os.environ['TORCHINDUCTOR_CACHE_DIR'] = '/var/folders/fd/kcv8m1kn0lqgxz42wvgr46sc0000gn/T/torchinductor_skarjala'

import torch
from torch import tensor, device
import torch.fx as fx
from torch._dynamo.testing import rand_strided
from math import inf
import torch._inductor.inductor_prims
import torch.distributed as dist
from torch.testing._internal.distributed.fake_pg import FakeStore

import torch._dynamo.config
import torch._inductor.config
import torch._functorch.config
import torch.fx.experimental._config


torch._functorch.config.functionalize_rng_ops = False
torch._functorch.config.fake_tensor_allow_unsafe_data_ptr_access = True
torch._functorch.config.unlift_effect_tokens = True



isolate_fails_code_str = None




# torch version: 2.9.0a0+gitf23d314
# torch cuda version: None
# torch git version: f23d31463ca452918e23063409a2bdc55efc0d46


# torch.cuda.is_available()==False, no GPU info collected

from torch.nn import *
class Repro(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()

    
    
    def forward(self, arg0_1):
        all_reduce = torch.ops._c10d_functional.all_reduce.default(arg0_1, 'sum', '0')
        wait_tensor = torch.ops._c10d_functional.wait_tensor.default(all_reduce);  all_reduce = None
        mul = torch.ops.aten.mul.Tensor(wait_tensor, 2)
        copy_ = torch.ops.aten.copy_.default(arg0_1, wait_tensor);  arg0_1 = wait_tensor = copy_ = None
        return (mul,)
        
def load_args(reader):
    buf0 = reader.storage(None, 64)
    reader.tensor(buf0, (4, 4), is_leaf=True)  # arg0_1
load_args._version = 0
mod = Repro()
if __name__ == '__main__':
    from torch._dynamo.repro.after_aot import run_repro
    # Initialize FakeProcessGroup for distributed operations
    store = FakeStore()
    dist.init_process_group(
        backend="fake",
        rank=0,
        world_size=2,
        store=store
    )
    with torch.no_grad():
        run_repro(mod, load_args, accuracy=False, command='run', save_dir=None, tracing_mode='real', check_str=None)
        # To run it separately, do 
        # mod, args = run_repro(mod, load_args, accuracy=False, command='get_args', save_dir=None, tracing_mode='real', check_str=None)
        # mod(*args)
    dist.destroy_process_group()