Repository: pytorch
Issue: Bug on Minified repro example 
Labels: triaged,oncall: pt2,module: minifier

### 🐛 Describe the bug

I created a minified repro to examine the cause of the runtime error (as the compiler seems to have no error report).

The card used to generate the repro is cuda:7. 

Then I ran the gerenreated repro. Now the repro complain runtime error as Unhandled FakeTensor Device Propagation for aten.mul.Tensor, found two different devices cuda:0, cuda:7
(scroll up for backtrace)

As I think we cant ensure to use cuda:0 to debug every time. I think it should be fixed on Minified repro. 

### Error logs

發生例外狀況: TorchRuntimeError


from user code:
   File "/code/EnlightDiff/torch_compile_debug/run_2023_03_28_13_57_57_112729-pid_1716815/minifier/minifier_launcher.py", line 43, in forward
    mul_1 = getitem * getitem_1;  getitem = getitem_1 = None

Last frame execution written to /code/EnlightDiff/torch_compile_debug/run_2023_03_28_14_01_45_743159-pid_1727270/error_recordings/forward_TorchRuntimeError_37.rec. To run only this frame while debugging, run torch._dynamo.replay('/code/EnlightDiff/torch_compile_debug/run_2023_03_28_14_01_45_743159-pid_1727270/error_recordings/forward_TorchRuntimeError_37.rec').

You can suppress this exception and fall back to eager by setting:
    torch._dynamo.config.suppress_errors = True
RuntimeError: Unhandled FakeTensor Device Propagation for aten.mul.Tensor, found two different devices cuda:0, cuda:7

The above exception was the direct cause of the following exception:

RuntimeError: Failed running call_function <built-in function mul>(*(FakeTensor(FakeTensor(..., device='meta', size=(32, 1), dtype=torch.int64), cuda:0), FakeTensor(FakeTensor(..., device='meta', size=(1, 32)), cuda:7)), **{}):
Unhandled FakeTensor Device Propagation for aten.mul.Tensor, found two different devices cuda:0, cuda:7
(scroll up for backtrace)

The above exception was the direct cause of the following exception:

  File "/code/EnlightDiff/torch_compile_debug/run_2023_03_28_13_57_57_112729-pid_1716815/minifier/minifier_launcher.py", line 69, in <module>
    opt_mod(*args)
torch._dynamo.exc.TorchRuntimeError: 

from user code:
   File "/code/EnlightDiff/torch_compile_debug/run_2023_03_28_13_57_57_112729-pid_1716815/minifier/minifier_launcher.py", line 43, in forward
    mul_1 = getitem * getitem_1;  getitem = getitem_1 = None

Last frame execution written to /code/EnlightDiff/torch_compile_debug/run_2023_03_28_14_01_45_743159-pid_1727270/error_recordings/forward_TorchRuntimeError_37.rec. To run only this frame while debugging, run torch._dynamo.replay('/code/EnlightDiff/torch_compile_debug/run_2023_03_28_14_01_45_743159-pid_1727270/error_recordings/forward_TorchRuntimeError_37.rec').



You can suppress this exception and fall back to eager by setting:
    torch._dynamo.config.suppress_errors = True


### Minified repro
```

import os
from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import functools
import torch._dynamo
from torch._dynamo.debug_utils import run_fwd_maybe_bwd
from torch._dynamo.backends.registry import lookup_backend
from torch._dynamo.testing import rand_strided

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\x88X\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\x10\x00\x00\x00guard_nn_modulesq\x0b\x89X\x1b\x00\x00\x00traceable_tensor_subclassesq\x0cc__builtin__\nset\nq\r]q\x0e\x85q\x0fRq\x10X\x0f\x00\x00\x00suppress_errorsq\x11\x89X\x15\x00\x00\x00replay_record_enabledq\x12\x88X \x00\x00\x00rewrite_assert_with_torch_assertq\x13\x88X\x12\x00\x00\x00print_graph_breaksq\x14\x89X\x07\x00\x00\x00disableq\x15\x89X*\x00\x00\x00allowed_functions_module_string_ignorelistq\x16h\r]q\x17(X\r\x00\x00\x00torch.testingq\x18X\r\x00\x00\x00torch._decompq\x19X\x0c\x00\x00\x00torch._primsq\x1aX\x13\x00\x00\x00torch.distributionsq\x1bX\x0b\x00\x00\x00torch._refsq\x1ce\x85q\x1dRq\x1eX\x12\x00\x00\x00repro_forward_onlyq\x1f\x89X\x0f\x00\x00\x00repro_toleranceq G?PbM\xd2\xf1\xa9\xfcX\x16\x00\x00\x00capture_scalar_outputsq!\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>\x00\x00\x00/home/cychan/mambaforge/envs/ldm3/lib/python3.10/site-packagesq*X\x0e\x00\x00\x00debug_dir_rootq+X%\x00\x00\x00/code/EnlightDiff/torch_compile_debugq,X)\x00\x00\x00DO_NOT_USE_legacy_non_fake_example_inputsq-\x89X\x13\x00\x00\x00_save_config_ignoreq.h\r]q/(X\x0b\x00\x00\x00repro_levelq0X\x0b\x00\x00\x00repro_afterq1X\x12\x00\x00\x00constant_functionsq2X!\x00\x00\x00skipfiles_inline_module_allowlistq3e\x85q4Rq5u.')
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\x17\x00\x00\x00realize_reads_thresholdq\x10K\x04X\x17\x00\x00\x00realize_bytes_thresholdq\x11M\xd0\x07X\x1b\x00\x00\x00realize_acc_reads_thresholdq\x12K\x08X\x0f\x00\x00\x00fallback_randomq\x13\x89X\x12\x00\x00\x00implicit_fallbacksq\x14\x88X\x0b\x00\x00\x00tune_layoutq\x15\x89X\x11\x00\x00\x00aggressive_fusionq\x16\x89X\x0f\x00\x00\x00max_fusion_sizeq\x17K@X\x1b\x00\x00\x00unroll_reductions_thresholdq\x18K\x08X\x0e\x00\x00\x00comment_originq\x19\x89X\x12\x00\x00\x00developer_warningsq\x1a\x89X\x0f\x00\x00\x00compile_threadsq\x1bK X\x13\x00\x00\x00kernel_name_max_opsq\x1cK\nX\r\x00\x00\x00shape_paddingq\x1d\x89X\x0e\x00\x00\x00permute_fusionq\x1e\x89X\x1a\x00\x00\x00profiler_mark_wrapper_callq\x1f\x89X\x18\x00\x00\x00_raise_error_for_testingq \x89X\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.cudagraphsq*\x89X\x17\x00\x00\x00triton.debug_sync_graphq+\x89X\x18\x00\x00\x00triton.debug_sync_kernelq,\x89X\x15\x00\x00\x00triton.dense_indexingq-\x89X\x10\x00\x00\x00triton.max_tilesq.K\x02X\x19\x00\x00\x00triton.autotune_pointwiseq/\x88X\'\x00\x00\x00triton.tiling_prevents_pointwise_fusionq0\x88X\'\x00\x00\x00triton.tiling_prevents_reduction_fusionq1\x88X\x1b\x00\x00\x00triton.ordered_kernel_namesq2\x89X\x1f\x00\x00\x00triton.descriptive_kernel_namesq3\x89X\x1c\x00\x00\x00triton.persistent_reductionsq4\x89X\r\x00\x00\x00trace.enabledq5\x89X\x0f\x00\x00\x00trace.debug_logq6\x88X\x0e\x00\x00\x00trace.info_logq7\x89X\x0e\x00\x00\x00trace.fx_graphq8\x88X\x1a\x00\x00\x00trace.fx_graph_transformedq9\x88X\x13\x00\x00\x00trace.ir_pre_fusionq:\x88X\x14\x00\x00\x00trace.ir_post_fusionq;\x88X\x11\x00\x00\x00trace.output_codeq<\x88X\x13\x00\x00\x00trace.graph_diagramq=\x89X\x15\x00\x00\x00trace.compile_profileq>\x89X\x10\x00\x00\x00trace.upload_tarq?Nu.')
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


args = [((32, 3, 160, 160), (76800, 25600, 160, 1), torch.float32, 'cuda', False), ((32,), (1,), torch.int64, 'cuda', False), ((32, 3, 160, 160), (76800, 25600, 160, 1), torch.float16, 'cuda', False)]
args = [rand_strided(sh, st, dt, dev).requires_grad_(rg) for (sh, st, dt, dev, rg) in args]


from torch.nn import *
class Repro(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.self_mlp_0 = Linear(in_features=64, out_features=256, bias=True).cuda()
        self.self_mlp_2 = Linear(in_features=256, out_features=64, bias=True).cuda()



    def forward(self, x : torch.Tensor, time : torch.Tensor, cond : torch.Tensor):
        arange = torch.arange(32, device = device(type='cuda', index=7))
        mul = arange * -0.2971077539347156;  arange = None
        exp = torch.exp(mul);  mul = None
        getitem = time[(slice(None, None, None), None)];  time = None
        getitem_1 = exp[(None, slice(None, None, None))];  exp = None
        mul_1 = getitem * getitem_1;  getitem = getitem_1 = None
        sin = mul_1.sin()
        cos = mul_1.cos();  mul_1 = None
        cat = torch.cat((sin, cos), dim = -1);  sin = cos = None
        self_mlp_0 = self.self_mlp_0(cat);  cat = None
        softplus = torch._C._nn.softplus(self_mlp_0)
        tanh = torch.tanh(softplus);  softplus = None
        mul_2 = self_mlp_0 * tanh;  self_mlp_0 = tanh = None
        self_mlp_2 = self.self_mlp_2(mul_2);  mul_2 = None
        cat_1 = torch.cat((x, cond), dim = 1);  x = cond = None
        return (cat_1, self_mlp_2)


mod = Repro()

# Setup debug minifier compiler
torch._dynamo.debug_utils.MINIFIER_SPAWNED = True
compiler_fn = lookup_backend("dynamo_minifier_backend")

dynamo_minifier_backend = functools.partial(
    compiler_fn,
    compiler_name="inductor",
)
opt_mod = torch._dynamo.optimize(dynamo_minifier_backend)(mod)

with torch.cuda.amp.autocast(enabled=True):
    opt_mod(*args)

