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Regression for caffe opencl branch. #4
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Sorry for the delay. Right now, ISAAC's test suite uses prime numbers for M, N and K, but caffe calls GEMM on corner cases (M=1, K=1 or N=1). This has caused some issues in the past. I'll add tests for corner cases ASAP so I can pinpoint the problem. The only hardware I can test this on is Broadwell 5500U iGPU. What hardware have you been using. I've not been using Beignet; I'll try this too. Thanks for the report! |
@ptillet Broadwell 5500U is good to reproduce this issue, although the performance is not as good as the OpenCL SDK. But the test suite pass rate is very good, please use the git master beignet. Thanks for your support! |
GEMM-xT fails for M=1. That's probably the cause of all your issues. I'm on it :) |
@gongzg I have just pushed two small fixes for BLAS, for an uninitialized variable in BLAS-2 and the BLAS3 failure when M==1. Does it help? |
@ptillet the crash issue is fixed, but the test failures still exist. |
I have fixed more dot() corner cases in dbfaef8. There were still some problems left for the cases where SGEMV degenerates to SDOT or SAXPY... Hopefully this should fix some more issues with caffe. |
@ptillet There are still many failures. I choose one case as below: build/test/test.testbin --gtest_filter=NetTest/2.TestSharedWeightsResumeIt fails with the latest ISAAC. And if I choose viennacl's GEMM and the other math functions still use ISAAC, it could pass. For your reference. Thanks. |
I've fixed some bugs in the master branch. Now all the OpenCL caffe tests pass on my machine. |
@ptillet I dig into the reduce_2d::generate_impl and found there is a barrier issue. element_wise_loop_1D(stream, p_.fetch_policy, (reduction_type_==REDUCE_ROWS)?p_.simd_width:1, "r", upper.str(), "$GLOBAL_IDX_1", "$GLOBAL_SIZE_1", device, [&](unsigned int cwidth) You can see the LOCAL_BARRIER is within the loop, and one example of the loop head is as below: Description If barrier is inside a conditional statement, then all work-items must enter the conditional if any work-item enters the conditional statement and executes the barrier. If barrier is inside a loop, all work-items must execute the barrier for each iteration of the loop before any are allowed to continue execution beyond the barrier. |
Thanks for investigating the generated kernels! The latest commit did introduce a synchronization issue not caught by the unit tests (dammit!). I'll try to fix this tonight. For reference, here is ISAAC normally handles this issue: The upper-bound on the outer loop for r should be rounded up to the next multiple of local_size_1, to ensure that every work group does enter all the iterations of the loop:
All bounds checks are done in this loop in parts that do not include a barrier. The entire work group enters not only the first loop but also all iterations the second one:
|
Just pushed a quick dirty fix that seems to fix synchronization issues on my machine, at the price of a performance hit. Will investigate and try to get a proper fix ASAP. |
@ptillet The latest fix works. Thanks for your quick fix. |
Interesting, the test fails randomly on my machine, about half of the time. Maybe an uninitialized variable... Looking into it... |
fd5c6d3 seems to have taken care of it. There is one more issue I'm fixing with the Intel OCL Driver -- there's a segfault on deinitialization, and I suspect it causes some tests to crash. |
There are two tests that failed under AddressSanitizer: * test/TritonGPU/loop-pipeline.mlir * python/test/regression/test_functional_regressions.py with an error: ``` ==8475==ERROR: AddressSanitizer: heap-use-after-free on address 0x50c000bd0be0 at pc 0x557b03278847 bp 0x7ffd69b2c4a0 sp 0x7ffd69b2c498 READ of size 8 at 0x50c000bd0be0 thread T0 #0 0x557b03278846 in getNextOperandUsingThisValue [third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h:43](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h?l=43&ws=aliia/3018&snapshot=215):58 #1 0x557b03278846 in operator++ [third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h:322](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h?l=322&ws=aliia/3018&snapshot=215):39 #2 0x557b03278846 in mlir::ResultRange::UseIterator::operator++() [third_party/llvm/llvm-project/mlir/lib/IR/OperationSupport.cpp:614](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/lib/IR/OperationSupport.cpp?l=614&ws=aliia/3018&snapshot=215):5 #3 0x557affde38c4 in operator++ [third_party/llvm/llvm-project/llvm/include/llvm/ADT/iterator.h:281](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/llvm/include/llvm/ADT/iterator.h?l=281&ws=aliia/3018&snapshot=215):5 #4 0x557affde38c4 in createAsyncCopy [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:117](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=117&ws=aliia/3018&snapshot=215):26 #5 0x557affde38c4 in createAsyncLoad [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:135](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=135&ws=aliia/3018&snapshot=215):3 #6 0x557affde38c4 in createAsynOps [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:501](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=501&ws=aliia/3018&snapshot=215):5 #7 0x557affde38c4 in mlir::triton::preProcessLoopAndGetSchedule(mlir::scf::ForOp&, int, mlir::triton::PipeliningOption&) [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:740](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=740&ws=aliia/3018&snapshot=215):7 #8 0x557affe01c0c in pipelineLoop [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/SoftwarePipeliner.cpp:76](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/SoftwarePipeliner.cpp?l=76&ws=aliia/3018&snapshot=215):19 ... ``` This is likely happening due to iterator being invalidated after `alloc.erase()`. This PR moves erases of allocations outside of a loop and fixes heap-use-after-free issue. Do you know if there is an easy way to run the tests under sanitizers upstream? It would be handy if we can automate it, so we catch this kind of errors early on.
There are two tests that failed under AddressSanitizer: * test/TritonGPU/loop-pipeline.mlir * python/test/regression/test_functional_regressions.py with an error: ``` ==8475==ERROR: AddressSanitizer: heap-use-after-free on address 0x50c000bd0be0 at pc 0x557b03278847 bp 0x7ffd69b2c4a0 sp 0x7ffd69b2c498 READ of size 8 at 0x50c000bd0be0 thread T0 #0 0x557b03278846 in getNextOperandUsingThisValue [third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h:43](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h?l=43&ws=aliia/3018&snapshot=215):58 triton-lang#1 0x557b03278846 in operator++ [third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h:322](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/include/mlir/IR/UseDefLists.h?l=322&ws=aliia/3018&snapshot=215):39 triton-lang#2 0x557b03278846 in mlir::ResultRange::UseIterator::operator++() [third_party/llvm/llvm-project/mlir/lib/IR/OperationSupport.cpp:614](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/mlir/lib/IR/OperationSupport.cpp?l=614&ws=aliia/3018&snapshot=215):5 triton-lang#3 0x557affde38c4 in operator++ [third_party/llvm/llvm-project/llvm/include/llvm/ADT/iterator.h:281](https://cs.corp.google.com/piper///depot/google3/third_party/llvm/llvm-project/llvm/include/llvm/ADT/iterator.h?l=281&ws=aliia/3018&snapshot=215):5 triton-lang#4 0x557affde38c4 in createAsyncCopy [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:117](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=117&ws=aliia/3018&snapshot=215):26 triton-lang#5 0x557affde38c4 in createAsyncLoad [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:135](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=135&ws=aliia/3018&snapshot=215):3 triton-lang#6 0x557affde38c4 in createAsynOps [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:501](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=501&ws=aliia/3018&snapshot=215):5 triton-lang#7 0x557affde38c4 in mlir::triton::preProcessLoopAndGetSchedule(mlir::scf::ForOp&, int, mlir::triton::PipeliningOption&) [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp:740](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/MatmulLoopPipeline.cpp?l=740&ws=aliia/3018&snapshot=215):7 triton-lang#8 0x557affe01c0c in pipelineLoop [third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/SoftwarePipeliner.cpp:76](https://cs.corp.google.com/piper///depot/google3/third_party/triton/lib/Dialect/TritonGPU/Transforms/Pipeliner/SoftwarePipeliner.cpp?l=76&ws=aliia/3018&snapshot=215):19 ... ``` This is likely happening due to iterator being invalidated after `alloc.erase()`. This PR moves erases of allocations outside of a loop and fixes heap-use-after-free issue. Do you know if there is an easy way to run the tests under sanitizers upstream? It would be handy if we can automate it, so we catch this kind of errors early on.
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
When running [convert_blocked1d_to_slice0](https://github.com/triton-lang/triton/blob/0ba5f0c3cd029d5c3d1f01b9bf29dac32c27345e/test/Conversion/tritongpu_to_llvm.mlir#L924) Triton ends up computing a rank of a matrix with 0 columns during linear layout lowering, which trips up f2reduce, and causes undefined behavior, detectable through [UBSAN](https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html). Fix this by returning the rank (0) early in these cases, without calling f2reduce. <details><summary>Stack trace</summary> <p> ``` third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30: runtime error: shift exponent 18446744073709551615 is too large for 64-bit type 'unsigned long long' #0 0x556ee2fea3be in inplace_rref_small third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30 #1 0x556ee2fea3be in f2reduce::inplace_rref_strided(unsigned long*, unsigned long, unsigned long, unsigned long) third_party/triton/third_party/f2reduce/f2reduce.cpp:470:9 #2 0x556ee2ea70da in getMatrixRank third_party/triton/lib/Tools/LinearLayout.cpp:125:3 #3 0x556ee2ea70da in mlir::triton::LinearLayout::checkInvariants(bool) third_party/triton/lib/Tools/LinearLayout.cpp:299:7 #4 0x556ee2ea656d in mlir::triton::LinearLayout::tryCreate(llvm::MapVector<mlir::StringAttr, std::__u::vector<std::__u::vector<int, std::__u::allocator<int>>, std::__u::allocator<std::__u::vector<int, std::__u::allocator<int>>>>, llvm::DenseMap<mlir::StringAttr, unsigned int, llvm::DenseMapInfo<mlir::StringAttr, void>, llvm::detail::DenseMapPair<mlir::StringAttr, unsigned int>>, llvm::SmallVector<std::__u::pair<mlir::StringAttr, std::__u::vector<std::__u::vector<int, std::__u::allocator<int>>, std::__u::allocator<std::__u::vector<int, std::__u::allocator<int>>>>>, 0u>>, llvm::ArrayRef<std::__u::pair<mlir::StringAttr, int>>, bool) third_party/triton/lib/Tools/LinearLayout.cpp:190:41 #5 0x556ee2eb2150 in mlir::triton::LinearLayout::divideRight(mlir::triton::LinearLayout const&) third_party/triton/lib/Tools/LinearLayout.cpp:654:51 #6 0x556ee2ee1c39 in mlir::cvtNeedsSharedMemory(mlir::RankedTensorType, mlir::RankedTensorType) third_party/triton/lib/Analysis/Utility.cpp:652:14 #7 0x556ee2cf38fd in mlir::triton::getRepShapeForCvtLayout(mlir::triton::gpu::ConvertLayoutOp) third_party/triton/lib/Analysis/Allocation.cpp:66:8 #8 0x556ee2cf3efa in mlir::triton::getScratchConfigForCvtLayout(mlir::triton::gpu::ConvertLayoutOp, unsigned int&, unsigned int&) third_party/triton/lib/Analysis/Allocation.cpp:95:19 #9 0x556ee2cf6057 in mlir::triton::AllocationAnalysis::getScratchValueSize(mlir::Operation*) third_party/triton/lib/Analysis/Allocation.cpp:272:24 #10 0x556ee2cf5499 in operator() third_party/triton/lib/Analysis/Allocation.cpp:343:7 #11 0x556ee2cf5499 in void llvm::function_ref<void (mlir::Operation*)>::callback_fn<mlir::triton::AllocationAnalysis::getValuesAndSizes()::'lambda'(mlir::Operation*)>(long, mlir::Operation*) third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLFunctionalExtras.h:45:12 #12 0x556edeeee7a9 in operator() third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLFunctionalExtras.h:68:12 #13 0x556edeeee7a9 in void mlir::detail::walk<mlir::ForwardIterator>(mlir::Operation*, llvm::function_ref<void (mlir::Operation*)>, mlir::WalkOrder) third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:174:5 #14 0x556edeeee87c in void mlir::detail::walk<mlir::ForwardIterator>(mlir::Operation*, llvm::function_ref<void (mlir::Operation*)>, mlir::WalkOrder) third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:182:9 #15 0x556ee2cf49e7 in walk<(mlir::WalkOrder)0, mlir::ForwardIterator, (lambda at third_party/triton/lib/Analysis/Allocation.cpp:341:42), mlir::Operation *, void> third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:313:10 #16 0x556ee2cf49e7 in walk<(mlir::WalkOrder)0, mlir::ForwardIterator, (lambda at third_party/triton/lib/Analysis/Allocation.cpp:341:42), void> third_party/llvm/llvm-project/mlir/include/mlir/IR/Operation.h:794:12 #17 0x556ee2cf49e7 in mlir::triton::AllocationAnalysis::getValuesAndSizes() third_party/triton/lib/Analysis/Allocation.cpp:341:16 #18 0x556ee2cf4852 in run third_party/triton/lib/Analysis/Allocation.cpp:182:5 #19 0x556ee2cf4852 in AllocationAnalysis third_party/triton/lib/Analysis/Allocation.cpp:169:5 #20 0x556ee2cf4852 in mlir::Allocation::run(llvm::DenseMap<mlir::FunctionOpInterface, mlir::Allocation, llvm::DenseMapInfo<mlir::FunctionOpInterface, void>, llvm::detail::DenseMapPair<mlir::FunctionOpInterface, mlir::Allocation>>&) third_party/triton/lib/Analysis/Allocation.cpp:627:3 #21 0x556ee1677402 in operator() third_party/triton/include/triton/Analysis/Allocation.h:227:26 #22 0x556ee1677402 in void mlir::CallGraph<mlir::Allocation>::doWalk<(mlir::WalkOrder)0, (mlir::WalkOrder)1, mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::CallOpInterface, mlir::FunctionOpInterface), mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::FunctionOpInterface)>(mlir::FunctionOpInterface, llvm::DenseSet<mlir::FunctionOpInterface, llvm::DenseMapInfo<mlir::FunctionOpInterface, void>>&, mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::CallOpInterface, mlir::FunctionOpInterface), mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::FunctionOpInterface)) third_party/triton/include/triton/Analysis/Utility.h:350:7 #23 0x556ee16756b3 in walk<(mlir::WalkOrder)0, (mlir::WalkOrder)1, (lambda at third_party/triton/include/triton/Analysis/Allocation.h:222:9), (lambda at third_party/triton/include/triton/Analysis/Allocation.h:224:9)> third_party/triton/include/triton/Analysis/Utility.h:242:7 #24 0x556ee16756b3 in mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp) third_party/triton/include/triton/Analysis/Allocation.h:220:5 #25 0x556ee2c2bf18 in (anonymous namespace)::AllocateSharedMemory::runOnOperation() third_party/triton/lib/Conversion/TritonGPUToLLVM/AllocateSharedMemory.cpp:26:22 ... UndefinedBehaviorSanitizer: invalid-shift-exponent third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30 ``` </p> </details>
…n 2D store. (triton-lang#1515) Support repetition cluster in 2D store. --------- Signed-off-by: Tiotto, Ettore <[email protected]> Co-authored-by: Tiotto, Ettore <[email protected]>
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
…n-lang#4) Signed-off-by: Ilya Enkovich <[email protected]>
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
…n-lang#4) Signed-off-by: Ilya Enkovich <[email protected]>
Signed-off-by: Anatoly Myachev <[email protected]>
When running [convert_blocked1d_to_slice0](https://github.com/triton-lang/triton/blob/0ba5f0c3cd029d5c3d1f01b9bf29dac32c27345e/test/Conversion/tritongpu_to_llvm.mlir#L924) Triton ends up computing a rank of a matrix with 0 columns during linear layout lowering, which trips up f2reduce, and causes undefined behavior, detectable through [UBSAN](https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html). Fix this by returning the rank (0) early in these cases, without calling f2reduce. <details><summary>Stack trace</summary> <p> ``` third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30: runtime error: shift exponent 18446744073709551615 is too large for 64-bit type 'unsigned long long' #0 0x556ee2fea3be in inplace_rref_small third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30 triton-lang#1 0x556ee2fea3be in f2reduce::inplace_rref_strided(unsigned long*, unsigned long, unsigned long, unsigned long) third_party/triton/third_party/f2reduce/f2reduce.cpp:470:9 triton-lang#2 0x556ee2ea70da in getMatrixRank third_party/triton/lib/Tools/LinearLayout.cpp:125:3 triton-lang#3 0x556ee2ea70da in mlir::triton::LinearLayout::checkInvariants(bool) third_party/triton/lib/Tools/LinearLayout.cpp:299:7 triton-lang#4 0x556ee2ea656d in mlir::triton::LinearLayout::tryCreate(llvm::MapVector<mlir::StringAttr, std::__u::vector<std::__u::vector<int, std::__u::allocator<int>>, std::__u::allocator<std::__u::vector<int, std::__u::allocator<int>>>>, llvm::DenseMap<mlir::StringAttr, unsigned int, llvm::DenseMapInfo<mlir::StringAttr, void>, llvm::detail::DenseMapPair<mlir::StringAttr, unsigned int>>, llvm::SmallVector<std::__u::pair<mlir::StringAttr, std::__u::vector<std::__u::vector<int, std::__u::allocator<int>>, std::__u::allocator<std::__u::vector<int, std::__u::allocator<int>>>>>, 0u>>, llvm::ArrayRef<std::__u::pair<mlir::StringAttr, int>>, bool) third_party/triton/lib/Tools/LinearLayout.cpp:190:41 triton-lang#5 0x556ee2eb2150 in mlir::triton::LinearLayout::divideRight(mlir::triton::LinearLayout const&) third_party/triton/lib/Tools/LinearLayout.cpp:654:51 triton-lang#6 0x556ee2ee1c39 in mlir::cvtNeedsSharedMemory(mlir::RankedTensorType, mlir::RankedTensorType) third_party/triton/lib/Analysis/Utility.cpp:652:14 triton-lang#7 0x556ee2cf38fd in mlir::triton::getRepShapeForCvtLayout(mlir::triton::gpu::ConvertLayoutOp) third_party/triton/lib/Analysis/Allocation.cpp:66:8 triton-lang#8 0x556ee2cf3efa in mlir::triton::getScratchConfigForCvtLayout(mlir::triton::gpu::ConvertLayoutOp, unsigned int&, unsigned int&) third_party/triton/lib/Analysis/Allocation.cpp:95:19 triton-lang#9 0x556ee2cf6057 in mlir::triton::AllocationAnalysis::getScratchValueSize(mlir::Operation*) third_party/triton/lib/Analysis/Allocation.cpp:272:24 triton-lang#10 0x556ee2cf5499 in operator() third_party/triton/lib/Analysis/Allocation.cpp:343:7 triton-lang#11 0x556ee2cf5499 in void llvm::function_ref<void (mlir::Operation*)>::callback_fn<mlir::triton::AllocationAnalysis::getValuesAndSizes()::'lambda'(mlir::Operation*)>(long, mlir::Operation*) third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLFunctionalExtras.h:45:12 triton-lang#12 0x556edeeee7a9 in operator() third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLFunctionalExtras.h:68:12 triton-lang#13 0x556edeeee7a9 in void mlir::detail::walk<mlir::ForwardIterator>(mlir::Operation*, llvm::function_ref<void (mlir::Operation*)>, mlir::WalkOrder) third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:174:5 triton-lang#14 0x556edeeee87c in void mlir::detail::walk<mlir::ForwardIterator>(mlir::Operation*, llvm::function_ref<void (mlir::Operation*)>, mlir::WalkOrder) third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:182:9 triton-lang#15 0x556ee2cf49e7 in walk<(mlir::WalkOrder)0, mlir::ForwardIterator, (lambda at third_party/triton/lib/Analysis/Allocation.cpp:341:42), mlir::Operation *, void> third_party/llvm/llvm-project/mlir/include/mlir/IR/Visitors.h:313:10 triton-lang#16 0x556ee2cf49e7 in walk<(mlir::WalkOrder)0, mlir::ForwardIterator, (lambda at third_party/triton/lib/Analysis/Allocation.cpp:341:42), void> third_party/llvm/llvm-project/mlir/include/mlir/IR/Operation.h:794:12 triton-lang#17 0x556ee2cf49e7 in mlir::triton::AllocationAnalysis::getValuesAndSizes() third_party/triton/lib/Analysis/Allocation.cpp:341:16 triton-lang#18 0x556ee2cf4852 in run third_party/triton/lib/Analysis/Allocation.cpp:182:5 triton-lang#19 0x556ee2cf4852 in AllocationAnalysis third_party/triton/lib/Analysis/Allocation.cpp:169:5 triton-lang#20 0x556ee2cf4852 in mlir::Allocation::run(llvm::DenseMap<mlir::FunctionOpInterface, mlir::Allocation, llvm::DenseMapInfo<mlir::FunctionOpInterface, void>, llvm::detail::DenseMapPair<mlir::FunctionOpInterface, mlir::Allocation>>&) third_party/triton/lib/Analysis/Allocation.cpp:627:3 triton-lang#21 0x556ee1677402 in operator() third_party/triton/include/triton/Analysis/Allocation.h:227:26 triton-lang#22 0x556ee1677402 in void mlir::CallGraph<mlir::Allocation>::doWalk<(mlir::WalkOrder)0, (mlir::WalkOrder)1, mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::CallOpInterface, mlir::FunctionOpInterface), mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::FunctionOpInterface)>(mlir::FunctionOpInterface, llvm::DenseSet<mlir::FunctionOpInterface, llvm::DenseMapInfo<mlir::FunctionOpInterface, void>>&, mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::CallOpInterface, mlir::FunctionOpInterface), mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp)::'lambda'(mlir::FunctionOpInterface)) third_party/triton/include/triton/Analysis/Utility.h:350:7 triton-lang#23 0x556ee16756b3 in walk<(mlir::WalkOrder)0, (mlir::WalkOrder)1, (lambda at third_party/triton/include/triton/Analysis/Allocation.h:222:9), (lambda at third_party/triton/include/triton/Analysis/Allocation.h:224:9)> third_party/triton/include/triton/Analysis/Utility.h:242:7 triton-lang#24 0x556ee16756b3 in mlir::ModuleAllocation::ModuleAllocation(mlir::ModuleOp) third_party/triton/include/triton/Analysis/Allocation.h:220:5 triton-lang#25 0x556ee2c2bf18 in (anonymous namespace)::AllocateSharedMemory::runOnOperation() third_party/triton/lib/Conversion/TritonGPUToLLVM/AllocateSharedMemory.cpp:26:22 ... UndefinedBehaviorSanitizer: invalid-shift-exponent third_party/triton/third_party/f2reduce/f2reduce.cpp:421:30 ``` </p> </details>
Summary: This is stll a kind of the boilerplate and basic lowering for the first milestone (compiling vector addition). This PR firstly lowers `tt.func` and `tt.return`. Test Plan: It can safely compile an empty kernel. ``` @triton.jit def add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): return ``` > TRITON_ENABLE_LLVM_DEBUG=1 TRITON_CPU_BACKEND=1 python3 empty_kerne.py ``` //===-------------------------------------------===// Legalizing operation : 'tt.func'(0x73be2a0) { * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.func -> ()' { Trying to match "(anonymous namespace)::FuncOpConversion" ** Insert : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Insert Block into : 'llvm.func'(0x6c04c70) ** Erase : 'tt.func'(0x73be2a0) "(anonymous namespace)::FuncOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.func'(0x6c04c70) { } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// ... //===-------------------------------------------===// Legalizing operation : 'tt.return'(0x73efeb0) { "tt.return"() : () -> () * Fold { } -> FAILURE : unable to fold * Pattern : 'tt.return -> ()' { Trying to match "(anonymous namespace)::ReturnOpConversion" ** Insert : 'llvm.return'(0x73c0f00) ** Replace : 'tt.return'(0x73efeb0) "(anonymous namespace)::ReturnOpConversion" result 1 //===-------------------------------------------===// Legalizing operation : 'llvm.return'(0x73c0f00) { "llvm.return"() : () -> () } -> SUCCESS : operation marked legal by the target //===-------------------------------------------===// } -> SUCCESS : pattern applied successfully ```
…n-lang#4) Signed-off-by: Ilya Enkovich <[email protected]>
The latest isaac code triggers many test failures with caffe's opencl branch. The good commit is:
Templates/Reduce1D: now properly loading 2D scalars commit 6ac5e1f
Since that commit, both "General: Internal code generator overhaul" and "JIT: No longer using fallbacks for stride[0] > 1" introduce some regressions.
It's easy to build the Caffe's opencl branch as below:
mkdir build
cmake -DUSE_GREENTEA=ON -DUSE_ISAAC=ON ..
cd build
make -j8
make runtest
Then you will see many new failures with the above two commit.
BTW It's better to use latest beignet driver as the OCL compiler. The good commit works great with beignet.
@ptillet Could you look at this issue? Thanks.
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