[inductor] Accuracy issue on Nvidia V100
Labels: triaged, module: inductor

### 🐛 Describe the bug

I got NaN value issue while running stable diffusion model + inductor on Nvidia V100. Note this issue does not exist while testing on A100 gpu, therefore I suspect this is a inductor/triton/cuda related issue.
```py
import torch
from diffusers import UNet2DConditionModel

cuda_device = torch.device("cuda:0")
unet = UNet2DConditionModel.from_pretrained(
    "CompVis/stable-diffusion-v1-4", subfolder="unet", revision=None
)
unet.train()
unet.to(cuda_device)
unet = torch.compile(unet, backend="inductor")

x = torch.rand([2, 4, 64, 64], dtype=torch.float32, device=cuda_device)
t = torch.randint(0, 1000, size=(2,), device=cuda_device)
hidden = torch.rand([2, 77, 768], dtype=torch.float32, device=cuda_device)

with torch.cuda.amp.autocast():
    model_pred = unet(x, t, hidden).sample
print(model_pred)
```
Running with `TORCHDYNAMO_REPRO_AFTER="dynamo" TORCHDYNAMO_REPRO_LEVEL=4 `, I got the `repro.py`.
```py
import torch._inductor.overrides

import torch
from torch import tensor, device
import torch.fx as fx
from torch._dynamo.testing import rand_strided
from math import inf
from torch.fx.experimental.proxy_tensor import make_fx

import torch._dynamo.config
import torch._inductor.config
import torch._functorch.config
torch._dynamo.config.load_config(b'\x80\x02}q\x00(X\x0b\x00\x00\x00output_codeq\x01\x89X\r\x00\x00\x00log_file_nameq\x02NX\x07\x00\x00\x00verboseq\x03\x89X\x11\x00\x00\x00output_graph_codeq\x04\x89X\x12\x00\x00\x00verify_correctnessq\x05\x89X\x12\x00\x00\x00minimum_call_countq\x06K\x01X\x15\x00\x00\x00dead_code_eliminationq\x07\x88X\x10\x00\x00\x00cache_size_limitq\x08K@X\x14\x00\x00\x00specialize_int_floatq\t\x88X\x0e\x00\x00\x00dynamic_shapesq\n\x89X\x18\x00\x00\x00assume_static_by_defaultq\x0b\x89X\x10\x00\x00\x00guard_nn_modulesq\x0c\x89X\x1b\x00\x00\x00traceable_tensor_subclassesq\rc__builtin__\nset\nq\x0e]q\x0f\x85q\x10Rq\x11X\x0f\x00\x00\x00suppress_errorsq\x12\x89X\x15\x00\x00\x00replay_record_enabledq\x13\x89X \x00\x00\x00rewrite_assert_with_torch_assertq\x14\x88X\x12\x00\x00\x00print_graph_breaksq\x15\x89X\x07\x00\x00\x00disableq\x16\x89X*\x00\x00\x00allowed_functions_module_string_ignorelistq\x17h\x0e]q\x18(X\r\x00\x00\x00torch.testingq\x19X\r\x00\x00\x00torch._decompq\x1aX\x13\x00\x00\x00torch.distributionsq\x1bX\x0c\x00\x00\x00torch._primsq\x1cX\x0b\x00\x00\x00torch._refsq\x1de\x85q\x1eRq\x1fX\x12\x00\x00\x00repro_forward_onlyq \x89X\x0f\x00\x00\x00repro_toleranceq!G?PbM\xd2\xf1\xa9\xfcX\x16\x00\x00\x00capture_scalar_outputsq"\x89X \x00\x00\x00capture_dynamic_output_shape_opsq#\x89X\x19\x00\x00\x00enforce_cond_guards_matchq$\x88X\x0c\x00\x00\x00optimize_ddpq%\x88X\x1a\x00\x00\x00raise_on_ctx_manager_usageq&\x88X\x1c\x00\x00\x00raise_on_unsafe_aot_autogradq\'\x89X\x17\x00\x00\x00raise_on_backend_changeq(\x89X\x18\x00\x00\x00error_on_nested_fx_traceq)\x88X\t\x00\x00\x00allow_rnnq*\x89X\x08\x00\x00\x00base_dirq+X\x18\x00\x00\x00/home/ubuntu/src/pytorchq,X\x0e\x00\x00\x00debug_dir_rootq-X+\x00\x00\x00/home/ubuntu/tmp/dynamo/torch_compile_debugq.X)\x00\x00\x00DO_NOT_USE_legacy_non_fake_example_inputsq/\x89X\x13\x00\x00\x00_save_config_ignoreq0h\x0e]q1(X\x0b\x00\x00\x00repro_afterq2X\x12\x00\x00\x00constant_functionsq3X!\x00\x00\x00skipfiles_inline_module_allowlistq4X\x0b\x00\x00\x00repro_levelq5e\x85q6Rq7u.')
torch._inductor.config.load_config(b'\x80\x02}q\x00(X\x05\x00\x00\x00debugq\x01\x89X\x10\x00\x00\x00disable_progressq\x02\x88X\x10\x00\x00\x00verbose_progressq\x03\x89X\x0b\x00\x00\x00cpp_wrapperq\x04\x89X\x03\x00\x00\x00dceq\x05\x89X\x14\x00\x00\x00static_weight_shapesq\x06\x88X\x0c\x00\x00\x00size_assertsq\x07\x88X\x10\x00\x00\x00pick_loop_ordersq\x08\x88X\x0f\x00\x00\x00inplace_buffersq\t\x88X\x11\x00\x00\x00benchmark_harnessq\n\x88X\x0f\x00\x00\x00epilogue_fusionq\x0b\x89X\x15\x00\x00\x00epilogue_fusion_firstq\x0c\x89X\x0f\x00\x00\x00pattern_matcherq\r\x88X\n\x00\x00\x00reorderingq\x0e\x89X\x0c\x00\x00\x00max_autotuneq\x0f\x89X\x15\x00\x00\x00search_autotune_cacheq\x10\x88X\x17\x00\x00\x00realize_reads_thresholdq\x11K\x04X\x17\x00\x00\x00realize_bytes_thresholdq\x12M\xd0\x07X\x1b\x00\x00\x00realize_acc_reads_thresholdq\x13K\x08X\x0f\x00\x00\x00fallback_randomq\x14\x89X\x12\x00\x00\x00implicit_fallbacksq\x15\x88X\x0b\x00\x00\x00tune_layoutq\x16\x89X\x11\x00\x00\x00aggressive_fusionq\x17\x89X\x0f\x00\x00\x00max_fusion_sizeq\x18K@X\x1b\x00\x00\x00unroll_reductions_thresholdq\x19K\x08X\x0e\x00\x00\x00comment_originq\x1a\x89X\x12\x00\x00\x00developer_warningsq\x1b\x88X\x0f\x00\x00\x00compile_threadsq\x1cK X\x11\x00\x00\x00global_cache_pathq\x1dNX\x13\x00\x00\x00kernel_name_max_opsq\x1eK\nX\r\x00\x00\x00shape_paddingq\x1f\x89X\x0e\x00\x00\x00permute_fusionq \x89X\x1a\x00\x00\x00profiler_mark_wrapper_callq!\x89X\x18\x00\x00\x00_raise_error_for_testingq"\x89X\x0c\x00\x00\x00_profile_varq#X\x00\x00\x00\x00q$X\x11\x00\x00\x00profile_bandwidthq%\x89X\x17\x00\x00\x00profile_bandwidth_regexq&h$X\x0b\x00\x00\x00cpp.threadsq\'J\xff\xff\xff\xffX\x13\x00\x00\x00cpp.dynamic_threadsq(\x89X\x0b\x00\x00\x00cpp.simdlenq)NX\x12\x00\x00\x00cpp.min_chunk_sizeq*M\x00\x10X\x07\x00\x00\x00cpp.cxxq+NX\x03\x00\x00\x00g++q,\x86q-X\x19\x00\x00\x00cpp.enable_kernel_profileq.\x89X\x12\x00\x00\x00cpp.weight_prepackq/\x88X\x11\x00\x00\x00triton.cudagraphsq0\x89X\x17\x00\x00\x00triton.debug_sync_graphq1\x89X\x18\x00\x00\x00triton.debug_sync_kernelq2\x89X\x12\x00\x00\x00triton.convolutionq3X\x04\x00\x00\x00atenq4X\x15\x00\x00\x00triton.dense_indexingq5\x89X\x10\x00\x00\x00triton.max_tilesq6K\x02X\x19\x00\x00\x00triton.autotune_pointwiseq7\x88X\'\x00\x00\x00triton.tiling_prevents_pointwise_fusionq8\x88X\'\x00\x00\x00triton.tiling_prevents_reduction_fusionq9\x88X\x1b\x00\x00\x00triton.ordered_kernel_namesq:\x89X\x1f\x00\x00\x00triton.descriptive_kernel_namesq;\x89X\x1c\x00\x00\x00triton.persistent_reductionsq<\x88X\x10\x00\x00\x00triton.max_blockq=}q>(X\x01\x00\x00\x00Xq?M\x00\x08X\x01\x00\x00\x00Yq@M\x00\x04X\x01\x00\x00\x00ZqAM\x00\x04uX\r\x00\x00\x00trace.enabledqB\x89X\x0f\x00\x00\x00trace.debug_logqC\x88X\x0e\x00\x00\x00trace.info_logqD\x89X\x0e\x00\x00\x00trace.fx_graphqE\x88X\x1a\x00\x00\x00trace.fx_graph_transformedqF\x88X\x13\x00\x00\x00trace.ir_pre_fusionqG\x88X\x14\x00\x00\x00trace.ir_post_fusionqH\x88X\x11\x00\x00\x00trace.output_codeqI\x88X\x13\x00\x00\x00trace.graph_diagramqJ\x89X\x15\x00\x00\x00trace.compile_profileqK\x89X\x10\x00\x00\x00trace.upload_tarqLNu.')
torch._functorch.config.load_config(b'\x80\x02}q\x00(X\x11\x00\x00\x00use_functionalizeq\x01\x88X\x0f\x00\x00\x00use_fake_tensorq\x02\x88X\x16\x00\x00\x00fake_tensor_allow_metaq\x03\x88X\x0c\x00\x00\x00debug_assertq\x04\x88X\x14\x00\x00\x00debug_fake_cross_refq\x05\x89X\x11\x00\x00\x00debug_partitionerq\x06\x89X\x0c\x00\x00\x00debug_graphsq\x07\x89X\x0b\x00\x00\x00debug_jointq\x08\x89X\x12\x00\x00\x00use_dynamic_shapesq\t\x89X\x14\x00\x00\x00static_weight_shapesq\n\x88X\x03\x00\x00\x00cseq\x0b\x88X\x10\x00\x00\x00max_dist_from_bwq\x0cK\x03X\t\x00\x00\x00log_levelq\rK\x14u.')


# REPLACEABLE COMMENT FOR TESTING PURPOSES


# torch version: 2.0.0a0+git9ded087
# torch cuda version: 11.8
# torch git version: 9ded087bac636d361c277dac99e822db5b9863b8


# CUDA Info: 
# nvcc: NVIDIA (R) Cuda compiler driver 
# Copyright (c) 2005-2022 NVIDIA Corporation 
# Built on Wed_Sep_21_10:33:58_PDT_2022 
# Cuda compilation tools, release 11.8, V11.8.89 
# Build cuda_11.8.r11.8/compiler.31833905_0 

# GPU Hardware Info: 
# Tesla V100-SXM2-32GB : 8 


from torch.nn import *
class Repro(torch.nn.Module):
    def __init__(self):
        super().__init__()

    
    
    def forward(self, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7):
        convert_element_type = torch.ops.prims.convert_element_type.default(primals_1, torch.float16);  primals_1 = None
        convert_element_type_1 = torch.ops.prims.convert_element_type.default(primals_6, torch.float16);  primals_6 = None
        permute = torch.ops.aten.permute.default(convert_element_type, [1, 0]);  convert_element_type = None
        view = torch.ops.aten.view.default(convert_element_type_1, [8192, 320]);  convert_element_type_1 = None
        mm = torch.ops.aten.mm.default(view, permute)
        view_1 = torch.ops.aten.view.default(mm, [2, 4096, 320]);  mm = None
        convert_element_type_2 = torch.ops.prims.convert_element_type.default(primals_2, torch.float16);  primals_2 = None
        convert_element_type_3 = torch.ops.prims.convert_element_type.default(primals_7, torch.float16);  primals_7 = None
        permute_1 = torch.ops.aten.permute.default(convert_element_type_2, [1, 0]);  convert_element_type_2 = None
        view_2 = torch.ops.aten.view.default(convert_element_type_3, [154, 768]);  convert_element_type_3 = None
        mm_1 = torch.ops.aten.mm.default(view_2, permute_1);  permute_1 = None
        view_3 = torch.ops.aten.view.default(mm_1, [2, 77, 320]);  mm_1 = None
        convert_element_type_4 = torch.ops.prims.convert_element_type.default(primals_3, torch.float16);  primals_3 = None
        permute_2 = torch.ops.aten.permute.default(convert_element_type_4, [1, 0]);  convert_element_type_4 = None
        mm_2 = torch.ops.aten.mm.default(view_2, permute_2);  permute_2 = None
        view_5 = torch.ops.aten.view.default(mm_2, [2, 77, 320]);  mm_2 = None
        view_6 = torch.ops.aten.view.default(view_1, [2, 4096, 8, 40]);  view_1 = None
        permute_3 = torch.ops.aten.permute.default(view_6, [0, 2, 1, 3]);  view_6 = None
        clone = torch.ops.aten.clone.default(permute_3, memory_format = torch.contiguous_format);  permute_3 = None
        _unsafe_view = torch.ops.aten._unsafe_view.default(clone, [16, 4096, 40]);  clone = None
        view_7 = torch.ops.aten.view.default(view_3, [2, 77, 8, 40]);  view_3 = None
        permute_4 = torch.ops.aten.permute.default(view_7, [0, 2, 1, 3]);  view_7 = None
        clone_1 = torch.ops.aten.clone.default(permute_4, memory_format = torch.contiguous_format);  permute_4 = None
        _unsafe_view_1 = torch.ops.aten._unsafe_view.default(clone_1, [16, 77, 40]);  clone_1 = None
        view_8 = torch.ops.aten.view.default(view_5, [2, 77, 8, 40]);  view_5 = None
        permute_5 = torch.ops.aten.permute.default(view_8, [0, 2, 1, 3]);  view_8 = None
        clone_2 = torch.ops.aten.clone.default(permute_5, memory_format = torch.contiguous_format);  permute_5 = None
        _unsafe_view_2 = torch.ops.aten._unsafe_view.default(clone_2, [16, 77, 40]);  clone_2 = None
        empty = torch.ops.aten.empty.memory_format([16, 4096, 77], dtype = torch.float16, device = device(type='cuda', index=0), pin_memory = False)
        permute_6 = torch.ops.aten.permute.default(_unsafe_view_1, [0, 2, 1]);  _unsafe_view_1 = None
        bmm = torch.ops.aten.bmm.default(_unsafe_view, permute_6)
        mul = torch.ops.aten.mul.Tensor(bmm, 0.15811388300841897);  bmm = None
        mul_1 = torch.ops.aten.mul.Tensor(empty, 0);  empty = None
        add = torch.ops.aten.add.Tensor(mul_1, mul);  mul_1 = mul = None
        convert_element_type_6 = torch.ops.prims.convert_element_type.default(add, torch.float32);  add = None
        amax = torch.ops.aten.amax.default(convert_element_type_6, [-1], True)
        sub = torch.ops.aten.sub.Tensor(convert_element_type_6, amax);  convert_element_type_6 = amax = None
        exp = torch.ops.aten.exp.default(sub);  sub = None
        sum_1 = torch.ops.aten.sum.dim_IntList(exp, [-1], True)
        div = torch.ops.aten.div.Tensor(exp, sum_1);  exp = sum_1 = None
        convert_element_type_7 = torch.ops.prims.convert_element_type.default(div, torch.float16)
        bmm_1 = torch.ops.aten.bmm.default(convert_element_type_7, _unsafe_view_2)
        view_9 = torch.ops.aten.view.default(bmm_1, [2, 8, 4096, 40]);  bmm_1 = None
        permute_7 = torch.ops.aten.permute.default(view_9, [0, 2, 1, 3]);  view_9 = None
        clone_3 = torch.ops.aten.clone.default(permute_7, memory_format = torch.contiguous_format);  permute_7 = None
        _unsafe_view_3 = torch.ops.aten._unsafe_view.default(clone_3, [2, 4096, 320]);  clone_3 = None
        convert_element_type_8 = torch.ops.prims.convert_element_type.default(primals_5, torch.float16);  primals_5 = None
        convert_element_type_9 = torch.ops.prims.convert_element_type.default(primals_4, torch.float16);  primals_4 = None
        permute_8 = torch.ops.aten.permute.default(convert_element_type_9, [1, 0]);  convert_element_type_9 = None
        view_10 = torch.ops.aten.view.default(_unsafe_view_3, [8192, 320]);  _unsafe_view_3 = None
        addmm = torch.ops.aten.addmm.default(convert_element_type_8, view_10, permute_8);  convert_element_type_8 = None
        view_11 = torch.ops.aten.view.default(addmm, [2, 4096, 320])
        permute_9 = torch.ops.aten.permute.default(permute_8, [1, 0]);  permute_8 = None
        permute_14 = torch.ops.aten.permute.default(convert_element_type_7, [0, 2, 1]);  convert_element_type_7 = None
        permute_15 = torch.ops.aten.permute.default(_unsafe_view_2, [0, 2, 1]);  _unsafe_view_2 = None
        permute_16 = torch.ops.aten.permute.default(permute_6, [0, 2, 1]);  permute_6 = None
        permute_17 = torch.ops.aten.permute.default(_unsafe_view, [0, 2, 1]);  _unsafe_view = None
        permute_30 = torch.ops.aten.permute.default(permute, [1, 0]);  permute = None
        return [view_11, addmm, view, view_2, div, view_10, permute_9, permute_14, permute_15, permute_16, permute_17, permute_30]
        
args = [((320, 320), (320, 1), torch.float32, 'cuda'), ((320, 768), (768, 1), torch.float32, 'cuda'), ((320, 768), (768, 1), torch.float32, 'cuda'), ((320, 320), (320, 1), torch.float32, 'cuda'), ((320,), (1,), torch.float32, 'cuda'), ((2, 4096, 320), (1310720, 320, 1), torch.float32, 'cuda'), ((2, 77, 768), (59136, 768, 1), torch.float32, 'cuda')]
args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args]
mod = make_fx(Repro(), tracing_mode='real')(*args)

from torch._inductor.compile_fx import compile_fx_inner
from torch._dynamo.debug_utils import same_two_models

compiled = compile_fx_inner(mod, args)
class AccuracyError(Exception):
    pass
if not same_two_models(mod, compiled, args, only_fwd=True):
    raise AccuracyError("Bad accuracy detected")
```
```
[2023-02-27 23:08:38,682] torch._dynamo.utils: [ERROR] RMSE (res-fp64): nan, (ref-fp64): nan and shape=torch.Size([2, 4096, 320])
Traceback (most recent call last):
  File "repro.py", line 116, in <module>
    raise AccuracyError("Bad accuracy detected")
__main__.AccuracyError: Bad accuracy detected
```


### Versions

```
Collecting environment information...
PyTorch version: 2.0.0a0+git9ded087
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~18.04) 9.4.0
Clang version: 13.0.1-++20220120110844+75e33f71c2da-1~exp1~20220120230854.66
CMake version: version 3.25.2
Libc version: glibc-2.27

Python version: 3.8.8 (default, Apr 13 2021, 19:58:26)  [GCC 7.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-1094-aws-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB

Nvidia driver version: 520.61.05
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.7.6.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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
Byte Order:          Little Endian
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz
Stepping:            4
CPU MHz:             1808.897
BogoMIPS:            4999.99
Hypervisor vendor:   KVM
Virtualization type: full
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            33792K
NUMA node0 CPU(s):   0-23,48-71
NUMA node1 CPU(s):   24-47,72-95
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke

Versions of relevant libraries:
[pip3] mypy==1.0.0
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.2
[pip3] torch==2.0.0a0+git34617d7
[pip3] torchvision==0.15.0a0+af04819
[conda] blas                      1.0                         mkl  
[conda] mkl                       2021.2.0           h06a4308_296  
[conda] mkl-service               2.3.0            py38h27cfd23_1  
[conda] mkl_fft                   1.3.0            py38h42c9631_2  
[conda] mkl_random                1.2.1            py38ha9443f7_2  
[conda] numpy                     1.22.2                   pypi_0    pypi
[conda] torch                     2.0.0a0+git34617d7           dev_0    <develop>
[conda] torchvision               0.15.0a0+af04819           dev_0    <develop>
```

cc @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire