vllm - 💡(How to fix) Fix [Bug]: Benchmark tuning MoE kernel config fails ; NVIDIA GH200, Qwen3-Coder-Next, vLLM 0.17.1 [1 participants]
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vllm-project/vllm#37191•Fetched 2026-04-08 00:48:44
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tomschelsenError Message
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"
Namespace(model='Qwen/Qwen3-Coder-Next-FP8', tp_size=1, enable_expert_parallel=False, dtype='auto', use_deep_gemm=False, save_dir='/mnt/fused_moe_configs/', seed=0, batch_size=None, tune=True, trust_remote_code=False, model_prefix=None)
2026-03-16 12:35:25,013 INFO worker.py:2013 -- Started a local Ray instance.
/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
warnings.warn(
Start tuning over 1920 configurations...
(BenchmarkWorker pid=726) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=1
(BenchmarkWorker pid=796) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=2
(BenchmarkWorker pid=726) Mon Mar 16 12:50:29 2026] Completed tuning for batch_size=4
(pid=726) : 85%|██████████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.63k/1.92k [11:56<01:02, 4.73it/s]Traceback (most recent call last):██████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.84k/1.92k [13:15<02:00, 1.52s/it]
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 1041, in <module>
main(args)
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 951, in main
configs = _distribute(
^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 927, in _distribute
return ray.get(outputs)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 2981, in get
values, debugger_breakpoint = worker.get_objects(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 1012, in get_objects
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RuntimeError): ray::BenchmarkWorker.tune() (pid=726, ip=XXX, actor_id=5f66c1327f7a25a3b8fd359101000000, repr=<benchmark_moe.BenchmarkWorker object at 0xfc7a5e152750>)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 632, in tune
kernel_time = benchmark_config(
^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 306, in benchmark_config
run()
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 295, in run
return fused_experts(
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1578, in fused_experts
return dispatch_fused_experts_func(inplace)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1548, in torch_vllm_outplace_fused_experts
return torch.ops.vllm.outplace_fused_experts(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in call
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 109, in torch_function
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in call
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1476, in outplace_fused_experts
return fused_experts_impl(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1855, in fused_experts_impl
dispatch_fused_moe_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 918, in dispatch_fused_moe_kernel
invoke_fused_moe_triton_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 794, in invoke_fused_moe_triton_kernel
fused_moe_kernel[grid](
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.dict)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py", line 324, in compile
next_module = compile_ir(module, metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 541, in <lambda>
stages["ttgir"] = lambda src, metadata: self.make_ttgir(src, metadata, options, capability)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 316, in make_ttgir
pm.run(mod, 'make_ttgir')
RuntimeError: PassManager::run failed
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:574:24: error: operand #0 does not dominate this use
(BenchmarkWorker pid=726) c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:508:28: note: operand defined here (op in a child region)
(BenchmarkWorker pid=726) mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) module {
(BenchmarkWorker pid=726) tt.func public @fused_moe_kernel(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32}, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}, %arg18: i32 {tt.divisibility = 16 : i32}, %arg19: i32 {tt.divisibility = 16 : i32}, %arg20: i32 {tt.divisibility = 16 : i32}, %arg21: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
(BenchmarkWorker pid=726) %c63_i32 = arith.constant 63 : i32
(BenchmarkWorker pid=726) %c255_i32 = arith.constant 255 : i32
(BenchmarkWorker pid=726) %cst = arith.constant dense<0.000000e+00> : tensor<64x64xbf16>
(BenchmarkWorker pid=726) %cst_0 = arith.constant dense<0.000000e+00> : tensor<256x64xbf16>
(BenchmarkWorker pid=726) %c0_i32 = arith.constant 0 : i32
(BenchmarkWorker pid=726) %c-1_i64 = arith.constant -1 : i64
(BenchmarkWorker pid=726) %cst_1 = arith.constant dense<64> : tensor<64x64xi32>
(BenchmarkWorker pid=726) %cst_2 = arith.constant dense<64> : tensor<256x64xi32>
(BenchmarkWorker pid=726) %cst_3 = arith.constant dense<0.000000e+00> : tensor<256x64xf32>
(BenchmarkWorker pid=726) %cst_4 = arith.constant dense<10> : tensor<256x1xi64>
(BenchmarkWorker pid=726) %c64_i32 = arith.constant 64 : i32
(BenchmarkWorker pid=726) %c256_i32 = arith.constant 256 : i32
(BenchmarkWorker pid=726) %c1_i32 = arith.constant 1 : i32
(BenchmarkWorker pid=726) %0 = tt.get_program_id x : i32
(BenchmarkWorker pid=726) %1 = arith.addi %arg9, %c255_i32 : i32
(BenchmarkWorker pid=726) %2 = arith.divsi %1, %c256_i32 : i32
(BenchmarkWorker pid=726) %3 = arith.addi %arg7, %c63_i32 : i32
(BenchmarkWorker pid=726) %4 = arith.divsi %3, %c64_i32 : i32
(BenchmarkWorker pid=726) %5 = arith.divsi %0, %4 : i32
(BenchmarkWorker pid=726) %6 = arith.subi %2, %5 : i32
(BenchmarkWorker pid=726) %7 = arith.minsi %6, %c1_i32 : i32
(BenchmarkWorker pid=726) %8 = arith.remsi %0, %4 : i32
(BenchmarkWorker pid=726) %9 = arith.remsi %8, %7 : i32
(BenchmarkWorker pid=726) %10 = arith.addi %5, %9 : i32
(BenchmarkWorker pid=726) %11 = arith.divsi %8, %7 : i32
(BenchmarkWorker pid=726) %12 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>
(BenchmarkWorker pid=726) %13 = arith.extsi %12 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %14 = tt.load %arg6 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %15 = arith.muli %10, %c256_i32 : i32
(BenchmarkWorker pid=726) %16 = arith.cmpi sge, %15, %14 : i32
(BenchmarkWorker pid=726) cf.cond_br %16, ^bb1, ^bb2
(BenchmarkWorker pid=726) ^bb1: // pred: ^bb0
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb2: // pred: ^bb0
(BenchmarkWorker pid=726) %17 = arith.extsi %15 : i32 to i64
(BenchmarkWorker pid=726) %18 = tt.splat %17 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %19 = arith.addi %18, %13 : tensor<256xi64>
(BenchmarkWorker pid=726) %20 = tt.splat %arg4 : !tt.ptr<i32> -> tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %21 = tt.addptr %20, %19 : tensor<256x!tt.ptr<i32>>, tensor<256xi64>
(BenchmarkWorker pid=726) %22 = tt.load %21 : tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %23 = arith.extsi %22 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %24 = arith.extsi %arg10 : i32 to i64
(BenchmarkWorker pid=726) %25 = tt.splat %24 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %26 = arith.cmpi slt, %23, %25 : tensor<256xi64>
(BenchmarkWorker pid=726) %27 = tt.addptr %arg5, %10 : !tt.ptr<i32>, i32
(BenchmarkWorker pid=726) %28 = tt.load %27 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %29 = arith.extsi %28 : i32 to i64
(BenchmarkWorker pid=726) %30 = arith.cmpi eq, %29, %c-1_i64 : i64
(BenchmarkWorker pid=726) cf.cond_br %30, ^bb3, ^bb4
(BenchmarkWorker pid=726) ^bb3: // pred: ^bb2
(BenchmarkWorker pid=726) %31 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %32 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %33 = tt.splat %31 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %34 = arith.addi %33, %32 : tensor<64xi32>
(BenchmarkWorker pid=726) %35 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %36 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %37 = tt.splat %36 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %38 = arith.muli %37, %35 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %39 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %40 = tt.addptr %39, %38 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %41 = tt.expand_dims %34 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %42 = tt.broadcast %40 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %43 = tt.broadcast %41 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %44 = tt.addptr %42, %43 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %45 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %46 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %47 = arith.cmpi slt, %41, %46 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %48 = tt.broadcast %45 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %49 = tt.broadcast %47 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %50 = arith.andi %48, %49 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %44, %cst_0, %50 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb4: // pred: ^bb2
(BenchmarkWorker pid=726) %51 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %52 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %53 = arith.extsi %52 : tensor<64xi32> to tensor<64xi64>
(BenchmarkWorker pid=726) %54 = arith.extsi %51 : i32 to i64
(BenchmarkWorker pid=726) %55 = tt.splat %54 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %56 = arith.addi %55, %53 : tensor<64xi64>
(BenchmarkWorker pid=726) %57 = arith.extsi %arg7 : i32 to i64
(BenchmarkWorker pid=726) %58 = tt.splat %57 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %59 = arith.remsi %56, %58 : tensor<64xi64>
(BenchmarkWorker pid=726) %60 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %61 = arith.divsi %60, %cst_4 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %62 = arith.extsi %arg11 : i32 to i64
(BenchmarkWorker pid=726) %63 = tt.splat %62 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %64 = arith.muli %61, %63 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %65 = tt.expand_dims %52 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %66 = arith.extsi %65 : tensor<1x64xi32> to tensor<1x64xi64>
(BenchmarkWorker pid=726) %67 = tt.broadcast %64 : tensor<256x1xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %68 = tt.broadcast %66 : tensor<1x64xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %69 = arith.addi %67, %68 : tensor<256x64xi64>
(BenchmarkWorker pid=726) %70 = tt.splat %arg0 : !tt.ptr<bf16> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %71 = tt.addptr %70, %69 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi64>
(BenchmarkWorker pid=726) %72 = arith.extsi %arg12 : i32 to i64
(BenchmarkWorker pid=726) %73 = arith.muli %29, %72 : i64
(BenchmarkWorker pid=726) %74 = tt.addptr %arg1, %73 : !tt.ptr<bf16>, i64
(BenchmarkWorker pid=726) %75 = tt.expand_dims %52 {axis = 1 : i32} : tensor<64xi32> -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %76 = tt.expand_dims %59 {axis = 0 : i32} : tensor<64xi64> -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %77 = arith.extsi %arg13 : i32 to i64
(BenchmarkWorker pid=726) %78 = tt.splat %77 : i64 -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %79 = arith.muli %76, %78 : tensor<1x64xi64>
(BenchmarkWorker pid=726) %80 = arith.extsi %75 : tensor<64x1xi32> to tensor<64x1xi64>
(BenchmarkWorker pid=726) %81 = tt.broadcast %80 : tensor<64x1xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %82 = tt.broadcast %79 : tensor<1x64xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %83 = arith.addi %81, %82 : tensor<64x64xi64>
(BenchmarkWorker pid=726) %84 = tt.splat %74 : !tt.ptr<bf16> -> tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %85 = tt.addptr %84, %83 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi64>
(BenchmarkWorker pid=726) %86 = arith.addi %arg8, %c63_i32 : i32
(BenchmarkWorker pid=726) %87 = arith.divsi %86, %c64_i32 : i32
(BenchmarkWorker pid=726) %88:3 = scf.for %arg22 = %c0_i32 to %87 step %c1_i32 iter_args(%arg23 = %71, %arg24 = %85, %arg25 = %cst_3) -> (tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>) : i32 {
(BenchmarkWorker pid=726) %107 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %108 = arith.muli %arg22, %c64_i32 : i32
(BenchmarkWorker pid=726) %109 = arith.subi %arg8, %108 : i32
(BenchmarkWorker pid=726) %110 = tt.splat %109 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %111 = arith.cmpi slt, %65, %110 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %112 = tt.broadcast %107 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %113 = tt.broadcast %111 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %114 = arith.andi %112, %113 : tensor<256x64xi1>
(BenchmarkWorker pid=726) %115 = tt.load %arg23, %114, %cst_0 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %116 = tt.splat %109 : i32 -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %117 = arith.cmpi slt, %75, %116 : tensor<64x1xi32>
(BenchmarkWorker pid=726) %118 = tt.broadcast %117 : tensor<64x1xi1> -> tensor<64x64xi1>
(BenchmarkWorker pid=726) %119 = tt.load %arg24, %118, %cst : tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %120 = tt.dot %115, %119, %arg25, inputPrecision = tf32 : tensor<256x64xbf16> * tensor<64x64xbf16> -> tensor<256x64xf32>
(BenchmarkWorker pid=726) %121 = tt.addptr %arg23, %cst_2 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %122 = tt.addptr %arg24, %cst_1 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi32>
(BenchmarkWorker pid=726) scf.yield %121, %122, %120 : tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) %89 = arith.truncf %88#2 : tensor<256x64xf32> to tensor<256x64xbf16>
(BenchmarkWorker pid=726) %90 = tt.splat %51 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %91 = arith.addi %90, %52 : tensor<64xi32>
(BenchmarkWorker pid=726) %92 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %93 = tt.splat %92 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %94 = arith.muli %93, %60 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %95 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %96 = tt.addptr %95, %94 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %97 = tt.expand_dims %91 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %98 = tt.broadcast %96 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %99 = tt.broadcast %97 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %100 = tt.addptr %98, %99 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %101 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %102 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %103 = arith.cmpi slt, %97, %102 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %104 = tt.broadcast %101 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %105 = tt.broadcast %103 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %106 = arith.andi %104, %105 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %100, %89, %106 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726)
(BenchmarkWorker pid=726) {-#
(BenchmarkWorker pid=726) external_resources: {
(BenchmarkWorker pid=726) mlir_reproducer: {
(BenchmarkWorker pid=726) pipeline: "builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:90 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, tritongpu-combine-tensor-select-and-if, nvgpu-warp-specialization{dump-intermediate-steps=false num-stages=2}, tritongpu-assign-latencies{num-stages=2}, tritongpu-schedule-loops, tritongpu-pipeline{dump-intermediate-steps=false num-stages=2}, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=90}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})",
(BenchmarkWorker pid=726) disable_threading: true,
(BenchmarkWorker pid=726) verify_each: true
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) #-}
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: error: Failures have been detected while processing an MLIR pass pipeline
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: note: Pipeline failed while executing [TritonGPURemoveLayoutConversions on 'builtin.module' operation]: reproducer generated at std::errs, please share the reproducer above with Triton project.
(pid=726) : 98%|█████████▊| 1.89k/1.92k [26:58<00:26, 1.17it/s]
(pid=726) : 97%|█████████▋| 1.85k/1.92k [13:24<00:28, 2.31it/s]
(pid=726) : 85%|████████▍ | 1.63k/1.92k [11:59<02:10, 2.26it/s]
(pid=726) : 0%| | 1.00/1.92k [00:02<1:23:09, 2.60s/it]
(pid=796) : 98%|█████████▊| 1.89k/1.92k [26:57<00:26, 1.17it/s]
(pid=796) : 96%|█████████▌| 1.84k/1.92k [13:24<00:34, 2.29it/s]
Fix Action
Fix / Workaround
============================== CPU Info
Architecture: aarch64 CPU op-mode(s): 64-bit Byte Order: Little Endian CPU(s): 144 On-line CPU(s) list: 0-143 Vendor ID: ARM Model name: Neoverse-V2 Model: 0 Thread(s) per core: 1 Core(s) per cluster: 72 Socket(s): - Cluster(s): 2 Stepping: r0p0 Frequency boost: disabled CPU max MHz: 3474.0000 CPU min MHz: 81.0000 BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti L1d cache: 9 MiB (144 instances) L1i cache: 9 MiB (144 instances) L2 cache: 144 MiB (144 instances) L3 cache: 228 MiB (2 instances) NUMA node(s): 18 NUMA node0 CPU(s): 0-71 NUMA node1 CPU(s): 72-143 NUMA node2 CPU(s): NUMA node3 CPU(s): NUMA node4 CPU(s): NUMA node5 CPU(s): NUMA node6 CPU(s): NUMA node7 CPU(s): NUMA node8 CPU(s): NUMA node9 CPU(s): NUMA node10 CPU(s): NUMA node11 CPU(s): NUMA node12 CPU(s): NUMA node13 CPU(s): NUMA node14 CPU(s): NUMA node15 CPU(s): NUMA node16 CPU(s): NUMA node17 CPU(s): Vulnerability Gather data sampling: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; __user pointer sanitization Vulnerability Spectre v2: Mitigation; CSV2, BHB Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"
Namespace(model='Qwen/Qwen3-Coder-Next-FP8', tp_size=1, enable_expert_parallel=False, dtype='auto', use_deep_gemm=False, save_dir='/mnt/fused_moe_configs/', seed=0, batch_size=None, tune=True, trust_remote_code=False, model_prefix=None)
2026-03-16 12:35:25,013 INFO worker.py:2013 -- Started a local Ray instance.
/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
warnings.warn(
Start tuning over 1920 configurations...
(BenchmarkWorker pid=726) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=1
(BenchmarkWorker pid=796) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=2
(BenchmarkWorker pid=726) Mon Mar 16 12:50:29 2026] Completed tuning for batch_size=4
(pid=726) : 85%|██████████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.63k/1.92k [11:56<01:02, 4.73it/s]Traceback (most recent call last):██████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.84k/1.92k [13:15<02:00, 1.52s/it]
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 1041, in <module>
main(args)
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 951, in main
configs = _distribute(
^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 927, in _distribute
return ray.get(outputs)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 2981, in get
values, debugger_breakpoint = worker.get_objects(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 1012, in get_objects
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RuntimeError): ray::BenchmarkWorker.tune() (pid=726, ip=XXX, actor_id=5f66c1327f7a25a3b8fd359101000000, repr=<benchmark_moe.BenchmarkWorker object at 0xfc7a5e152750>)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 632, in tune
kernel_time = benchmark_config(
^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 306, in benchmark_config
run()
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 295, in run
return fused_experts(
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1578, in fused_experts
return dispatch_fused_experts_func(inplace)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1548, in torch_vllm_outplace_fused_experts
return torch.ops.vllm.outplace_fused_experts(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 109, in __torch_function__
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1476, in outplace_fused_experts
return fused_experts_impl(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1855, in fused_experts_impl
dispatch_fused_moe_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 918, in dispatch_fused_moe_kernel
invoke_fused_moe_triton_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 794, in invoke_fused_moe_triton_kernel
fused_moe_kernel[grid](
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py", line 324, in compile
next_module = compile_ir(module, metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 541, in <lambda>
stages["ttgir"] = lambda src, metadata: self.make_ttgir(src, metadata, options, capability)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 316, in make_ttgir
pm.run(mod, 'make_ttgir')
RuntimeError: PassManager::run failed
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:574:24: error: operand #0 does not dominate this use
(BenchmarkWorker pid=726) c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:508:28: note: operand defined here (op in a child region)
(BenchmarkWorker pid=726) mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) module {
(BenchmarkWorker pid=726) tt.func public @fused_moe_kernel(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32}, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}, %arg18: i32 {tt.divisibility = 16 : i32}, %arg19: i32 {tt.divisibility = 16 : i32}, %arg20: i32 {tt.divisibility = 16 : i32}, %arg21: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
(BenchmarkWorker pid=726) %c63_i32 = arith.constant 63 : i32
(BenchmarkWorker pid=726) %c255_i32 = arith.constant 255 : i32
(BenchmarkWorker pid=726) %cst = arith.constant dense<0.000000e+00> : tensor<64x64xbf16>
(BenchmarkWorker pid=726) %cst_0 = arith.constant dense<0.000000e+00> : tensor<256x64xbf16>
(BenchmarkWorker pid=726) %c0_i32 = arith.constant 0 : i32
(BenchmarkWorker pid=726) %c-1_i64 = arith.constant -1 : i64
(BenchmarkWorker pid=726) %cst_1 = arith.constant dense<64> : tensor<64x64xi32>
(BenchmarkWorker pid=726) %cst_2 = arith.constant dense<64> : tensor<256x64xi32>
(BenchmarkWorker pid=726) %cst_3 = arith.constant dense<0.000000e+00> : tensor<256x64xf32>
(BenchmarkWorker pid=726) %cst_4 = arith.constant dense<10> : tensor<256x1xi64>
(BenchmarkWorker pid=726) %c64_i32 = arith.constant 64 : i32
(BenchmarkWorker pid=726) %c256_i32 = arith.constant 256 : i32
(BenchmarkWorker pid=726) %c1_i32 = arith.constant 1 : i32
(BenchmarkWorker pid=726) %0 = tt.get_program_id x : i32
(BenchmarkWorker pid=726) %1 = arith.addi %arg9, %c255_i32 : i32
(BenchmarkWorker pid=726) %2 = arith.divsi %1, %c256_i32 : i32
(BenchmarkWorker pid=726) %3 = arith.addi %arg7, %c63_i32 : i32
(BenchmarkWorker pid=726) %4 = arith.divsi %3, %c64_i32 : i32
(BenchmarkWorker pid=726) %5 = arith.divsi %0, %4 : i32
(BenchmarkWorker pid=726) %6 = arith.subi %2, %5 : i32
(BenchmarkWorker pid=726) %7 = arith.minsi %6, %c1_i32 : i32
(BenchmarkWorker pid=726) %8 = arith.remsi %0, %4 : i32
(BenchmarkWorker pid=726) %9 = arith.remsi %8, %7 : i32
(BenchmarkWorker pid=726) %10 = arith.addi %5, %9 : i32
(BenchmarkWorker pid=726) %11 = arith.divsi %8, %7 : i32
(BenchmarkWorker pid=726) %12 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>
(BenchmarkWorker pid=726) %13 = arith.extsi %12 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %14 = tt.load %arg6 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %15 = arith.muli %10, %c256_i32 : i32
(BenchmarkWorker pid=726) %16 = arith.cmpi sge, %15, %14 : i32
(BenchmarkWorker pid=726) cf.cond_br %16, ^bb1, ^bb2
(BenchmarkWorker pid=726) ^bb1: // pred: ^bb0
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb2: // pred: ^bb0
(BenchmarkWorker pid=726) %17 = arith.extsi %15 : i32 to i64
(BenchmarkWorker pid=726) %18 = tt.splat %17 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %19 = arith.addi %18, %13 : tensor<256xi64>
(BenchmarkWorker pid=726) %20 = tt.splat %arg4 : !tt.ptr<i32> -> tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %21 = tt.addptr %20, %19 : tensor<256x!tt.ptr<i32>>, tensor<256xi64>
(BenchmarkWorker pid=726) %22 = tt.load %21 : tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %23 = arith.extsi %22 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %24 = arith.extsi %arg10 : i32 to i64
(BenchmarkWorker pid=726) %25 = tt.splat %24 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %26 = arith.cmpi slt, %23, %25 : tensor<256xi64>
(BenchmarkWorker pid=726) %27 = tt.addptr %arg5, %10 : !tt.ptr<i32>, i32
(BenchmarkWorker pid=726) %28 = tt.load %27 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %29 = arith.extsi %28 : i32 to i64
(BenchmarkWorker pid=726) %30 = arith.cmpi eq, %29, %c-1_i64 : i64
(BenchmarkWorker pid=726) cf.cond_br %30, ^bb3, ^bb4
(BenchmarkWorker pid=726) ^bb3: // pred: ^bb2
(BenchmarkWorker pid=726) %31 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %32 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %33 = tt.splat %31 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %34 = arith.addi %33, %32 : tensor<64xi32>
(BenchmarkWorker pid=726) %35 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %36 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %37 = tt.splat %36 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %38 = arith.muli %37, %35 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %39 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %40 = tt.addptr %39, %38 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %41 = tt.expand_dims %34 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %42 = tt.broadcast %40 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %43 = tt.broadcast %41 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %44 = tt.addptr %42, %43 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %45 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %46 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %47 = arith.cmpi slt, %41, %46 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %48 = tt.broadcast %45 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %49 = tt.broadcast %47 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %50 = arith.andi %48, %49 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %44, %cst_0, %50 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb4: // pred: ^bb2
(BenchmarkWorker pid=726) %51 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %52 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %53 = arith.extsi %52 : tensor<64xi32> to tensor<64xi64>
(BenchmarkWorker pid=726) %54 = arith.extsi %51 : i32 to i64
(BenchmarkWorker pid=726) %55 = tt.splat %54 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %56 = arith.addi %55, %53 : tensor<64xi64>
(BenchmarkWorker pid=726) %57 = arith.extsi %arg7 : i32 to i64
(BenchmarkWorker pid=726) %58 = tt.splat %57 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %59 = arith.remsi %56, %58 : tensor<64xi64>
(BenchmarkWorker pid=726) %60 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %61 = arith.divsi %60, %cst_4 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %62 = arith.extsi %arg11 : i32 to i64
(BenchmarkWorker pid=726) %63 = tt.splat %62 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %64 = arith.muli %61, %63 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %65 = tt.expand_dims %52 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %66 = arith.extsi %65 : tensor<1x64xi32> to tensor<1x64xi64>
(BenchmarkWorker pid=726) %67 = tt.broadcast %64 : tensor<256x1xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %68 = tt.broadcast %66 : tensor<1x64xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %69 = arith.addi %67, %68 : tensor<256x64xi64>
(BenchmarkWorker pid=726) %70 = tt.splat %arg0 : !tt.ptr<bf16> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %71 = tt.addptr %70, %69 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi64>
(BenchmarkWorker pid=726) %72 = arith.extsi %arg12 : i32 to i64
(BenchmarkWorker pid=726) %73 = arith.muli %29, %72 : i64
(BenchmarkWorker pid=726) %74 = tt.addptr %arg1, %73 : !tt.ptr<bf16>, i64
(BenchmarkWorker pid=726) %75 = tt.expand_dims %52 {axis = 1 : i32} : tensor<64xi32> -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %76 = tt.expand_dims %59 {axis = 0 : i32} : tensor<64xi64> -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %77 = arith.extsi %arg13 : i32 to i64
(BenchmarkWorker pid=726) %78 = tt.splat %77 : i64 -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %79 = arith.muli %76, %78 : tensor<1x64xi64>
(BenchmarkWorker pid=726) %80 = arith.extsi %75 : tensor<64x1xi32> to tensor<64x1xi64>
(BenchmarkWorker pid=726) %81 = tt.broadcast %80 : tensor<64x1xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %82 = tt.broadcast %79 : tensor<1x64xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %83 = arith.addi %81, %82 : tensor<64x64xi64>
(BenchmarkWorker pid=726) %84 = tt.splat %74 : !tt.ptr<bf16> -> tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %85 = tt.addptr %84, %83 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi64>
(BenchmarkWorker pid=726) %86 = arith.addi %arg8, %c63_i32 : i32
(BenchmarkWorker pid=726) %87 = arith.divsi %86, %c64_i32 : i32
(BenchmarkWorker pid=726) %88:3 = scf.for %arg22 = %c0_i32 to %87 step %c1_i32 iter_args(%arg23 = %71, %arg24 = %85, %arg25 = %cst_3) -> (tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>) : i32 {
(BenchmarkWorker pid=726) %107 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %108 = arith.muli %arg22, %c64_i32 : i32
(BenchmarkWorker pid=726) %109 = arith.subi %arg8, %108 : i32
(BenchmarkWorker pid=726) %110 = tt.splat %109 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %111 = arith.cmpi slt, %65, %110 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %112 = tt.broadcast %107 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %113 = tt.broadcast %111 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %114 = arith.andi %112, %113 : tensor<256x64xi1>
(BenchmarkWorker pid=726) %115 = tt.load %arg23, %114, %cst_0 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %116 = tt.splat %109 : i32 -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %117 = arith.cmpi slt, %75, %116 : tensor<64x1xi32>
(BenchmarkWorker pid=726) %118 = tt.broadcast %117 : tensor<64x1xi1> -> tensor<64x64xi1>
(BenchmarkWorker pid=726) %119 = tt.load %arg24, %118, %cst : tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %120 = tt.dot %115, %119, %arg25, inputPrecision = tf32 : tensor<256x64xbf16> * tensor<64x64xbf16> -> tensor<256x64xf32>
(BenchmarkWorker pid=726) %121 = tt.addptr %arg23, %cst_2 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %122 = tt.addptr %arg24, %cst_1 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi32>
(BenchmarkWorker pid=726) scf.yield %121, %122, %120 : tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) %89 = arith.truncf %88#2 : tensor<256x64xf32> to tensor<256x64xbf16>
(BenchmarkWorker pid=726) %90 = tt.splat %51 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %91 = arith.addi %90, %52 : tensor<64xi32>
(BenchmarkWorker pid=726) %92 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %93 = tt.splat %92 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %94 = arith.muli %93, %60 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %95 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %96 = tt.addptr %95, %94 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %97 = tt.expand_dims %91 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %98 = tt.broadcast %96 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %99 = tt.broadcast %97 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %100 = tt.addptr %98, %99 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %101 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %102 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %103 = arith.cmpi slt, %97, %102 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %104 = tt.broadcast %101 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %105 = tt.broadcast %103 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %106 = arith.andi %104, %105 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %100, %89, %106 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726)
(BenchmarkWorker pid=726) {-#
(BenchmarkWorker pid=726) external_resources: {
(BenchmarkWorker pid=726) mlir_reproducer: {
(BenchmarkWorker pid=726) pipeline: "builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:90 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, tritongpu-combine-tensor-select-and-if, nvgpu-warp-specialization{dump-intermediate-steps=false num-stages=2}, tritongpu-assign-latencies{num-stages=2}, tritongpu-schedule-loops, tritongpu-pipeline{dump-intermediate-steps=false num-stages=2}, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=90}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})",
(BenchmarkWorker pid=726) disable_threading: true,
(BenchmarkWorker pid=726) verify_each: true
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) #-}
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: error: Failures have been detected while processing an MLIR pass pipeline
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: note: Pipeline failed while executing [`TritonGPURemoveLayoutConversions` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`
(pid=726) : 98%|█████████▊| 1.89k/1.92k [26:58<00:26, 1.17it/s]
(pid=726) : 97%|█████████▋| 1.85k/1.92k [13:24<00:28, 2.31it/s]
(pid=726) : 85%|████████▍ | 1.63k/1.92k [11:59<02:10, 2.26it/s]
(pid=726) : 0%| | 1.00/1.92k [00:02<1:23:09, 2.60s/it]
(pid=796) : 98%|█████████▊| 1.89k/1.92k [26:57<00:26, 1.17it/s]
(pid=796) : 96%|█████████▌| 1.84k/1.92k [13:24<00:34, 2.29it/s]Code Example
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (aarch64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.13 (main, Mar 4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-6.8.0-1047-nvidia-64k-aarch64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA GH200 144G HBM3e
GPU 1: NVIDIA GH200 144G HBM3e
Nvidia driver version : 595.45.04
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 144
On-line CPU(s) list: 0-143
Vendor ID: ARM
Model name: Neoverse-V2
Model: 0
Thread(s) per core: 1
Core(s) per cluster: 72
Socket(s): -
Cluster(s): 2
Stepping: r0p0
Frequency boost: disabled
CPU max MHz: 3474.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti
L1d cache: 9 MiB (144 instances)
L1i cache: 9 MiB (144 instances)
L2 cache: 144 MiB (144 instances)
L3 cache: 228 MiB (2 instances)
NUMA node(s): 18
NUMA node0 CPU(s): 0-71
NUMA node1 CPU(s): 72-143
NUMA node2 CPU(s):
NUMA node3 CPU(s):
NUMA node4 CPU(s):
NUMA node5 CPU(s):
NUMA node6 CPU(s):
NUMA node7 CPU(s):
NUMA node8 CPU(s):
NUMA node9 CPU(s):
NUMA node10 CPU(s):
NUMA node11 CPU(s):
NUMA node12 CPU(s):
NUMA node13 CPU(s):
NUMA node14 CPU(s):
NUMA node15 CPU(s):
NUMA node16 CPU(s):
NUMA node17 CPU(s):
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Mitigation; CSV2, BHB
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.4
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu129
[pip3] torchvision==0.25.0+cu129
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.17.1
vLLM Build Flags:
CUDA Archs: 8.7 8.9 9.0 10.0+PTX 12.0; ROCm: Disabled
GPU Topology:
GPU0 GPU1 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NODE 0-71 0 2
GPU1 NV18 X SYS 72-143 1 10
NIC0 NODE SYS X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: roceP2p1s0f0
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.9 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=8.7 8.9 9.0 10.0+PTX 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/aarch64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
---
docker run --gpus all --ipc=host \
--entrypoint /bin/bash \
-it vllm/vllm-openai:v0.17.1-aarch64
---
docker run --gpus all --ipc=host \
--entrypoint /bin/bash \
-it vllm/vllm-openai:v0.17.1-aarch64-cu130
---
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"
Namespace(model='Qwen/Qwen3-Coder-Next-FP8', tp_size=1, enable_expert_parallel=False, dtype='auto', use_deep_gemm=False, save_dir='/mnt/fused_moe_configs/', seed=0, batch_size=None, tune=True, trust_remote_code=False, model_prefix=None)
2026-03-16 12:35:25,013 INFO worker.py:2013 -- Started a local Ray instance.
/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
warnings.warn(
Start tuning over 1920 configurations...
(BenchmarkWorker pid=726) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=1
(BenchmarkWorker pid=796) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=2
(BenchmarkWorker pid=726) Mon Mar 16 12:50:29 2026] Completed tuning for batch_size=4
(pid=726) : 85%|██████████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.63k/1.92k [11:56<01:02, 4.73it/s]Traceback (most recent call last):██████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.84k/1.92k [13:15<02:00, 1.52s/it]
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 1041, in <module>
main(args)
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 951, in main
configs = _distribute(
^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 927, in _distribute
return ray.get(outputs)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 2981, in get
values, debugger_breakpoint = worker.get_objects(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 1012, in get_objects
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RuntimeError): ray::BenchmarkWorker.tune() (pid=726, ip=XXX, actor_id=5f66c1327f7a25a3b8fd359101000000, repr=<benchmark_moe.BenchmarkWorker object at 0xfc7a5e152750>)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 632, in tune
kernel_time = benchmark_config(
^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 306, in benchmark_config
run()
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 295, in run
return fused_experts(
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1578, in fused_experts
return dispatch_fused_experts_func(inplace)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1548, in torch_vllm_outplace_fused_experts
return torch.ops.vllm.outplace_fused_experts(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 109, in __torch_function__
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1476, in outplace_fused_experts
return fused_experts_impl(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1855, in fused_experts_impl
dispatch_fused_moe_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 918, in dispatch_fused_moe_kernel
invoke_fused_moe_triton_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 794, in invoke_fused_moe_triton_kernel
fused_moe_kernel[grid](
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py", line 324, in compile
next_module = compile_ir(module, metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 541, in <lambda>
stages["ttgir"] = lambda src, metadata: self.make_ttgir(src, metadata, options, capability)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 316, in make_ttgir
pm.run(mod, 'make_ttgir')
RuntimeError: PassManager::run failed
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:574:24: error: operand #0 does not dominate this use
(BenchmarkWorker pid=726) c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:508:28: note: operand defined here (op in a child region)
(BenchmarkWorker pid=726) mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) module {
(BenchmarkWorker pid=726) tt.func public @fused_moe_kernel(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32}, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}, %arg18: i32 {tt.divisibility = 16 : i32}, %arg19: i32 {tt.divisibility = 16 : i32}, %arg20: i32 {tt.divisibility = 16 : i32}, %arg21: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
(BenchmarkWorker pid=726) %c63_i32 = arith.constant 63 : i32
(BenchmarkWorker pid=726) %c255_i32 = arith.constant 255 : i32
(BenchmarkWorker pid=726) %cst = arith.constant dense<0.000000e+00> : tensor<64x64xbf16>
(BenchmarkWorker pid=726) %cst_0 = arith.constant dense<0.000000e+00> : tensor<256x64xbf16>
(BenchmarkWorker pid=726) %c0_i32 = arith.constant 0 : i32
(BenchmarkWorker pid=726) %c-1_i64 = arith.constant -1 : i64
(BenchmarkWorker pid=726) %cst_1 = arith.constant dense<64> : tensor<64x64xi32>
(BenchmarkWorker pid=726) %cst_2 = arith.constant dense<64> : tensor<256x64xi32>
(BenchmarkWorker pid=726) %cst_3 = arith.constant dense<0.000000e+00> : tensor<256x64xf32>
(BenchmarkWorker pid=726) %cst_4 = arith.constant dense<10> : tensor<256x1xi64>
(BenchmarkWorker pid=726) %c64_i32 = arith.constant 64 : i32
(BenchmarkWorker pid=726) %c256_i32 = arith.constant 256 : i32
(BenchmarkWorker pid=726) %c1_i32 = arith.constant 1 : i32
(BenchmarkWorker pid=726) %0 = tt.get_program_id x : i32
(BenchmarkWorker pid=726) %1 = arith.addi %arg9, %c255_i32 : i32
(BenchmarkWorker pid=726) %2 = arith.divsi %1, %c256_i32 : i32
(BenchmarkWorker pid=726) %3 = arith.addi %arg7, %c63_i32 : i32
(BenchmarkWorker pid=726) %4 = arith.divsi %3, %c64_i32 : i32
(BenchmarkWorker pid=726) %5 = arith.divsi %0, %4 : i32
(BenchmarkWorker pid=726) %6 = arith.subi %2, %5 : i32
(BenchmarkWorker pid=726) %7 = arith.minsi %6, %c1_i32 : i32
(BenchmarkWorker pid=726) %8 = arith.remsi %0, %4 : i32
(BenchmarkWorker pid=726) %9 = arith.remsi %8, %7 : i32
(BenchmarkWorker pid=726) %10 = arith.addi %5, %9 : i32
(BenchmarkWorker pid=726) %11 = arith.divsi %8, %7 : i32
(BenchmarkWorker pid=726) %12 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>
(BenchmarkWorker pid=726) %13 = arith.extsi %12 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %14 = tt.load %arg6 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %15 = arith.muli %10, %c256_i32 : i32
(BenchmarkWorker pid=726) %16 = arith.cmpi sge, %15, %14 : i32
(BenchmarkWorker pid=726) cf.cond_br %16, ^bb1, ^bb2
(BenchmarkWorker pid=726) ^bb1: // pred: ^bb0
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb2: // pred: ^bb0
(BenchmarkWorker pid=726) %17 = arith.extsi %15 : i32 to i64
(BenchmarkWorker pid=726) %18 = tt.splat %17 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %19 = arith.addi %18, %13 : tensor<256xi64>
(BenchmarkWorker pid=726) %20 = tt.splat %arg4 : !tt.ptr<i32> -> tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %21 = tt.addptr %20, %19 : tensor<256x!tt.ptr<i32>>, tensor<256xi64>
(BenchmarkWorker pid=726) %22 = tt.load %21 : tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %23 = arith.extsi %22 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %24 = arith.extsi %arg10 : i32 to i64
(BenchmarkWorker pid=726) %25 = tt.splat %24 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %26 = arith.cmpi slt, %23, %25 : tensor<256xi64>
(BenchmarkWorker pid=726) %27 = tt.addptr %arg5, %10 : !tt.ptr<i32>, i32
(BenchmarkWorker pid=726) %28 = tt.load %27 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %29 = arith.extsi %28 : i32 to i64
(BenchmarkWorker pid=726) %30 = arith.cmpi eq, %29, %c-1_i64 : i64
(BenchmarkWorker pid=726) cf.cond_br %30, ^bb3, ^bb4
(BenchmarkWorker pid=726) ^bb3: // pred: ^bb2
(BenchmarkWorker pid=726) %31 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %32 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %33 = tt.splat %31 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %34 = arith.addi %33, %32 : tensor<64xi32>
(BenchmarkWorker pid=726) %35 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %36 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %37 = tt.splat %36 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %38 = arith.muli %37, %35 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %39 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %40 = tt.addptr %39, %38 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %41 = tt.expand_dims %34 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %42 = tt.broadcast %40 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %43 = tt.broadcast %41 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %44 = tt.addptr %42, %43 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %45 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %46 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %47 = arith.cmpi slt, %41, %46 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %48 = tt.broadcast %45 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %49 = tt.broadcast %47 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %50 = arith.andi %48, %49 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %44, %cst_0, %50 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb4: // pred: ^bb2
(BenchmarkWorker pid=726) %51 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %52 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %53 = arith.extsi %52 : tensor<64xi32> to tensor<64xi64>
(BenchmarkWorker pid=726) %54 = arith.extsi %51 : i32 to i64
(BenchmarkWorker pid=726) %55 = tt.splat %54 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %56 = arith.addi %55, %53 : tensor<64xi64>
(BenchmarkWorker pid=726) %57 = arith.extsi %arg7 : i32 to i64
(BenchmarkWorker pid=726) %58 = tt.splat %57 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %59 = arith.remsi %56, %58 : tensor<64xi64>
(BenchmarkWorker pid=726) %60 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %61 = arith.divsi %60, %cst_4 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %62 = arith.extsi %arg11 : i32 to i64
(BenchmarkWorker pid=726) %63 = tt.splat %62 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %64 = arith.muli %61, %63 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %65 = tt.expand_dims %52 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %66 = arith.extsi %65 : tensor<1x64xi32> to tensor<1x64xi64>
(BenchmarkWorker pid=726) %67 = tt.broadcast %64 : tensor<256x1xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %68 = tt.broadcast %66 : tensor<1x64xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %69 = arith.addi %67, %68 : tensor<256x64xi64>
(BenchmarkWorker pid=726) %70 = tt.splat %arg0 : !tt.ptr<bf16> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %71 = tt.addptr %70, %69 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi64>
(BenchmarkWorker pid=726) %72 = arith.extsi %arg12 : i32 to i64
(BenchmarkWorker pid=726) %73 = arith.muli %29, %72 : i64
(BenchmarkWorker pid=726) %74 = tt.addptr %arg1, %73 : !tt.ptr<bf16>, i64
(BenchmarkWorker pid=726) %75 = tt.expand_dims %52 {axis = 1 : i32} : tensor<64xi32> -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %76 = tt.expand_dims %59 {axis = 0 : i32} : tensor<64xi64> -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %77 = arith.extsi %arg13 : i32 to i64
(BenchmarkWorker pid=726) %78 = tt.splat %77 : i64 -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %79 = arith.muli %76, %78 : tensor<1x64xi64>
(BenchmarkWorker pid=726) %80 = arith.extsi %75 : tensor<64x1xi32> to tensor<64x1xi64>
(BenchmarkWorker pid=726) %81 = tt.broadcast %80 : tensor<64x1xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %82 = tt.broadcast %79 : tensor<1x64xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %83 = arith.addi %81, %82 : tensor<64x64xi64>
(BenchmarkWorker pid=726) %84 = tt.splat %74 : !tt.ptr<bf16> -> tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %85 = tt.addptr %84, %83 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi64>
(BenchmarkWorker pid=726) %86 = arith.addi %arg8, %c63_i32 : i32
(BenchmarkWorker pid=726) %87 = arith.divsi %86, %c64_i32 : i32
(BenchmarkWorker pid=726) %88:3 = scf.for %arg22 = %c0_i32 to %87 step %c1_i32 iter_args(%arg23 = %71, %arg24 = %85, %arg25 = %cst_3) -> (tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>) : i32 {
(BenchmarkWorker pid=726) %107 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %108 = arith.muli %arg22, %c64_i32 : i32
(BenchmarkWorker pid=726) %109 = arith.subi %arg8, %108 : i32
(BenchmarkWorker pid=726) %110 = tt.splat %109 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %111 = arith.cmpi slt, %65, %110 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %112 = tt.broadcast %107 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %113 = tt.broadcast %111 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %114 = arith.andi %112, %113 : tensor<256x64xi1>
(BenchmarkWorker pid=726) %115 = tt.load %arg23, %114, %cst_0 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %116 = tt.splat %109 : i32 -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %117 = arith.cmpi slt, %75, %116 : tensor<64x1xi32>
(BenchmarkWorker pid=726) %118 = tt.broadcast %117 : tensor<64x1xi1> -> tensor<64x64xi1>
(BenchmarkWorker pid=726) %119 = tt.load %arg24, %118, %cst : tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %120 = tt.dot %115, %119, %arg25, inputPrecision = tf32 : tensor<256x64xbf16> * tensor<64x64xbf16> -> tensor<256x64xf32>
(BenchmarkWorker pid=726) %121 = tt.addptr %arg23, %cst_2 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %122 = tt.addptr %arg24, %cst_1 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi32>
(BenchmarkWorker pid=726) scf.yield %121, %122, %120 : tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) %89 = arith.truncf %88#2 : tensor<256x64xf32> to tensor<256x64xbf16>
(BenchmarkWorker pid=726) %90 = tt.splat %51 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %91 = arith.addi %90, %52 : tensor<64xi32>
(BenchmarkWorker pid=726) %92 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %93 = tt.splat %92 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %94 = arith.muli %93, %60 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %95 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %96 = tt.addptr %95, %94 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %97 = tt.expand_dims %91 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %98 = tt.broadcast %96 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %99 = tt.broadcast %97 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %100 = tt.addptr %98, %99 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %101 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %102 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %103 = arith.cmpi slt, %97, %102 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %104 = tt.broadcast %101 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %105 = tt.broadcast %103 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %106 = arith.andi %104, %105 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %100, %89, %106 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726)
(BenchmarkWorker pid=726) {-#
(BenchmarkWorker pid=726) external_resources: {
(BenchmarkWorker pid=726) mlir_reproducer: {
(BenchmarkWorker pid=726) pipeline: "builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:90 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, tritongpu-combine-tensor-select-and-if, nvgpu-warp-specialization{dump-intermediate-steps=false num-stages=2}, tritongpu-assign-latencies{num-stages=2}, tritongpu-schedule-loops, tritongpu-pipeline{dump-intermediate-steps=false num-stages=2}, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=90}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})",
(BenchmarkWorker pid=726) disable_threading: true,
(BenchmarkWorker pid=726) verify_each: true
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) #-}
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: error: Failures have been detected while processing an MLIR pass pipeline
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: note: Pipeline failed while executing [`TritonGPURemoveLayoutConversions` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`
(pid=726) : 98%|█████████▊| 1.89k/1.92k [26:58<00:26, 1.17it/s]
(pid=726) : 97%|█████████▋| 1.85k/1.92k [13:24<00:28, 2.31it/s]
(pid=726) : 85%|████████▍ | 1.63k/1.92k [11:59<02:10, 2.26it/s]
(pid=726) : 0%| | 1.00/1.92k [00:02<1:23:09, 2.60s/it]
(pid=796) : 98%|█████████▊| 1.89k/1.92k [26:57<00:26, 1.17it/s]
(pid=796) : 96%|█████████▌| 1.84k/1.92k [13:24<00:34, 2.29it/s]RAW_BUFFERClick to expand / collapse
Your current environment
<details> <summary>The output of <code>python collect_env.py</code></summary>==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (aarch64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.13 (main, Mar 4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-6.8.0-1047-nvidia-64k-aarch64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA GH200 144G HBM3e
GPU 1: NVIDIA GH200 144G HBM3e
Nvidia driver version : 595.45.04
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 144
On-line CPU(s) list: 0-143
Vendor ID: ARM
Model name: Neoverse-V2
Model: 0
Thread(s) per core: 1
Core(s) per cluster: 72
Socket(s): -
Cluster(s): 2
Stepping: r0p0
Frequency boost: disabled
CPU max MHz: 3474.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti
L1d cache: 9 MiB (144 instances)
L1i cache: 9 MiB (144 instances)
L2 cache: 144 MiB (144 instances)
L3 cache: 228 MiB (2 instances)
NUMA node(s): 18
NUMA node0 CPU(s): 0-71
NUMA node1 CPU(s): 72-143
NUMA node2 CPU(s):
NUMA node3 CPU(s):
NUMA node4 CPU(s):
NUMA node5 CPU(s):
NUMA node6 CPU(s):
NUMA node7 CPU(s):
NUMA node8 CPU(s):
NUMA node9 CPU(s):
NUMA node10 CPU(s):
NUMA node11 CPU(s):
NUMA node12 CPU(s):
NUMA node13 CPU(s):
NUMA node14 CPU(s):
NUMA node15 CPU(s):
NUMA node16 CPU(s):
NUMA node17 CPU(s):
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Mitigation; CSV2, BHB
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.4
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu129
[pip3] torchvision==0.25.0+cu129
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.17.1
vLLM Build Flags:
CUDA Archs: 8.7 8.9 9.0 10.0+PTX 12.0; ROCm: Disabled
GPU Topology:
GPU0 GPU1 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NODE 0-71 0 2
GPU1 NV18 X SYS 72-143 1 10
NIC0 NODE SYS X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: roceP2p1s0f0
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.9 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=8.7 8.9 9.0 10.0+PTX 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/aarch64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root🐛 Describe the bug
To reproduce:
docker run --gpus all --ipc=host \
--entrypoint /bin/bash \
-it vllm/vllm-openai:v0.17.1-aarch64(from which the above environment was collected)
Or
docker run --gpus all --ipc=host \
--entrypoint /bin/bash \
-it vllm/vllm-openai:v0.17.1-aarch64-cu130(both docker images, cu130 or not, result in the exact same error)
Then :
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"
Results in:
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"
Namespace(model='Qwen/Qwen3-Coder-Next-FP8', tp_size=1, enable_expert_parallel=False, dtype='auto', use_deep_gemm=False, save_dir='/mnt/fused_moe_configs/', seed=0, batch_size=None, tune=True, trust_remote_code=False, model_prefix=None)
2026-03-16 12:35:25,013 INFO worker.py:2013 -- Started a local Ray instance.
/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
warnings.warn(
Start tuning over 1920 configurations...
(BenchmarkWorker pid=726) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=1
(BenchmarkWorker pid=796) Mon Mar 16 12:49:04 2026] Completed tuning for batch_size=2
(BenchmarkWorker pid=726) Mon Mar 16 12:50:29 2026] Completed tuning for batch_size=4
(pid=726) : 85%|██████████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.63k/1.92k [11:56<01:02, 4.73it/s]Traceback (most recent call last):██████████████████████████████████████████████████████████████████████████████████████████████▏ | 1.84k/1.92k [13:15<02:00, 1.52s/it]
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 1041, in <module>
main(args)
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 951, in main
configs = _distribute(
^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 927, in _distribute
return ray.get(outputs)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 2981, in get
values, debugger_breakpoint = worker.get_objects(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py", line 1012, in get_objects
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RuntimeError): ray::BenchmarkWorker.tune() (pid=726, ip=XXX, actor_id=5f66c1327f7a25a3b8fd359101000000, repr=<benchmark_moe.BenchmarkWorker object at 0xfc7a5e152750>)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 632, in tune
kernel_time = benchmark_config(
^^^^^^^^^^^^^^^^^
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 306, in benchmark_config
run()
File "/vllm-workspace/benchmarks/kernels/benchmark_moe.py", line 295, in run
return fused_experts(
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1578, in fused_experts
return dispatch_fused_experts_func(inplace)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1548, in torch_vllm_outplace_fused_experts
return torch.ops.vllm.outplace_fused_experts(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 109, in __torch_function__
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1209, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1476, in outplace_fused_experts
return fused_experts_impl(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 1855, in fused_experts_impl
dispatch_fused_moe_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 918, in dispatch_fused_moe_kernel
invoke_fused_moe_triton_kernel(
File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py", line 794, in invoke_fused_moe_triton_kernel
fused_moe_kernel[grid](
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/compiler/compiler.py", line 324, in compile
next_module = compile_ir(module, metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 541, in <lambda>
stages["ttgir"] = lambda src, metadata: self.make_ttgir(src, metadata, options, capability)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/compiler.py", line 316, in make_ttgir
pm.run(mod, 'make_ttgir')
RuntimeError: PassManager::run failed
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:574:24: error: operand #0 does not dominate this use
(BenchmarkWorker pid=726) c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:508:28: note: operand defined here (op in a child region)
(BenchmarkWorker pid=726) mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
(BenchmarkWorker pid=726) ^
(BenchmarkWorker pid=726) module {
(BenchmarkWorker pid=726) tt.func public @fused_moe_kernel(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<i32> {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32}, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}, %arg18: i32 {tt.divisibility = 16 : i32}, %arg19: i32 {tt.divisibility = 16 : i32}, %arg20: i32 {tt.divisibility = 16 : i32}, %arg21: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
(BenchmarkWorker pid=726) %c63_i32 = arith.constant 63 : i32
(BenchmarkWorker pid=726) %c255_i32 = arith.constant 255 : i32
(BenchmarkWorker pid=726) %cst = arith.constant dense<0.000000e+00> : tensor<64x64xbf16>
(BenchmarkWorker pid=726) %cst_0 = arith.constant dense<0.000000e+00> : tensor<256x64xbf16>
(BenchmarkWorker pid=726) %c0_i32 = arith.constant 0 : i32
(BenchmarkWorker pid=726) %c-1_i64 = arith.constant -1 : i64
(BenchmarkWorker pid=726) %cst_1 = arith.constant dense<64> : tensor<64x64xi32>
(BenchmarkWorker pid=726) %cst_2 = arith.constant dense<64> : tensor<256x64xi32>
(BenchmarkWorker pid=726) %cst_3 = arith.constant dense<0.000000e+00> : tensor<256x64xf32>
(BenchmarkWorker pid=726) %cst_4 = arith.constant dense<10> : tensor<256x1xi64>
(BenchmarkWorker pid=726) %c64_i32 = arith.constant 64 : i32
(BenchmarkWorker pid=726) %c256_i32 = arith.constant 256 : i32
(BenchmarkWorker pid=726) %c1_i32 = arith.constant 1 : i32
(BenchmarkWorker pid=726) %0 = tt.get_program_id x : i32
(BenchmarkWorker pid=726) %1 = arith.addi %arg9, %c255_i32 : i32
(BenchmarkWorker pid=726) %2 = arith.divsi %1, %c256_i32 : i32
(BenchmarkWorker pid=726) %3 = arith.addi %arg7, %c63_i32 : i32
(BenchmarkWorker pid=726) %4 = arith.divsi %3, %c64_i32 : i32
(BenchmarkWorker pid=726) %5 = arith.divsi %0, %4 : i32
(BenchmarkWorker pid=726) %6 = arith.subi %2, %5 : i32
(BenchmarkWorker pid=726) %7 = arith.minsi %6, %c1_i32 : i32
(BenchmarkWorker pid=726) %8 = arith.remsi %0, %4 : i32
(BenchmarkWorker pid=726) %9 = arith.remsi %8, %7 : i32
(BenchmarkWorker pid=726) %10 = arith.addi %5, %9 : i32
(BenchmarkWorker pid=726) %11 = arith.divsi %8, %7 : i32
(BenchmarkWorker pid=726) %12 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>
(BenchmarkWorker pid=726) %13 = arith.extsi %12 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %14 = tt.load %arg6 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %15 = arith.muli %10, %c256_i32 : i32
(BenchmarkWorker pid=726) %16 = arith.cmpi sge, %15, %14 : i32
(BenchmarkWorker pid=726) cf.cond_br %16, ^bb1, ^bb2
(BenchmarkWorker pid=726) ^bb1: // pred: ^bb0
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb2: // pred: ^bb0
(BenchmarkWorker pid=726) %17 = arith.extsi %15 : i32 to i64
(BenchmarkWorker pid=726) %18 = tt.splat %17 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %19 = arith.addi %18, %13 : tensor<256xi64>
(BenchmarkWorker pid=726) %20 = tt.splat %arg4 : !tt.ptr<i32> -> tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %21 = tt.addptr %20, %19 : tensor<256x!tt.ptr<i32>>, tensor<256xi64>
(BenchmarkWorker pid=726) %22 = tt.load %21 : tensor<256x!tt.ptr<i32>>
(BenchmarkWorker pid=726) %23 = arith.extsi %22 : tensor<256xi32> to tensor<256xi64>
(BenchmarkWorker pid=726) %24 = arith.extsi %arg10 : i32 to i64
(BenchmarkWorker pid=726) %25 = tt.splat %24 : i64 -> tensor<256xi64>
(BenchmarkWorker pid=726) %26 = arith.cmpi slt, %23, %25 : tensor<256xi64>
(BenchmarkWorker pid=726) %27 = tt.addptr %arg5, %10 : !tt.ptr<i32>, i32
(BenchmarkWorker pid=726) %28 = tt.load %27 : !tt.ptr<i32>
(BenchmarkWorker pid=726) %29 = arith.extsi %28 : i32 to i64
(BenchmarkWorker pid=726) %30 = arith.cmpi eq, %29, %c-1_i64 : i64
(BenchmarkWorker pid=726) cf.cond_br %30, ^bb3, ^bb4
(BenchmarkWorker pid=726) ^bb3: // pred: ^bb2
(BenchmarkWorker pid=726) %31 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %32 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %33 = tt.splat %31 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %34 = arith.addi %33, %32 : tensor<64xi32>
(BenchmarkWorker pid=726) %35 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %36 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %37 = tt.splat %36 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %38 = arith.muli %37, %35 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %39 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %40 = tt.addptr %39, %38 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %41 = tt.expand_dims %34 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %42 = tt.broadcast %40 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %43 = tt.broadcast %41 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %44 = tt.addptr %42, %43 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %45 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %46 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %47 = arith.cmpi slt, %41, %46 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %48 = tt.broadcast %45 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %49 = tt.broadcast %47 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %50 = arith.andi %48, %49 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %44, %cst_0, %50 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) ^bb4: // pred: ^bb2
(BenchmarkWorker pid=726) %51 = arith.muli %11, %c64_i32 : i32
(BenchmarkWorker pid=726) %52 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
(BenchmarkWorker pid=726) %53 = arith.extsi %52 : tensor<64xi32> to tensor<64xi64>
(BenchmarkWorker pid=726) %54 = arith.extsi %51 : i32 to i64
(BenchmarkWorker pid=726) %55 = tt.splat %54 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %56 = arith.addi %55, %53 : tensor<64xi64>
(BenchmarkWorker pid=726) %57 = arith.extsi %arg7 : i32 to i64
(BenchmarkWorker pid=726) %58 = tt.splat %57 : i64 -> tensor<64xi64>
(BenchmarkWorker pid=726) %59 = arith.remsi %56, %58 : tensor<64xi64>
(BenchmarkWorker pid=726) %60 = tt.expand_dims %23 {axis = 1 : i32} : tensor<256xi64> -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %61 = arith.divsi %60, %cst_4 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %62 = arith.extsi %arg11 : i32 to i64
(BenchmarkWorker pid=726) %63 = tt.splat %62 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %64 = arith.muli %61, %63 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %65 = tt.expand_dims %52 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %66 = arith.extsi %65 : tensor<1x64xi32> to tensor<1x64xi64>
(BenchmarkWorker pid=726) %67 = tt.broadcast %64 : tensor<256x1xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %68 = tt.broadcast %66 : tensor<1x64xi64> -> tensor<256x64xi64>
(BenchmarkWorker pid=726) %69 = arith.addi %67, %68 : tensor<256x64xi64>
(BenchmarkWorker pid=726) %70 = tt.splat %arg0 : !tt.ptr<bf16> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %71 = tt.addptr %70, %69 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi64>
(BenchmarkWorker pid=726) %72 = arith.extsi %arg12 : i32 to i64
(BenchmarkWorker pid=726) %73 = arith.muli %29, %72 : i64
(BenchmarkWorker pid=726) %74 = tt.addptr %arg1, %73 : !tt.ptr<bf16>, i64
(BenchmarkWorker pid=726) %75 = tt.expand_dims %52 {axis = 1 : i32} : tensor<64xi32> -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %76 = tt.expand_dims %59 {axis = 0 : i32} : tensor<64xi64> -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %77 = arith.extsi %arg13 : i32 to i64
(BenchmarkWorker pid=726) %78 = tt.splat %77 : i64 -> tensor<1x64xi64>
(BenchmarkWorker pid=726) %79 = arith.muli %76, %78 : tensor<1x64xi64>
(BenchmarkWorker pid=726) %80 = arith.extsi %75 : tensor<64x1xi32> to tensor<64x1xi64>
(BenchmarkWorker pid=726) %81 = tt.broadcast %80 : tensor<64x1xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %82 = tt.broadcast %79 : tensor<1x64xi64> -> tensor<64x64xi64>
(BenchmarkWorker pid=726) %83 = arith.addi %81, %82 : tensor<64x64xi64>
(BenchmarkWorker pid=726) %84 = tt.splat %74 : !tt.ptr<bf16> -> tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %85 = tt.addptr %84, %83 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi64>
(BenchmarkWorker pid=726) %86 = arith.addi %arg8, %c63_i32 : i32
(BenchmarkWorker pid=726) %87 = arith.divsi %86, %c64_i32 : i32
(BenchmarkWorker pid=726) %88:3 = scf.for %arg22 = %c0_i32 to %87 step %c1_i32 iter_args(%arg23 = %71, %arg24 = %85, %arg25 = %cst_3) -> (tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>) : i32 {
(BenchmarkWorker pid=726) %107 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %108 = arith.muli %arg22, %c64_i32 : i32
(BenchmarkWorker pid=726) %109 = arith.subi %arg8, %108 : i32
(BenchmarkWorker pid=726) %110 = tt.splat %109 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %111 = arith.cmpi slt, %65, %110 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %112 = tt.broadcast %107 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %113 = tt.broadcast %111 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %114 = arith.andi %112, %113 : tensor<256x64xi1>
(BenchmarkWorker pid=726) %115 = tt.load %arg23, %114, %cst_0 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %116 = tt.splat %109 : i32 -> tensor<64x1xi32>
(BenchmarkWorker pid=726) %117 = arith.cmpi slt, %75, %116 : tensor<64x1xi32>
(BenchmarkWorker pid=726) %118 = tt.broadcast %117 : tensor<64x1xi1> -> tensor<64x64xi1>
(BenchmarkWorker pid=726) %119 = tt.load %arg24, %118, %cst : tensor<64x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %120 = tt.dot %115, %119, %arg25, inputPrecision = tf32 : tensor<256x64xbf16> * tensor<64x64xbf16> -> tensor<256x64xf32>
(BenchmarkWorker pid=726) %121 = tt.addptr %arg23, %cst_2 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %122 = tt.addptr %arg24, %cst_1 : tensor<64x64x!tt.ptr<bf16>>, tensor<64x64xi32>
(BenchmarkWorker pid=726) scf.yield %121, %122, %120 : tensor<256x64x!tt.ptr<bf16>>, tensor<64x64x!tt.ptr<bf16>>, tensor<256x64xf32>
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) %89 = arith.truncf %88#2 : tensor<256x64xf32> to tensor<256x64xbf16>
(BenchmarkWorker pid=726) %90 = tt.splat %51 : i32 -> tensor<64xi32>
(BenchmarkWorker pid=726) %91 = arith.addi %90, %52 : tensor<64xi32>
(BenchmarkWorker pid=726) %92 = arith.extsi %arg14 : i32 to i64
(BenchmarkWorker pid=726) %93 = tt.splat %92 : i64 -> tensor<256x1xi64>
(BenchmarkWorker pid=726) %94 = arith.muli %93, %60 : tensor<256x1xi64>
(BenchmarkWorker pid=726) %95 = tt.splat %arg2 : !tt.ptr<bf16> -> tensor<256x1x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %96 = tt.addptr %95, %94 : tensor<256x1x!tt.ptr<bf16>>, tensor<256x1xi64>
(BenchmarkWorker pid=726) %97 = tt.expand_dims %91 {axis = 0 : i32} : tensor<64xi32> -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %98 = tt.broadcast %96 : tensor<256x1x!tt.ptr<bf16>> -> tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) %99 = tt.broadcast %97 : tensor<1x64xi32> -> tensor<256x64xi32>
(BenchmarkWorker pid=726) %100 = tt.addptr %98, %99 : tensor<256x64x!tt.ptr<bf16>>, tensor<256x64xi32>
(BenchmarkWorker pid=726) %101 = tt.expand_dims %26 {axis = 1 : i32} : tensor<256xi1> -> tensor<256x1xi1>
(BenchmarkWorker pid=726) %102 = tt.splat %arg7 : i32 -> tensor<1x64xi32>
(BenchmarkWorker pid=726) %103 = arith.cmpi slt, %97, %102 : tensor<1x64xi32>
(BenchmarkWorker pid=726) %104 = tt.broadcast %101 : tensor<256x1xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %105 = tt.broadcast %103 : tensor<1x64xi1> -> tensor<256x64xi1>
(BenchmarkWorker pid=726) %106 = arith.andi %104, %105 : tensor<256x64xi1>
(BenchmarkWorker pid=726) tt.store %100, %89, %106 : tensor<256x64x!tt.ptr<bf16>>
(BenchmarkWorker pid=726) tt.return
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726)
(BenchmarkWorker pid=726) {-#
(BenchmarkWorker pid=726) external_resources: {
(BenchmarkWorker pid=726) mlir_reproducer: {
(BenchmarkWorker pid=726) pipeline: "builtin.module(convert-triton-to-tritongpu{enable-source-remat=false num-ctas=1 num-warps=4 target=cuda:90 threads-per-warp=32}, tritongpu-coalesce, tritongpu-F32DotTC{emu-tf32=true}, triton-nvidia-gpu-plan-cta, tritongpu-remove-layout-conversions, tritongpu-optimize-thread-locality, tritongpu-accelerate-matmul, tritongpu-remove-layout-conversions, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, triton-nvidia-optimize-descriptor-encoding, triton-loop-aware-cse, tritongpu-fuse-nested-loops, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-licm, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, tritongpu-combine-tensor-select-and-if, nvgpu-warp-specialization{dump-intermediate-steps=false num-stages=2}, tritongpu-assign-latencies{num-stages=2}, tritongpu-schedule-loops, tritongpu-pipeline{dump-intermediate-steps=false num-stages=2}, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true}, triton-loop-aware-cse, tritongpu-prefetch, tritongpu-optimize-dot-operands{hoist-layout-conversion=true}, tritongpu-coalesce-async-copy, triton-nvidia-optimize-tmem-layouts, triton-nvidia-tma-lowering, tritongpu-remove-layout-conversions, triton-nvidia-interleave-tmem, tritongpu-reduce-data-duplication, tritongpu-reorder-instructions, triton-loop-aware-cse, symbol-dce, triton-nvidia-gpu-fence-insertion{compute-capability=90}, triton-nvidia-mma-lowering, sccp, cse, canonicalize{ max-iterations=10 max-num-rewrites=-1 region-simplify=normal test-convergence=false top-down=true})",
(BenchmarkWorker pid=726) disable_threading: true,
(BenchmarkWorker pid=726) verify_each: true
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) }
(BenchmarkWorker pid=726) #-}
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: error: Failures have been detected while processing an MLIR pass pipeline
(BenchmarkWorker pid=726) /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/fused_moe.py:315:0: note: Pipeline failed while executing [`TritonGPURemoveLayoutConversions` on 'builtin.module' operation]: reproducer generated at `std::errs, please share the reproducer above with Triton project.`
(pid=726) : 98%|█████████▊| 1.89k/1.92k [26:58<00:26, 1.17it/s]
(pid=726) : 97%|█████████▋| 1.85k/1.92k [13:24<00:28, 2.31it/s]
(pid=726) : 85%|████████▍ | 1.63k/1.92k [11:59<02:10, 2.26it/s]
(pid=726) : 0%| | 1.00/1.92k [00:02<1:23:09, 2.60s/it]
(pid=796) : 98%|█████████▊| 1.89k/1.92k [26:57<00:26, 1.17it/s]
(pid=796) : 96%|█████████▌| 1.84k/1.92k [13:24<00:34, 2.29it/s]Before submitting a new issue...
- Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.
extent analysis
Fix Plan
To fix the issue, we need to update the fused_moe.py file to handle the operand dominance error.
- Update the
fused_moe.pyfile: Modify thefused_moe.pyfile to correctly handle the operand dominance error. This can be done by reordering the operations to ensure that the operands are defined before they are used.
# fused_moe.py
def outplace_fused_experts(**kwargs):
# ... (other code remains the same)
# Reorder the operations to ensure operand dominance
c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
# ... (other code remains the same)- Update the
benchmark_moe.pyfile: Modify thebenchmark_moe.pyfile to handle the updatedfused_moe.pyfile.
# benchmark_moe.py
def tune(self):
# ... (other code remains the same)
# Update the kernel invocation to handle the updated fused_moe.py file
kernel_time = benchmark_config(
# ... (other code remains the same)
)
# ... (other code remains the same)Verification
To verify that the fix worked, run the benchmark_moe.py script again with the updated fused_moe.py file.
python3 benchmark_moe.py -tp 1 --tune --model "Qwen/Qwen3-Coder-Next-FP8"If the fix is successful, the script should run without errors and produce the expected output.
Extra Tips
- Make sure to update the
fused_moe.pyfile correctly to handle the operand dominance error. - Verify that the updated
fused_moe.pyfile is being used by thebenchmark_moe.pyscript. - If issues persist, try debugging the
fused_moe.pyfile to identify the root cause of the error.
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×6Another batch ranked right after the header list — different links, same matching logic.
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- Bug: send_message tool bypasses validate_media_delivery_path security check
- Proposal: Add Mnemosyne to official memory provider documentation
- feat(swarm): support custom verifier/synthesizer body + skills
- Template conversion failed
- Error occurred in the operation of the agent node in the workflow.
- PubSub client overrides Sentinel client when REDIS_USE_SENTINEL is enabled
- Frontend description of the Retrieval node output does not match the actual output
- JSON type input var raise Intenal server error
- cannot extract elements from a scalar
- 负载均衡 为模型配置多组凭据，并自动调用，此功能无法选择
- add models is error
- panic: could not create filter
- Persist partially generated messages when /chat-messages/:task_id/stop is called
- MCP server connection fails with 403 — request never leaves Dify (SSRF proxy suspected)
- Support durable async execution backends for long-running workflow steps
- [Xiaomi MiMo] Credentials validation fails with 400 "Not supported model mimo-v2-flash" when using Token Plan endpoint (v0.0.7)
- After clicking preview on a parent-child segmented knowledge base, it shows 0 chunks
- Retrieval score differs between UI upload (.docx) and API upload (.txt) despite identical chunk content and embedding model
- gemini cli crash again
- Xbox gift card code damage
- Damage caused by the gemini cli crash
- ioctl(2) failed, EBADF (Bad File Descriptor)
- Feat: Support Bun as an alternative runtime/package manager for updates and extensions
- fatal error again!!!!
- ioctl error
- Critical Crash: ioctl(2) failed, EBADF in ShellExecutionService.resizePty
- ioctl(2) failed, EBADF
- v0.44.0 Regression: Critical crash with ioctl(2) failed, EBADF during PTY resize
- Crash on startup: ioctl(2) failed, EBADF in UnixTerminal.resize
- Crash: `ioctl(2) failed, EBADF` in `node-pty` during PTY resize on macOS
- Gemini CLI crashes with `ioctl(2) failed, EBADF` in `node-pty` during `resizePty`
- Remote Role
- ERROR ioctl(2) failed, EBADF /home/mich
- RangeError: Maximum call stack size exceeded
- EBADF Error during folder creationg broke session and terminal glitches
- MAIP / Gargoub Project - Mediterania - North Coast
- Gemini cli crash again in this morning
- ERROR ioctl(2) failed, EBADF
- Verified node install fails — Checksum verification failed (Cloud)
- The extended debugging key did not arrive during registration.
- CollaborationPane unmounts collaboration store on single-user instances, causing permanent "No network connection" state
- Workflow cannot be saved when the name contains "->" (Potentially malicious string)
- automation does not work and does not show an error
- Raj Ai Automation
- Default Data Loader: DOMMatrix is not defined error
- Feature: Per-node execution timestamp overlay on canvas during workflow run
- AI Agent + Vertex `gemini-3.5-flash`: 400 "missing thought_signature" on sequential multi-turn tool calls (post-#24982)
- PDF Loader in Pinecone Vector Store fails due to pdf-parse version conflict (v2 not supported)
- emailReadImap: add UID deduplication, batch size cap, and numeric uid enforcement
- Manual node execution fails with "Could not find a node" when autosave is disabled (N8N_WORKFLOWS_AUTOSAVE_DISABLED)
- Schedule Trigger stopped firing — workflow Published & active, manual executions succeed, no automated fires for 2+ hours
- [MCP SDK] create_workflow_from_code intermittently returns HTTP 500, often as a false negative (workflow persists anyway, causing duplicates on retry)
- Credential-load wedge: workflows using googleApi/jwtAuth credentials silently fail to execute after key rotation
- Google Sheets Trigger every minute is not working manual Execute is working sent email
- [BUG] Plugin marketplace MCP connector remains stuck "still connecting" when mcp-remote requires OAuth
- [redacted at user request]
- Opus 4.7 behavioral regression: loaded instruction-following discipline degraded in recent Claude Code/Cowork updates
- [BUG] Tailscale via Homebrew CLI + Mac App Store GUI, both Macs on macOS, Cowork blocked by VPN detector despite Tailscale being a mesh VPN with no traffic interception
- stopShellPty on tab switch kills active sessions (exit 143) — regression in May 27 build
- [BUG] Long URLs are broken into multiple lines and become unclickable in terminal output
- [BUG] claude rm/stop/reap SIGKILLs background session tree without SIGTERM grace, orphaning git index.lock and similar
- [BUG] Default git workflow in the system prompt was pushed without context or consent
- [MODEL] Inconsistent output quality / Ignoring instructions (overfitting and inappropriate repetition of Korean vocabulary)
- You've hit your weekly limit · resets May 31 at 5pm (Asia/Shanghai)
- Paid yearly subscription silently downgraded to Free with no user action
- [Regression v2.1.153] Plugin bash hooks fail with "echo: write error: Permission denied" on Windows (claude-mem, shell: "bash")
- [BUG] Connector toggles in conversation are not clickable — must click text label instead
- [remote-control] Input from mobile app/browser not reaching host session — output works fine
- Model fails to read/reference CLAUDE.md contents despite being loaded in context
- [BUG] Claude Desktop reinstall destroys Code chat history (transcripts + Recents) while regular Chat history, project files, and memory all survive
- Bypass mode clamps to Accept Edits even with the toggle ON (Claude Code Desktop 1.9255.2 / CC 2.1.149)
- [BUG] TUI input freezes randomly mid-typing — entire prompt becomes unresponsive for minutes
- [BUG] Cowork downloads Linux ELF binary instead of macOS binary on macOS Sonoma 14.8.7 — exit code 132 (SIGILL) on every session
- [Feature Request] Persistent project memory — sessions forget everything on close, forcing users to keep many sessions open
- [Bug] Thread context stale after sleep/resume, returns outdated date and calendar data
- [FEATURE] Add context window usage indicator and warning before auto-compaction
- [BUG] Dictation error: Invalid character in header content ["x-config-keyterms"] on Windows
- [Bug] Anthropic API Error: Server rate limiting despite normal usage
- Does delegating work to `claude -p` subprocesses reduce context accumulation in the parent session?
- [BUG] Claude Code hangs on M1 Mac when terminal says "opening browser to sign in" and browser opens
- [BUG] Claude_Preview MCP preview_start spawns dev server with main-repo cwd instead of session's worktree cwd
- [Bug] Anthropic API Error: Server rate limiting during request execution
- [Bug] Anthropic API Error: Server rate limiting on concurrent requests
- [Bug] Ultraplan ready notification fires before cloud agent completes execution
- [BUG] API 500 ERROR ALL THROUGHOUT THE DAY
- [BUG] Cowork: Live Artifacts folder path changed in 1.9255.2, no automatic migration from Documents\Claude\Artifacts
- [Bug] Auto-compact never triggers despite statusline reporting "100% context used" (v2.1.153, Max sub, 200K mode)
- [BUG] [Desktop / macOS] 'Open in → New Window' detached session: font renders smaller than main, no per-window controls, Cmd+/Cmd- keystrokes routed to main window instead
- Feature request: option to switch between classic and new minimal UI
- [Feature Request] Show timestamps for each message
- [BUG] Terminal corruption when permission prompt appears while navigating Agent Teams agent selection menu
- [FEATURE] Allow users to customize the background color of the Claude desktop app beyond the current light/dark theme presets.
- [BUG] Statusline not displaying on Windows [fixed]
- Background agent UI Stop button is a no-op for stuck agents — process keeps consuming tokens
- Background agents silently die on session pause/resume — no completion notification, no work recovery
- Add option to hide email address from welcome banner
- [BUG] SSH Remote: `projects` field in remote ~/.claude.json becomes null after desktop restart — jsonl files intact, UI shows 'No messages yet' for every session
- [Bug] Claude Code not applying fixes despite claiming to complete tasks
- billing is unfair and poorly documented
- [BUG] Claude Code on the web: declared plugins inactive on first session, require restart to fully load
- [BUG] Restore from archive deleted sessions instead of restoring them
- [BUG] M365 connector fails with AADSTS50011 in Cowork — localhost vs 127.0.0.1 redirect URI mismatch
- claude agents: workflow slash-commands missing from dispatch-input completion (regression-adjacent to #61424)
- Claude Desktop's Info.plist missing TCC usage strings, blocks all EventKit-based MCP servers
- False-positive safety blocks on self-administered governance amendments — request for owner-authority mode for verified professional users
- [BUG] Stop pushing "AUTO"-mode
- [DOCS] Plugin marketplace guide omits `skipLfs` option for git-based sources
- [DOCS] MCP docs omit combined startup notification for MCP server and connector authentication
- [DOCS] Agent view docs omit macOS Privacy & Security identity for background agents
- [DOCS] Npm update docs do not explain release-channel behavior for `claude update`
- [DOCS] Agent SDK docs omit `subagent_type: "claude"` worktree and output persistence behavior
- [DOCS] Background session docs omit `$CLAUDE_JOB_DIR` temp-file behavior
- [FR] mask env-var values in 'claude mcp get <server>' output
- [FR] subagent worktrees should not inherit stale local 'user.email' from prior dispatches
- [BUG] Windows: Grep tool leaks rg.exe + conhost.exe processes (~2000 zombies / 14 GB RAM in long sessions)
- [BUG] Stats dashboard "Peak hour" appears off by one hour
- [BUG] Diff highlight (teal SGR background) bleeds past changed text in 2.1.150–2.1.153
- [FEATURE] confirm before deleting session
- Plugin PostToolUse hooks still silently skip in Claude Desktop / Cowork (re-filing closed #51904)
- /code-review skill: silent fallback to main...HEAD reviews other people's commits, and JSON-only output is hard to read
- Monitor tool doesn't source the shell snapshot like Bash does; PATH-dependent tools (jq, sleep, etc.) fail in Monitor commands on macOS/Nix
- [Bug] Long input lines truncated with ellipsis while typing instead of wrapping in terminal UI
- [FEATURE] VS Code extension: Render submitted user messages as Markdown in chat
- OSC 52 copy from Claude TUI doesn't reach clipboard inside tmux (regression in 2.1.146–2.1.153)
- [BUG] RemoteTrigger create/update returns HTTP 400 with circular error: "event_type is required" / "unknown field event_type"
- [BUG] Option to hide or minimize the built-in "status footer" (multi-line debug/cost panel) [re-raise of #31475]
- [Bug] Feedback submissions being closed without review or action
- [FEATURE] Word-jump cursor navigation in Chat input (option+arrow / bindable actions)
- [FEATURE] ! shell mode: filesystem tab completion
- [BUG] API Error: Usage credits required for 1M context
- claude agents: OSC 52 clipboard emission broken in tmux (regression in 2.1.146–2.1.153)
- CLI crashes on macOS 15 M3 - exit code 1
- [FEATURE] Support Cmd+V image paste from clipboard
- [FEATURE] Enhance claude.ai M365 connector to support MS Planner
- [BUG] Slash command autocomplete hijacks pasted absolute file paths starting with /
- PreToolUse hook `if` filter false-positives on complex Bash commands
- [BUG] Diff panel hangs/whites out
- Feature Request: Support drag-and-drop for binary documents (.wps, .doc, .docx, .xlsx, .pdf) in VS Code extension
- [BUG] activation of 1M context in VSCode
- [FEATURE] Support i18n / language localization for built-in slash command outputs
- Ctrl+V para colar imagens deixou de funcionar no CLI (Windows, PowerShell)
- [FEATURE] Please add Norwegian (Bokmål/Nynorsk) language support to the Claude Code interface
- [BUG] OTel log events (claude_code.user_prompt, api_request_body, tool_decision, hook_execution_complete) emitted with empty trace_id/span_id while sibling spans correlate correctly
- [BUG] Cowork crashes on every message, no VM logs generated, missing AppData\Roaming\Claude
- [FEATURE] first-class session handoff + per-session token budgets for unattended runs
- [FEATURE] Smart paste: convert clipboard code to file reference chips (like Cursor)
- [Feature Request] Restore chat pin functionality to title chat submenu
- [BUG] SIGILL issues with version 2.1.153
- [BUG] Cowork plugin upload fails with generic "Plugin validation failed" when a `description` field in any SKILL.md frontmatter contains angle brackets (`<…>`)
- [BUG] Desktop App 2.1.144+: startup scanner deletes cliSessionId from claude-code-sessions local files on every launch — session not found on disk
- [Feature Request] Add keyboard shortcut to copy last message with proper formatting
- [MODEL] Opus 4.7 not 1M
- Allow naming/renaming background agents in `claude agents` view
- Stale worktrees in .claude/worktrees/ are never cleaned up, consuming massive disk space
- Agent worktrees are never cleaned up, silently consuming disk space
- Subagent worktrees not auto-cleaned when reviewer writes scratch files
- [Bug] Skill initialization hangs for extended duration in Plan Mode
- Claude Desktop writes malformed registry Run entry (nested escaped quotes) - crashes Windows Task Manager and other Run-key parsers
- IME candidate window shows at bottom-right corner instead of caret position (Windows CMD)
- [BUG] Pressing 'Escape' doesn't close the /BTW conversation when the main conversation is asking for approval
- [BUG] Opus 4.7 (1M) intermittently emits empty-string values for tool_use.input fields, killing the session
- FleetView agent UI shows "running" with incrementing elapsed time after agent has returned
- /doctor flags context-scoped cmd+c binding as macOS conflict (false positive)
- [BUG] Text Rendering in Elvish
- Desktop app: Bypass Permissions mode flips to Accept Edits on first prompt (M5 / macOS 26.5)
- [Workaround] Date-Weekday Verification Hook — Prevents Claude from writing wrong weekdays
- [BUG] Claude Code create c:/memfs directory without asking me.
- [BUG] Claude Code's Bash execution waits forever with no processes running
- [BUG] usage stays stuck waiting for 5 hr limit after upgrading to premium seat in team plan
- [Workflow tool] resume cache is unreachable for nontrivial workflows because LLM dispatchers can't transcribe args byte-exactly
- Code review (Preview): "Add a repository" shows no results for private GitHub org repos
- [BUG] /context commands blows up context
- [Feature Request] Add precache expiry hook to enable proactive compaction before token eviction
- [BUG] Context indicator shows 0% at session start despite ~20K+ tokens already loaded
- [Feature Request] Add semantic search for --resume session history
- [Feature Request] Add session search, tagging, and filtering capabilities
- [BUG] Cowork Dispatch reports "desktop not available" on Windows 11 while standard Cowork works normally
- [Bug] Claude Code provides incorrect suggestions with high confidence despite errors
- defaultMode: acceptEdits silently overrides per-path permissions.ask rules for Write/Edit
- [FEATUR configurable tip interval (e.g. tipIntervalSeconds: 30 in settings)E]
- Plugin marketplace fails to load: schema rejects 'displayName' key (v2.1.153)
- claude agents: in-session copy uses broken OSC 52 path while overview correctly uses tmux buffer
- [BUG] Plugin agent descriptions (and custom agents) load unconditionally into context — no parity with disable-model-invocation for skills
- Crashed ultrareview consumed a free credit despite producing zero findings
- [Bug] Character rendering issue - invisible or missing text display
- [BUG] Cowork: processo Claude Code encerra com código 3 — .claude.json não contém token de autenticação (Windows 11 25H2)
- [BUG] 2.1.153 silently discards tools/list response from rmcp 0.12.0 HTTP MCP server (works in 2.1.152, wire-identical handshake)
- VS Code extension: option to auto-resume last session when reopening a workspace folder
- [Bug] Conversation continuation failure
- [BUG] Cowork crashes every time I start a new chat or attempt to continue an existing one in any project. The error displayed is: "Claude Code è andato in crash
- [Bug] Unannounced quota changes
- Native update/install fails with 'socket connection was closed unexpectedly' behind proxy — undici TLS incompatibility
- [BUG] Session name reverting after manual change
- [BUG] 非正常思考，上下文过长时，一直显示思考，点击interrupt按钮失效
- Honor `tools:` frontmatter when an agent is invoked via `@mention` — strip `Task` only when the agent did not declare it
- macOS TCC popup still recurring on v2.1.153 — "2.1.153" would like to access data from other apps
- Claude Code leaks pty handles — exhausts pseudo-terminals on macOS after long session
- [Bug] Agent fails to execute or respond to user input
- [BUG] Persistent "Expecting value: line 1 column 1 (char 0)" JSON parse error after tool execution
- [Feature Request] Implement proactive unit test coverage recommendations for recurring bugs
- VS Code panel lacks status line + terminal lacks image paste in Codespaces, forcing a tradeoff
- `/powerup` only shows ~10 lessons — allow viewing the full catalog
- [Bug] Context contamination after auto-compact with unrelated email draft of Tejo/Sado Basin
- [Bug] VSCode terminal output displays corrupted text with garbled symbols
- [Feature Request] Add LaTeX/KaTeX math rendering to TUI
- [Bug] Sub-agent PR review results not validated by orchestrating agent
- Subagents on Pro 1M tier: trivial probes pass, real workloads fail at first tool call (probe-vs-workload divergence)
- Path-scoped rules and subdirectory CLAUDE.md not loaded when creating new files matching the pattern
- AskUserQuestion: cancelling during extended thinking poisons the whole session with 400 'thinking blocks cannot be modified' (2.1.153); concurrent prompts overwrite each other
- Ideas Missing from Claude Cowork Menu (Windows)
- [BUG_BOUNTY_SAFE_POC_2026] Prompt Injection RCE Test - Command Execution Proof
- [BUG] Cowork scheduled task: execution history row not showing after successful run
- Resuming an extended-thinking session fails permanently with 400 "thinking blocks cannot be modified" (transcript stores thinking text as empty but keeps signature)
- [Bug] Plugin-registered CwdChanged and FileChanged hooks don't fire (settings.json works) — v2.1.153
- Auto-archive on PR merge / branch delete — clarify autoArchiveSessions semantics or add dedicated opt-out
- `claude mcp add` echoes Authorization header value verbatim to stdout, leaks bearer tokens to terminal and session transcripts
- [BUG] Bug report — /insights skill, Claude Code The /insights skill outputs a malformed file path.
- Plugin slash commands render with '*'-inline format instead of two-column, despite matching official plugin shape
- [Bug] Unexpected long text generation without user input or goal
- [Bug] Thinking blocks causing task progression blocked without user modification
- [BUG] (Critical!) contamination by an unknown session simirlar to the report => [Bug] Context contamination after auto-compact with unrelated email draft of Tejo/Sado Basin #63137
- [Critical] Opus 4.7 Korean output degeneration — Korean grammar itself collapses in long contexts
- [BUG] Title: Autocompact buffer persists across /clear — wastes tokens for irrelevant old context
- [Bug] Auto-Compact loses user input before processing in conversation history
- Feature: per-invocation effort parameter + runtime session-config introspection for skills
- Auto-mode classifier mislabels Azure DevOps vote -5 as "Reject" when denying PR vote actions
- [BUG] Claude Desktop and Claude Code CLI never re-register MCP tools after OAuth 2.1 handshake on a remote HTTP server
- [BUG] Workspace file tags leak across sessions
- [BUG] Ink renderer crashes on Windows 11 build 26200 (Canary) duplicate banners, terminal mode leaks, mid-operation aborts
- [BUG] Claude Code Desktop issue
- PTY master fd leak in Claude desktop app exhausts macOS kern.tty.ptmx_max after ~2-3 days
- [BUG] Claude Code — Session Management after Unexpected Interruption
- [Windows] Cowork OpenTelemetry exporter does not initialize - zero events emitted to any destination, including loopback
- [Bug] Opus 4.7: 400 `thinking blocks ... cannot be modified` on long extended-thinking sessions, triggered by history-altering events (scheduled prompts / parallel tool-call cancellation)
- [BUG] API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited
- Multi-plugin custom marketplace: only first plugin registered in installed_plugins.json, skills don't load
- [BUG] Git push through the SDK's git proxy fan-outs into ~500 GitHub REST API calls, exhausting the 5,000/hour budget after a handful of pushes
- [BUG] Claude took liberties it really shouldn't with my global config
- [BUG] Agent window focus lost after navigating with arrow keys, causing scroll deadlock
- [BUG] `--model` flag silently ignored in interactive sessions (works in `--print` only)
- [BUG] Dispatch permanently shows "desktop appears offline" on Windows 11 - never worked on first use
- feat: support per-command enableWeakerNetworkIsolation as safer alternative to dangerouslyDisableSandbox
- /code-review outputs a raw JSON array instead of readable findings
- [BUG] Cowork — Additional allowed domains ignored on Team plan; same domain works on Pro plan
- Haiku
- [Bug] False positive blocking beneficial outcomes in tool execution
- 3P Bedrock SSO: credentials silently expire without triggering re-auth on day 2+
- CLAUDE_AUTOCOMPACT_PCT_OVERRIDE in settings.json env block silently ignored by autocompact logic
- Auto-compaction deletes main session JSONL before verifying summary completion, causing data loss
- [Bug] Claude Code not executing stated actions or producing expected results
- [FEATURE] Deferred Messages — Queue Input for End of Turn
- [BUG] Up/Down arrows in input box navigate history instead of moving cursor — regression in 2.1.149+
- Cancelling a parallel tool-call batch corrupts thinking blocks -> 400 "thinking blocks cannot be modified" permanently wedges the session
- Claude Code caused data loss, then contradicted itself about recovery (two incidents, one session)
- [Bug] Unclear error messages from Claude Code CLI
- [Bug] Agent tool rejecting due to context size limit exceeded
- claude agents: daemon and bg-spare processes spin at ~100% CPU when idle
- [BUG] Compaction fails with "context window limit" error even when context usage is low (e.g., 20%) — regression in v2.1.153
- Remote Control entitlement lost after May 27-28 incident — `Error: Remote Control is not yet enabled for your account` on active Max subscription
- PreToolUse hook exit code 2 does not block Write tool
- [Bug] Thinking blocks in latest assistant message are immutable
- GUI: dispatch file:// and custom-scheme clicks to OS shell handler
- Show current model in statusLine by default
- [Bug] Agent console becomes unresponsive to keyboard input after multiple agents initialized
- [FEATURE] PreToolUse hooks should have a way of updating the environment
- [Bug] Unable to start or use Claude Code CLI
- [BUG] Repository not visible in Claude Code web repo picker
- Session permanently wedged on 400 "thinking blocks cannot be modified" after parallel tool_results
- [Bug] @ autocomplete loses sibling repos after a file edit in multi-repo workspace
- Unclear error message when creating sub-agent without authentication
- [Bug] Anthropic API errors causing frequent failures and high token usage
- [BUG] @ mention file picker only shows packages, not individual files (desktop app - Code tab)
- [Bug] TUI panel footer remains sticky and consumes excessive terminal space
- PR-status polling exhausts GitHub GraphQL rate limit on repos with many open PRs
- [BUG] Windows: welcome panel not shown in some project folders (2.1.153)
- [Bug] Anthropic API Error: thinking blocks corrupted during context compaction with extended thinking enabled
- API 400 "thinking blocks cannot be modified" permanently bricks session during agent activation (interleaved thinking + tool use)
- Right-click Copy copies the whole message instead of the selection; pasted text retains dark background
- Mid-session model switch corrupts conversation when extended thinking is enabled (API 400: 'thinking blocks cannot be modified')
- [BUG] Markdown file links in chat output do not open files when clicked (VS Code extension)
- Stuck retry loop: `400 thinking blocks cannot be modified` on large interleaved-thinking turns using AskUserQuestion
- [FEATURE] Prompt user for approval before auto-compaction proceeds
- Custom MCP connectors not attachable to scheduled routines — no UUID discovery path
- [BUG] Claude in Chrome — Navigation blocked for teams.cloud.microsoft and outlook.cloud.microsoft after Microsoft domain migration**
- [BUG] Claude Desktop — Personal plugins panel renders list but is entirely non-interactive (macOS, v1.9255.2)
- [Bug] error when using Workflows
- [BUG] Persistent "update available" notification despite being on latest version
- [BUG] Sweep Agent from /code-review never completes
- [Bug] Tool calls not executing or returning results
- [FEATURE] Cloud-synced memory and settings across machines
- [Bug] Terminal UI freezes when Ctrl+O view exits during interactive prompt in plan mode
- Continuous api errors when using claude code with Opus 4.7 with thinking on low
- [Feature Request] Add support for installing and using previous Claude Code versions
- [Bug] Extended Thinking: Summarized thinking blocks fail signature validation when resent to API
- [Bug] Anthropic API Error: 'thinking' blocks cannot be modified
- [Bug] Anthropic API Error: Thinking blocks cannot be modified with extended thinking mode
- Feature request: Lazy/on-demand MCP server connections
- [Bug] Tool Arguments Parsed as String Instead of Object
- [Bug] Anthropic API Error: Insufficient context provided
- [Bug] Claude Opus occasionally uses moskovian(russian) orthography instead of Ukrainian in system-prompted responses
- Opus 4.8: backgrounded task completions (subagents AND Bash) crash with 400 "thinking blocks cannot be modified"
- [Bug] Opus 4.7 fabricates stable preferences ("my default") to rationalize arbitrary choices when challenged
- [Bug] Unable to update Claude Code CLI
- [BUG] Desktop app: /remote-control mints link + connects bridge (main.log) but in-chat link/QR panel never renders
- Feature: sessionColor and sessionName in .claude/settings.json
- [BUG] Anthropic API error: thinking blocks
- [FEATURE] Support Remote MCPs in Cowork as in Claude Code
- [Bug] Anthropic API Error: 400 Bad Request with Redacted Thinking - 0 4.7 & 4.8
- [Bug] Anthropic API Error: Cannot modify thinking blocks from different model versions
- Interleaved thinking + multi-tool turn corrupts thinking block (text blanked, signature kept) → permanent 400 'blocks must remain as they were'
- [BUG] Mode/permission changes mid-tool-loop (effortLevel: xhigh) poisons entire session
- Session failure log: Opus 4.6 ignores its own rules for an entire session
- [BUG] "400 Guardrail was enabled" error when using Claude Opus 4.8 with AWS Bedrock
- [Feature Request] Add subagent approach selection option to avoid accidental feedback
- Persistent 400 'thinking blocks in the latest assistant message cannot be modified' — interleaved thinking persisted with empty text + signature bricks sessions
- [BUG] DesktopvsApp
- [BUG] Opus 4.7 cache hit rate collapse after May 27 incident — Messages 1.1k→88.9k in 9 minutes, $630/session
- [Bug] Anthropic API Error: Invalid thinking block format
- [BUG] FUCK CLAUDE
- Opus 4.8 extended thinking: Stop hook block re-entry corrupts thinking blocks → 400
- [Bug] 4.8 Fails when accessing previous model history
- [Bug] Unintended File Modifications During Execution
- [DOCS] Model configuration docs omit lean system prompt default scope and model exceptions
- Add "Always allow globally" option to permission prompts
- Server-side model upgrade (Opus 4.7→4.8) wedges in-flight sessions with `thinking blocks cannot be modified` 400
- [DOCS] AskUserQuestion docs missing multiple-choice prompt decision threshold
- [DOCS] Agent view docs omit shell-command background session launch syntax
- [DOCS] Agent view dispatch input docs incorrectly imply `/logout` dispatches as a prompt
- [DOCS] Claude in Chrome docs omit connected-browser selection behavior
- [DOCS] Plugin docs omit `defaultEnabled: false` for opt-in plugins
- Feature Request: Customizable chat text colors for user and assistant messages
- [DOCS] `/plugin` Discover tab docs omit directory-based suggested plugin pins
- VSCode Chrome integration silently fails: 3 distinct bugs
- [DOCS] MCP stdio docs omit session environment variables
- [Bug] Anthropic API error on second request within session with Claude Opus 4.8
- Cowork emits a blank session "index" handoff on focus when a CLI session is paused awaiting input
- [DOCS] MCP docs omit `claude mcp list/get` pending-approval output for unapproved project servers
- [BUG] /compact fails with 400 error when last assistant turn contains thinking blocks
- [DOCS] `/claude-api` docs omit Opus 4.8 migration guidance
- [DOCS] Fast mode docs still recommend deprecated Opus 4.6 override variable
- [DOCS] Bash tool docs omit `$TMPDIR` consistency across sandboxed and unsandboxed commands
- [Bug] Anthropic API Error: 400 Bad Request on Extended Thinking
- [DOCS] Background session docs omit worktree-isolation behavior for spawned subagents
- Built-in mechanistic self-verification of verifiable claims (symmetric to the auto permission gate)
- [DOCS] Worktree docs do not clarify `worktree.baseRef: "head"` inside linked worktrees
- [BUG] Excessive RAM usage with multiple parallel chats (~10 sessions → 30 GB memory pressure, macOS OOM)
- [DOCS] Managed MCP policy docs omit invalid `allowedMcpServers`/`deniedMcpServers` entry behavior
- [DOCS] Effort docs omit `CLAUDE_CODE_ALWAYS_ENABLE_EFFORT` unsupported-model behavior
- Regression (2.1.147–2.1.150?): resuming an extended-thinking session after a CC update/model-switch → unrecoverable 400, session bricked
- [DOCS] Windows updater docs omit `claude.exe` in-use recovery guidance
- [DOCS] VS Code auto mode docs still tie mode-picker visibility to bypass-permissions setting
- [DOCS] MCP docs omit `/mcp` tool list and detail rendering behavior
- [DOCS] Fine-grained tool streaming docs still describe provider opt-in behavior
- bypassPermissions: session startup reads flat pref, GUI toggle writes per-account pref — they never sync
- [BUG] Claude Desktop Code tab causes disk write limit violation — 8.5GB in 11 min, macOS kills app (M5, v1.9659.1)
- Ultrareview v2.1.96: docs describe /tasks command + claude ultrareview --json subcommand that don't exist; findings hard to read after completion
- I'd be happy to help create a GitHub issue title, but I don't see the error message in your message. Could you please share the specific error you're encountering? That way I can generate an accurate and descriptive issue title for you.
- [BUG] Claude in Chrome `file_upload` rejects all scheduled-task sessions with misleading error (real cause: INVALID_SESSION)
- Extended thinking: signed thinking block 'cannot be modified' (400) permanently wedges session
- RTL text support for Hebrew (and Arabic) in Claude Code
- [Bug] Random errors occurring across multiple operations