# 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_1/inductor_cache/ej/cejbaw3u6a6ms3irj4kumrcxpifvb2l5rykaqk7fbzej6akuba3y.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 0, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 6
    x2 = (xindex // 24)
    x3 = xindex % 24
    x4 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((-4) + x3 + (16*x2)), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/7r/c7rf7t47ugrrljnac7yn3tts4ejnsegxpwszwvc6aw6xc4esqj7a.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_1 => convolution
#   x_2 => constant_pad_nd_1
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%convolution, [1, 1, 0, 0], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_convolution_1', '''
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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_constant_pad_nd_convolution_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x4 = (xindex // 6)
    x2 = (xindex // 24) % 4
    x5 = xindex
    tmp0 = (-1) + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((-1) + x0 + (4*x4)), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x2), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/rz/crzz77zyrjeawdii7kpwczc66hufsoxl7t6zpm5lr2jvxmthziwx.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_4 => constant_pad_nd_2
# Graph fragment:
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [1, 1, 0, 0], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_2 = async_compile.triton('triton_poi_fused_constant_pad_nd_2', '''
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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6)
    x2 = xindex
    tmp0 = (-1) + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((-1) + x0 + (4*x1)), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/7f/c7fv2odkmldcjl5nuu5vkxh3lyr6vlkp4rn6frplmkxdomm2qqss.py
# Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_5 => convolution_2
#   x_6 => constant_pad_nd_3
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_2, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %constant_pad_nd_3 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%convolution_2, [0, 0, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_3 = async_compile.triton('triton_poi_fused_constant_pad_nd_convolution_3', '''
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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_convolution_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_constant_pad_nd_convolution_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 6
    x4 = (xindex // 24)
    x5 = xindex % 24
    x2 = (xindex // 24) % 4
    x6 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((-4) + x5 + (16*x4)), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x2), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + (x6), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qs/cqss3i54iwr5rty66bqdpmwvq6qebsg2m22uzbv5qrpwikwr25fk.py
# Topologically Sorted Source Nodes: [x_3, x_7, out], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   out => add
#   x_3 => convolution_1
#   x_7 => convolution_3
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_3, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %convolution_3), kwargs = {})
triton_poi_fused_add_convolution_4 = async_compile.triton('triton_poi_fused_add_convolution_4', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask)
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 384, grid=grid(384), stream=stream0)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_1.run(buf1, primals_3, buf2, 384, grid=grid(384), stream=stream0)
        del buf1
        del primals_3
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_2.run(primals_1, buf4, 384, grid=grid(384), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_3.run(buf5, primals_7, buf6, 384, grid=grid(384), stream=stream0)
        del buf5
        del primals_7
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_3, x_7, out], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_4.run(buf8, primals_5, buf7, primals_9, 256, grid=grid(256), stream=stream0)
        del buf7
        del primals_5
        del primals_9
    return (buf8, primals_2, primals_4, primals_6, primals_8, buf0, buf2, buf4, buf6, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 1), (12, 3, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1, 3), (12, 3, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 1, 3), (12, 3, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 3, 1), (12, 3, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel


class Conv2D(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', stride=1, dilation=1, groups=1):
        super(Conv2D, self).__init__()
        assert type(kernel_size) in [int, tuple
            ], 'Allowed kernel type [int or tuple], not {}'.format(type(
            kernel_size))
        assert padding == 'same', 'Allowed padding type {}, not {}'.format(
            'same', padding)
        self.kernel_size = kernel_size
        if isinstance(kernel_size, tuple):
            self.h_kernel = kernel_size[0]
            self.w_kernel = kernel_size[1]
        else:
            self.h_kernel = kernel_size
            self.w_kernel = kernel_size
        self.padding = padding
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=self.stride,
            dilation=self.dilation, groups=self.groups)

    def forward(self, x):
        if self.padding == 'same':
            height, width = x.shape[2:]
            h_pad_need = max(0, (height - 1) * self.stride + self.h_kernel -
                height)
            w_pad_need = max(0, (width - 1) * self.stride + self.w_kernel -
                width)
            pad_left = w_pad_need // 2
            pad_right = w_pad_need - pad_left
            pad_top = h_pad_need // 2
            pad_bottom = h_pad_need - pad_top
            padding = pad_left, pad_right, pad_top, pad_bottom
            x = F.pad(x, padding, 'constant', 0)
        x = self.conv(x)
        return x


class GCN(nn.Module):
    """
        Large Kernel Matters -- https://arxiv.org/abs/1703.02719
    """

    def __init__(self, in_channels, out_channels, k=3):
        super(GCN, self).__init__()
        self.conv_l1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')
        self.conv_l2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')

    def forward(self, x):
        x1 = self.conv_l1(x)
        x1 = self.conv_l2(x1)
        x2 = self.conv_r1(x)
        x2 = self.conv_r2(x2)
        out = x1 + x2
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

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 import nn
import torch.nn.functional as F
import torch.nn.parallel
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x2 = xindex // 24
    x3 = xindex % 24
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x4 = xindex // 6
    x2 = xindex // 24 % 4
    x5 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x4), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x2, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_3(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x4 = xindex // 24
    x5 = xindex % 24
    x2 = xindex // 24 % 4
    x6 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-4 + x5 + 16 * x4), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x2, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x6, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(384)](primals_1, buf0, 384,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_1[grid(384)](buf1,
            primals_3, buf2, 384, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(384)](primals_1, buf4, 384,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_3[grid(384)](buf5,
            primals_7, buf6, 384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf5
        del primals_7
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf3
        del buf3
        triton_poi_fused_add_convolution_4[grid(256)](buf8, primals_5, buf7,
            primals_9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        del primals_5
        del primals_9
    return (buf8, primals_2, primals_4, primals_6, primals_8, buf0, buf2,
        buf4, buf6)


class Conv2D(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', stride=1, dilation=1, groups=1):
        super(Conv2D, self).__init__()
        assert type(kernel_size) in [int, tuple
            ], 'Allowed kernel type [int or tuple], not {}'.format(type(
            kernel_size))
        assert padding == 'same', 'Allowed padding type {}, not {}'.format(
            'same', padding)
        self.kernel_size = kernel_size
        if isinstance(kernel_size, tuple):
            self.h_kernel = kernel_size[0]
            self.w_kernel = kernel_size[1]
        else:
            self.h_kernel = kernel_size
            self.w_kernel = kernel_size
        self.padding = padding
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=self.stride,
            dilation=self.dilation, groups=self.groups)

    def forward(self, x):
        if self.padding == 'same':
            height, width = x.shape[2:]
            h_pad_need = max(0, (height - 1) * self.stride + self.h_kernel -
                height)
            w_pad_need = max(0, (width - 1) * self.stride + self.w_kernel -
                width)
            pad_left = w_pad_need // 2
            pad_right = w_pad_need - pad_left
            pad_top = h_pad_need // 2
            pad_bottom = h_pad_need - pad_top
            padding = pad_left, pad_right, pad_top, pad_bottom
            x = F.pad(x, padding, 'constant', 0)
        x = self.conv(x)
        return x


class GCNNew(nn.Module):
    """
        Large Kernel Matters -- https://arxiv.org/abs/1703.02719
    """

    def __init__(self, in_channels, out_channels, k=3):
        super(GCNNew, self).__init__()
        self.conv_l1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')
        self.conv_l2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')

    def forward(self, input_0):
        primals_2 = self.conv_l1.conv.weight
        primals_3 = self.conv_l1.conv.bias
        primals_4 = self.conv_l2.conv.weight
        primals_5 = self.conv_l2.conv.bias
        primals_6 = self.conv_r1.conv.weight
        primals_7 = self.conv_r1.conv.bias
        primals_8 = self.conv_r2.conv.weight
        primals_9 = self.conv_r2.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