```
### Versions

Collecting environment information...
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.10.9 | packaged by conda-forge | (main, Feb  2 2023, 20:20:04) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
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
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.85.05
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          48
On-line CPU(s) list:             0-47
Thread(s) per core:              2
Core(s) per socket:              12
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Gold 6256 CPU @ 3.60GHz
Stepping:                        7
CPU MHz:                         1713.576
CPU max MHz:                     4500.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        7200.00
Virtualization:                  VT-x
L1d cache:                       768 KiB
L1i cache:                       768 KiB
L2 cache:                        24 MiB
L3 cache:                        66 MiB
NUMA node0 CPU(s):               0-11,24-35
NUMA node1 CPU(s):               12-23,36-47
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; Enhanced IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; TSX disabled
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] lion-pytorch==0.0.7
[pip3] numpy==1.24.2
[pip3] pytorch-lightning==2.0.0
[pip3] torch==2.0.0
[pip3] torchmetrics==0.11.4
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl    conda-forge
[conda] cudatoolkit               11.8.0              h37601d7_11    conda-forge
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] lion-pytorch              0.0.7                    pypi_0    pypi
[conda] mkl                       2022.2.1         h84fe81f_16997    conda-forge
[conda] numpy                     1.24.2          py310h8deb116_0    conda-forge
[conda] pytorch                   2.0.0           py3.10_cuda11.8_cudnn8.7.0_0    pytorch
[conda] pytorch-cuda              11.8                 h7e8668a_3    pytorch
[conda] pytorch-lightning         2.0.0              pyhd8ed1ab_1    conda-forge
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.11.4             pyhd8ed1ab_0    conda-forge
[conda] torchtriton               2.0.0                     py310    pytorch
[conda] torchvision               0.15.0              py310_cu118    pytorch

cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh