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Fix barrier insertion after assert
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#5114
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Please add a regression test
Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>
done |
@@ -34,6 +33,20 @@ def _kernel(COND: tl.constexpr): | |||
getattr(torch, device).synchronize() | |||
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def test_device_assert_barrier(monkeypatch, device): |
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In general such bugs transformations it is better to do lit tests to test specifically the case that cause the bug
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@ThomasRaoux Thanks for the advice!
Should I try writing a lit test now?
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would be nice to replace this with a lit if you have a chance
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It seems that this issue is somehow related to multithreading. I have a lit test (unfortunately only for the XPU backend) and if I also specify --mlir-disable-threading
, the issue goes away. Also, it seems that this is still a problem of LLVM, could you give some advice on how to rewrite the reproducer in order to fill out an issue for them? Or maybe there are some ideas on what can be done about this?
Lit test
// RUN: triton-opt %s --convert-triton-intel-gpu-to-llvm
// -----
#blocked = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [32], warpsPerCTA = [4], order = [0]}>
// CHECK: llvm.call @__assertfail
module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, triton_gpu.target = "xpu", "triton_gpu.threads-per-warp" = 32 : i32} {
tt.func public @_kernel_qwerty(%arg0: !tt.ptr<i32>) {
%cst = arith.constant dense<1> : tensor<8xi32, #blocked>
%0 = tt.make_range {end = 8 : i32, start = 0 : i32} : tensor<8xi32, #blocked>
%1 = tt.splat %arg0 : !tt.ptr<i32> -> tensor<8x!tt.ptr<i32>, #blocked>
%2 = tt.addptr %1, %0 : tensor<8x!tt.ptr<i32>, #blocked>, tensor<8xi32, #blocked>
%3 = tt.load %2 : tensor<8x!tt.ptr<i32>, #blocked>
%4 = arith.cmpi slt, %3, %cst : tensor<8xi32, #blocked>
tt.assert %4, "" : tensor<8xi1, #blocked>
tt.return
}
}
Stack trace
RUN: at line 1: .../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt .../intel-xpu-backend-for-triton/test/Conversion/intel/tritonintelgpu_to_llvm.mlir --convert-triton-intel-gpu-to-llvm
+ .../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt .../intel-xpu-backend-for-triton/test/Conversion/intel/tritonintelgpu_to_llvm.mlir --convert-triton-intel-gpu-to-llvm
triton-opt: /home/runner/work/triton/triton/llvm-project/llvm/include/llvm/ADT/ilist_iterator.h:168: llvm::ilist_iterator::reference llvm::ilist_iterator<llvm::ilist_detail::node_options<mlir::Operation, true, false, void, false, void>, false, false>::operator*() const [OptionsT = llvm::ilist_detail::node_options<mlir::Operation, true, false, void, false, void>, IsReverse = false, IsConst = false]: Assertion `!NodePtr->isKnownSentinel()' failed.
PLEASE submit a bug report to https://github.com/llvm/llvm-project/issues/ and include the crash backtrace.
#0 0x000055d5cf9bf447 llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x312f447)
#1 0x000055d5cf9bcf6e llvm::sys::RunSignalHandlers() (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x312cf6e)
#2 0x000055d5cf9bfaff SignalHandler(int) Signals.cpp:0:0
#3 0x00007f25ba794520 (/lib/x86_64-linux-gnu/libc.so.6+0x42520)
#4 0x00007f25ba7e89fc __pthread_kill_implementation ./nptl/pthread_kill.c:44:76
#5 0x00007f25ba7e89fc __pthread_kill_internal ./nptl/pthread_kill.c:78:10
#6 0x00007f25ba7e89fc pthread_kill ./nptl/pthread_kill.c:89:10
#7 0x00007f25ba794476 gsignal ./signal/../sysdeps/posix/raise.c:27:6
#8 0x00007f25ba77a7f3 abort ./stdlib/abort.c:81:7
#9 0x00007f25ba77a71b _nl_load_domain ./intl/loadmsgcat.c:1177:9
#10 0x00007f25ba78be96 (/lib/x86_64-linux-gnu/libc.so.6+0x39e96)
#11 0x000055d5cf90c137 mlir::Operation::updateOrderIfNecessary() (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x307c137)
#12 0x000055d5cf90bf7f mlir::Operation::isBeforeInBlock(mlir::Operation*) (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x307bf7f)
#13 0x000055d5cf8e4222 mlir::DominanceInfo::properlyDominates(mlir::Value, mlir::Operation*) const (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x3054222)
#14 0x000055d5cf92b224 (anonymous namespace)::OperationVerifier::verifyOpAndDominance(mlir::Operation&) Verifier.cpp:0:0
#15 0x000055d5cf92c9be std::_Function_handler<void (), llvm::LogicalResult mlir::failableParallelForEach<mlir::Operation**, (anonymous namespace)::OperationVerifier::verifyOnExit(mlir::Operation&)::$_3>(mlir::MLIRContext*, mlir::Operation**, mlir::Operation**, (anonymous namespace)::OperationVerifier::verifyOnExit(mlir::Operation&)::$_3&&)::'lambda'()>::_M_invoke(std::_Any_data const&) Verifier.cpp:0:0
#16 0x000055d5cea285e8 std::_Function_handler<std::unique_ptr<std::__future_base::_Result_base, std::__future_base::_Result_base::_Deleter> (), std::__future_base::_Task_setter<std::unique_ptr<std::__future_base::_Result<void>, std::__future_base::_Result_base::_Deleter>, std::thread::_Invoker<std::tuple<std::function<void ()>>>, void>>::_M_invoke(std::_Any_data const&) (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x21985e8)
#17 0x000055d5cea28547 std::__future_base::_State_baseV2::_M_do_set(std::function<std::unique_ptr<std::__future_base::_Result_base, std::__future_base::_Result_base::_Deleter> ()>*, bool*) (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x2198547)
#18 0x00007f25ba7ebee8 __pthread_once_slow ./nptl/pthread_once.c:118:7
#19 0x000055d5cea288fb std::__future_base::_Deferred_state<std::thread::_Invoker<std::tuple<std::function<void ()>>>, void>::_M_complete_async() (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x21988fb)
#20 0x000055d5cea289a9 void std::__invoke_impl<void, std::shared_future<void> llvm::ThreadPoolInterface::asyncImpl<void>(std::function<void ()>, llvm::ThreadPoolTaskGroup*)::'lambda'()&>(std::__invoke_other, std::shared_future<void> llvm::ThreadPoolInterface::asyncImpl<void>(std::function<void ()>, llvm::ThreadPoolTaskGroup*)::'lambda'()&) .../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x21989a9)
#21 0x000055d5cf99c1db llvm::StdThreadPool::processTasks(llvm::ThreadPoolTaskGroup*) (.../intel-xpu-backend-for-triton/python/build/cmake.linux-x86_64-cpython-3.10/bin/triton-opt+0x310c1db)
#22 0x000055d5cf99d4d7 void* llvm::thread::ThreadProxy<std::tuple<llvm::StdThreadPool::grow(int)::$_0>>(void*) ThreadPool.cpp:0:0
#23 0x00007f25ba7e6ac3 start_thread ./nptl/pthread_create.c:442:8
#24 0x00007f25ba878850 ./misc/../sysdeps/unix/sysv/linux/x86_64/clone3.S:83:0
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I'm guessing the fact that it goes away without multi-threading is just that there is some memory corruption and it passes by luck otherwise. Is the barrier inserted at the right spot in this case? Checking that should be enough
If you think it is an issue in MLIR core you can file an issue in llvm github: https://github.com/llvm/llvm-project
…d code (#5135) I discovered it while I was looking at #5114 (comment). Looks like `bar_id` attribute is no longer used. The attribute setting was removed in: dd2a323. Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>
This will fix the following problem: ```bash python: /home/runner/work/triton/triton/llvm-project/llvm/include/llvm/ADT/ilist_iterator.h:168: llvm::ilist_iterator::reference llvm::ilist_iterator<llvm::ilist_detail::node_options<mlir::Operation, true, false, void, false, void>, false, false>::operator*() const [OptionsT = llvm::ilist_detail::node_options<mlir::Operation, true, false, void, false, void>, IsReverse = false, IsConst = false]: Assertion `!NodePtr->isKnownSentinel()' failed. Aborted (core dumped) ``` The problem was found when using PyTorch on Intel gpu: <details> <summary> Simplified reproducer triton-lang#1:</summary> ```python from torch._inductor.async_compile import AsyncCompile async_compile = AsyncCompile() triton_per_fused_add_embedding_native_layer_norm_0 = async_compile.triton('triton_per_fused_add_embedding_native_layer_norm_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, DeviceProperties triton_helpers.set_driver_to_gpu() @triton_heuristics.persistent_reduction( size_hints=[512, 128], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {'in_ptr0': '*i64', 'in_ptr1': '*fp32', 'in_ptr2': '*fp32', 'in_ptr3': '*fp32', 'in_ptr4': '*fp32', 'in_ptr5': '*fp32', 'out_ptr2': '*fp32', 'xnumel': 'i32', 'rnumel': 'i32'}, 'device': DeviceProperties(type='xpu', index=0, cc={'driver_version': '1.3.30049', 'gpu_eu_count': 448, 'gpu_subslice_count': 56, 'has_atomic64': True, 'has_bfloat16_conversions': True, 'has_fp16': True, 'has_fp64': True, 'has_subgroup_2d_block_io': True, 'has_subgroup_matrix_multiply_accumulate': True, 'has_subgroup_matrix_multiply_accumulate_tensor_float32': False, 'max_compute_units': 448, 'max_num_sub_groups': 64, 'max_work_group_size': 1024, 'name': 'Intel(R) Data Center GPU Max 1100', 'platform_name': 'Intel(R) Level-Zero', 'sub_group_sizes': [16, 32], 'total_memory': 51539607552, 'type': 'gpu', 'vendor': 'Intel(R) Corporation', 'version': '1.3'}, major=None, regs_per_multiprocessor=None, max_threads_per_multi_processor=None, multi_processor_count=None, warp_size=32), 'constants': {}, 'configs': [AttrsDescriptor.from_dict({'arg_properties': {'tt.divisibility': (0, 1, 2, 3, 4, 5, 6, 7, 8), 'tt.equal_to': ()}, 'cls': 'AttrsDescriptor'})]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_embedding_native_layer_norm_0', 'mutated_arg_names': [], 'optimize_mem': True, 'no_x_dim': False, 'num_load': 5, 'num_reduction': 4, 'backend_hash': 'D82C2E8E2C9203D653D1A2B8A0511701E4F7567A195A5128E03B9AA7218348AA', 'are_deterministic_algorithms_enabled': True, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_embedding_native_layer_norm_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 512 rnumel = 128 RBLOCK: tl.constexpr = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) x0 = xindex r1 = rindex tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr2 + (r1 + (128*x0)), xmask, other=0.0) tmp9 = tl.load(in_ptr3 + (r1 + (128*x0)), xmask, other=0.0) tmp34 = tl.load(in_ptr4 + (r1), None, eviction_policy='evict_last') tmp36 = tl.load(in_ptr5 + (r1), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK, RBLOCK], 30000, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tl.device_assert(((0 <= tmp4) & (tmp4 < 30000)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 30000") ''', device_str='xpu') ``` </details>
…d code (triton-lang#5135) I discovered it while I was looking at triton-lang#5114 (comment). Looks like `bar_id` attribute is no longer used. The attribute setting was removed in: triton-lang@dd2a323. Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>
This will fix the following problem:
The problem was found when using PyTorch on Intel gpu:
Simplified reproducer #1: