# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_9/inductor_cache/2x/c2xhoblha6s7iwnyxeyrs5rr6r3w337d4snkqkp3aq3l2xtfn63s.py
# Topologically Sorted Source Nodes: [sub], Original ATen: [aten.sub]
# Source node to ATen node mapping:
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select, %select_1), kwargs = {})
triton_poi_fused_sub_0 = async_compile.triton('triton_poi_fused_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub], Original ATen: [aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sub_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sub_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf0,


def get_module_name(cls_or_func):
    module_name = cls_or_func.__module__
    if module_name == '__main__':
        for frm in inspect.stack():
            if inspect.getmodule(frm[0]).__name__ == '__main__':
                main_file_path = Path(inspect.getsourcefile(frm[0]))
                if not Path().samefile(main_file_path.parent):
                    raise RuntimeError(
                        f'You are using "{main_file_path}" to launch your experiment, please launch the experiment under the directory where "{main_file_path.name}" is located.'
                        )
                module_name = main_file_path.stem
                break
    if module_name == '__main__':
        warnings.warn(
            'Callstack exhausted but main module still not found. This will probably cause issues that the function/class cannot be imported.'
            )
    if (f'{cls_or_func.__module__}.{cls_or_func.__name__}' ==
        'torch.nn.modules.rnn.LSTM'):
        module_name = cls_or_func.__module__
    return module_name


def reset_uid(namespace: 'str'='default') ->None:
    _last_uid[namespace] = 0


def _create_wrapper_cls(cls, store_init_parameters=True, reset_mutation_uid
    =False, stop_parsing=True):

    class wrapper(cls):

        def __init__(self, *args, **kwargs):
            self._stop_parsing = stop_parsing
            if reset_mutation_uid:
                reset_uid('mutation')
            if store_init_parameters:
                argname_list = list(inspect.signature(cls.__init__).
                    parameters.keys())[1:]
                full_args = {}
                full_args.update(kwargs)
                assert len(args) <= len(argname_list
                    ), f'Length of {args} is greater than length of {argname_list}.'
                for argname, value in zip(argname_list, args):
                    full_args[argname] = value
                args = list(args)
                for i, value in enumerate(args):
                    if isinstance(value, Translatable):
                        args[i] = value._translate()
                for i, value in kwargs.items():
                    if isinstance(value, Translatable):
                        kwargs[i] = value._translate()
                self._init_parameters = full_args
            else:
                self._init_parameters = {}
            super().__init__(*args, **kwargs)
    wrapper.__module__ = get_module_name(cls)
    wrapper.__name__ = cls.__name__
    wrapper.__qualname__ = cls.__qualname__
    wrapper.__init__.__doc__ = cls.__init__.__doc__
    return wrapper


def serialize_cls(cls):
    """
    To create an serializable class.
    """
    return _create_wrapper_cls(cls)


def basic_unit(cls):
    """
    To wrap a module as a basic unit, to stop it from parsing and make it mutate-able.
    """
    import torch.nn as nn
    assert issubclass(cls, nn.Module
        ), 'When using @basic_unit, the class must be a subclass of nn.Module.'
    return serialize_cls(cls)


class Translatable(abc.ABC):
    """
    Inherit this class and implement ``translate`` when the inner class needs a different
    parameter from the wrapper class in its init function.
    """

    @abc.abstractmethod
    def _translate(self) ->Any:
        pass


@basic_unit
class BinaryMinusNew(nn.Module):

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
