Repository: pytorch
Issue: BackendCompilerFailed error is raised when applying torch.compile on torch.fill
Labels: triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug

When compiling torch.fill in cuda environment, the compiled function will raise `BackendCompilerFailed` error when input is an `uint` tensor. It seems that this issue is caused by invalid argument type when using triton's API `tl.full`.
Note that directly calling torch.fill with uint tensor does not lead to any exception.

Here is the code to reproduce:

```
import torch
f = torch.fill
cf = torch.compile(f)
input = torch.randn(1,2,3).to(torch.uint32).to('cuda')
value = -90
print(f(input,value))  # tensor([[[4294967206, 4294967206, 4294967206],[4294967206, 4294967206, 4294967206]]], device='cuda:0',dtype=torch.uint32)
cf_out = cf(input,value)
```

Although the traceback is super long, the error message seems straightforward:

```
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] Triton compilation failed: triton_poi_fused_fill_0
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] def triton_poi_fused_fill_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xnumel = 6
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xoffset = tl.program_id(0) * XBLOCK
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xindex = xoffset + tl.arange(0, XBLOCK)[:]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xmask = xindex < xnumel
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     x0 = xindex
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     tmp0 = tl.full([1], -90, tl.uint32)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     tl.store(out_ptr0 + (x0), tmp0, xmask)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] metadata: {'signature': {'out_ptr0': '*u32', 'xnumel': 'i32'}, 'device': 0, 'constants': {'XBLOCK': 8}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())], 'device_type': 'cuda', 'num_warps': 1, 'num_stages': 1, 'debug': True, 'cc': 86}
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] Traceback (most recent call last):
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/triton/language/core.py", line 35, in wrapper
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     return fn(*args, **kwargs)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]            ^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/triton/language/core.py", line 1223, in full
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     return semantic.full(shape, value, dtype, _builder)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/triton/language/semantic.py", line 530, in full
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     value = get_value_fn(value)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]             ^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] TypeError: get_uint32(): incompatible function arguments. The following argument types are supported:
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     1. (self: triton._C.libtriton.ir.builder, arg0: int) -> triton._C.libtriton.ir.value
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] Invoked with: <triton._C.libtriton.ir.builder object at 0x7fca9840ecf0>, -90
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] The above exception was the direct cause of the following exception:
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] Traceback (most recent call last):
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 532, in _precompile_config
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     binary = triton.compile(*compile_args, **compile_kwargs)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/triton/compiler/compiler.py", line 276, in compile
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     module = src.make_ir(options, codegen_fns, context)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]   File "/opt/conda/lib/python3.11/site-packages/triton/compiler/compiler.py", line 113, in make_ir
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] triton.compiler.errors.CompilationError: at 7:11:
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0] def triton_poi_fused_fill_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xnumel = 6
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xoffset = tl.program_id(0) * XBLOCK
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xindex = xoffset + tl.arange(0, XBLOCK)[:]
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     xmask = xindex < xnumel
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     x0 = xindex
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]     tmp0 = tl.full([1], -90, tl.uint32)
E0104 08:55:10.835000 220442 site-packages/torch/_inductor/runtime/triton_heuristics.py:534] [0/0]            ^
Traceback (most recent call last):
  File "/root/try.py", line 7, in <module>
    cf_out = cf(input,value)
             ^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 576, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 1404, in __call__
    return self._torchdynamo_orig_callable(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 1188, in __call__
    result = self._inner_convert(
             ^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 565, in __call__
    return _compile(
           ^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 1005, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 733, in compile_inner
    return _compile_inner(code, one_graph, hooks, transform)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_utils_internal.py", line 95, in wrapper_function
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 768, in _compile_inner
    out_code = transform_code_object(code, transform)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py", line 1402, in transform_code_object
    transformations(instructions, code_options)
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 236, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 680, in transform
    tracer.run()
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2906, in run
    super().run()
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 1076, in run
    while self.step():
