# AOT ID: ['0_forward']
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_4/inductor_cache/7l/c7l4yh3rq3amhzr2k5aw6lwb4sevj6dj4jt7xr4rxsrjphk5dvwi.py
# Topologically Sorted Source Nodes: [abs_1, abs_2, edge], Original ATen: [aten.abs, aten.add]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   edge => add
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%convolution,), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%convolution_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, %abs_2), kwargs = {})
triton_poi_fused_abs_add_0 = async_compile.triton('triton_poi_fused_abs_add_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_add_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_abs_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 507904
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp2 = tl.load(in_ptr1 + (x0), None)
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp1 + tmp3
    tl.store(out_ptr0 + (x0), tmp4, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (31, 31, 3, 3), (279, 9, 3, 1))
    assert_size_stride(primals_2, (4, 31, 64, 64), (126976, 4096, 64, 1))
    assert_size_stride(primals_3, (31, 31, 3, 3), (279, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [edge_x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 31, 64, 64), (126976, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [edge_y], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 31, 64, 64), (126976, 4096, 64, 1))
        buf2 = empty_strided_cuda((4, 31, 64, 64), (126976, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [abs_1, abs_2, edge], Original ATen: [aten.abs, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_add_0.run(buf0, buf1, buf2, 507904, grid=grid(507904), stream=stream0)
    return (buf2, primals_1, primals_2, primals_3, buf0, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((31, 31, 3, 3), (279, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 31, 64, 64), (126976, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((31, 31, 3, 3), (279, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 torch.nn as nn


class SobelConv(nn.Module):

    def __init__(self, in_channel=31, batch_num=16):
        super(SobelConv, self).__init__()
        self.bz = batch_num
        self.in_channel = in_channel
        self.convx = nn.Conv2d(in_channels=31, out_channels=31, kernel_size
            =3, stride=1, bias=False, padding=1)
        self.convy = nn.Conv2d(in_channels=31, out_channels=31, kernel_size
            =3, stride=1, bias=False, padding=1)

    def init_weight(self):
        temp_x = torch.tensor([[-1, 0, -1], [2, 0, 2], [-1, 0, -1]])
        temp_y = torch.tensor([[-1, 2, -1], [0, 0, 0], [-1, 2, -1]])
        param_x = torch.zeros_like(self.convx.weight)
        param_y = torch.zeros_like(self.convy.weight)
        for index in range(self.in_channel):
            param_x[index, index, :, :] = temp_x
            param_y[index, index, :, :] = temp_y
        self.convx.weight = nn.Parameter(param_x)
        self.convy.weight = nn.Parameter(param_y)

    def forward(self, input_x):
        edge_x = self.convx(input_x)
        edge_y = self.convy(input_x)
        edge = edge_x.abs() + edge_y.abs()
        return edge


def get_inputs():
    return [torch.rand([4, 31, 64, 64])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
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_abs_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp2 = tl.load(in_ptr1 + x0, None)
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp1 + tmp3
    tl.store(out_ptr0 + x0, tmp4, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (31, 31, 3, 3), (279, 9, 3, 1))
    assert_size_stride(primals_2, (4, 31, 64, 64), (126976, 4096, 64, 1))
    assert_size_stride(primals_3, (31, 31, 3, 3), (279, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 31, 64, 64), (126976, 4096, 64, 1))
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 31, 64, 64), (126976, 4096, 64, 1))
        buf2 = empty_strided_cuda((4, 31, 64, 64), (126976, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_add_0[grid(507904)](buf0, buf1, buf2, 507904,
            XBLOCK=512, num_warps=8, num_stages=1)
    return buf2, primals_1, primals_2, primals_3, buf0, buf1


class SobelConvNew(nn.Module):

    def __init__(self, in_channel=31, batch_num=16):
        super(SobelConvNew, self).__init__()
        self.bz = batch_num
        self.in_channel = in_channel
        self.convx = nn.Conv2d(in_channels=31, out_channels=31, kernel_size
            =3, stride=1, bias=False, padding=1)
        self.convy = nn.Conv2d(in_channels=31, out_channels=31, kernel_size
            =3, stride=1, bias=False, padding=1)

    def init_weight(self):
        temp_x = torch.tensor([[-1, 0, -1], [2, 0, 2], [-1, 0, -1]])
        temp_y = torch.tensor([[-1, 2, -1], [0, 0, 0], [-1, 2, -1]])
        param_x = torch.zeros_like(self.convx.weight)
        param_y = torch.zeros_like(self.convy.weight)
        for index in range(self.in_channel):
            param_x[index, index, :, :] = temp_x
            param_y[index, index, :, :] = temp_y
        self.convx.weight = nn.Parameter(param_x)
        self.convy.weight = nn.Parameter(param_y)

    def forward(self, input_0):
        primals_1 = self.convx.weight
        primals_3 = self.convy.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
