block_sparse_attn.py
- Added a print statement to display the kernel source for better debugging.
- Updated the main execution block to disable cache and call `run_regression_perf()`, printing the resulting latency for performance measurement.
- Enhanced the producer-consumer logic in producer_consumer_ws.cc with new utility functions to manage local variable access and improve code clarity.
- Introduced a new test case in test_tilelang_issue_tma_no_ws.py to ensure consumer-only local initializations do not leak into the producer, verifying correct behavior in TMA contexts.
* Revert "Add debugging output and update execution flow in example_tilelang_block_sparse_attn.py"
This reverts commit
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Lei WangandZhiwen Mo
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fee041c3cd |
Fix predicated cp.async pipeline scheduling (#1937)
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei WangandZhiwen Mo
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8fe367c81f |
[Runtime] Improve TMA descriptor diagnostics (#1931)
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Lei WangandZhiwen Mo
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d338c4ff95 |
[Bugfix] Fix concurrent TempDirectory creation during CUDA compilation (#1926)
* [Refactor] Update TVM subproject and enhance CUDA artifact path resolution - Updated the TVM subproject to commit 470430cf. - Introduced a new helper function `_resolve_artifact_paths` in `nvcc.py` to streamline the resolution of CUDA artifact paths, improving code clarity and maintainability. - Refactored the `compile_cuda` function to utilize the new helper for managing temporary file paths based on the specified output directory. * [Enhancement] Add CUDA requirement decorator to matmul test - Introduced the `@tilelang.testing.requires_cuda` decorator to the `test_matmul` function, ensuring that the test is only executed in environments with CUDA support. This change improves test reliability and clarity regarding dependencies. * lint fix --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Liu Yunuo
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7b6b64f93e |
[Enhancement] Add eager-mode support for tilelang.autotune (#1906)
* [Enhancement] Add eager-mode support for tilelang.autotune * Fix type annotation and improve code structure in tuner.py * Fix misleading docstring in autotune tests |