          ^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 986, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 3086, in RETURN_VALUE
    self._return(inst)
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 3071, in _return
    self.output.compile_subgraph(
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1107, in compile_subgraph
    self.compile_and_call_fx_graph(
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1390, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1440, in call_user_compiler
    return self._call_user_compiler(gm)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1493, in _call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1472, in _call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/repro/after_dynamo.py", line 130, in __call__
    compiled_gm = compiler_fn(gm, example_inputs)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/__init__.py", line 2314, in __call__
    return compile_fx(model_, inputs_, config_patches=self.config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1886, in compile_fx
    return aot_autograd(
           ^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/backends/common.py", line 83, in __call__
    cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 1170, in aot_module_simplified
    compiled_fn = AOTAutogradCache.load(
                  ^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/autograd_cache.py", line 754, in load
    compiled_fn = dispatch_and_compile()
                  ^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 1155, in dispatch_and_compile
    compiled_fn, _ = create_aot_dispatcher_function(
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 582, in create_aot_dispatcher_function
    return _create_aot_dispatcher_function(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 832, in _create_aot_dispatcher_function
    compiled_fn, fw_metadata = compiler_fn(
                               ^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 201, in aot_dispatch_base
    compiled_fw = compiler(fw_module, updated_flat_args)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 491, in __call__
    return self.compiler_fn(gm, example_inputs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1764, in fw_compiler_base
    return inner_compile(
           ^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 575, in compile_fx_inner
    return wrap_compiler_debug(_compile_fx_inner, compiler_name="inductor")(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_dynamo/repro/after_aot.py", line 102, in debug_wrapper
    inner_compiled_fn = compiler_fn(gm, example_inputs)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 689, in _compile_fx_inner
    mb_compiled_graph = fx_codegen_and_compile(
                        ^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1132, in fx_codegen_and_compile
    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1047, in codegen_and_compile
    compiled_fn = graph.compile_to_module().call
                  ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/graph.py", line 1978, in compile_to_module
    return self._compile_to_module()
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/graph.py", line 2019, in _compile_to_module
    mod = PyCodeCache.load_by_key_path(
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/codecache.py", line 2769, in load_by_key_path
    mod = _reload_python_module(key, path)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/runtime/compile_tasks.py", line 46, in _reload_python_module
    exec(code, mod.__dict__, mod.__dict__)
  File "/tmp/torchinductor_root/iy/ciyo7ytism5a7pcooebqj3wuyepsq3xgyjogs2vpfqkhrxqs2l5w.py", line 48, in <module>
    triton_poi_fused_fill_0 = async_compile.triton('triton_poi_fused_fill_0', '''
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/async_compile.py", line 214, in triton
    kernel.precompile()
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 302, in precompile
    compiled_binary, launcher = self._precompile_config(
                                ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 532, in _precompile_config
    binary = triton.compile(*compile_args, **compile_kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/triton/compiler/compiler.py", line 276, in compile
    module = src.make_ir(options, codegen_fns, context)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/triton/compiler/compiler.py", line 113, in make_ir
    return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
CompilationError: at 7:11:
def triton_poi_fused_fill_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.full([1], -90, tl.uint32)
           ^

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

### Versions

Collecting environment information...
PyTorch version: 2.6.0a0+gite15442a
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.2
Libc version: glibc-2.35

Python version: 3.11.10 | packaged by conda-forge | (main, Oct 16 2024, 01:27:36) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.14.0-427.37.1.el9_4.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 560.35.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper PRO 3975WX 32-Cores
CPU family: 23
Model: 49
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 1
Stepping: 0
Frequency boost: enabled
CPU max MHz: 4368.1641
CPU min MHz: 2200.0000
BogoMIPS: 7000.73
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization: AMD-V
L1d cache: 1 MiB (32 instances)
L1i cache: 1 MiB (32 instances)
L2 cache: 16 MiB (32 instances)
L3 cache: 128 MiB (8 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-63
Vulnerability Gather data sampling: 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 Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] mypy==1.13.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.17.0
[pip3] onnxscript==0.1.0.dev20240817
[pip3] optree==0.13.0
[pip3] torch==2.6.0a0+gite15442a
[pip3] triton==3.1.0
[conda] numpy 1.26.2 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] optree 0.13.0 pypi_0 pypi
[conda] torch 2.6.0a0+gite15442a pypi_0 pypi
[conda] triton 3.1.0 pypi_0 pypi

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov @BoyuanFeng