# 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_8/inductor_cache/pw/cpw5jgywzg5ntkknxkt5orxsrrr5zq7a6eoteboi3ba7zrcxj2p7.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    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_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_0(in_out_ptr0, in_ptr0, 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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rj/crjpl3jmbmwnxwonla2tvvkbjlcyv3hdapcyjhr3ordv2xdlf6yc.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_1 => cat
# Graph fragment:
#   %cat : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + ((-4) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/xo/cxolpdzkubgo5ufahxrzpfdk2h2v2eeb7ifew2xrw2lmz477xrd5.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_3 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %where_1], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 12
    x0 = xindex % 16
    x2 = (xindex // 192)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (128*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 12, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + ((-8) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cg/ccghnjdc3iqeymd2jwhlytana6ybxj7gmrbkwgmbda5m5dmbwjcb.py
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_5 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%cat_1, %where_2], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x2 = (xindex // 256)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 12, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (192*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 16, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-12) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + ((-12) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/76/c7665mpifcix3ip26ptnuqzlnmv5yympzortnftxpi7rmf66ctna.py
# Topologically Sorted Source Nodes: [conv2d_3, out_6, add], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
# Source node to ATen node mapping:
#   add => add
#   conv2d_3 => convolution_3
#   out_6 => gt_3, mul_3, where_3
# Graph fragment:
#   %convolution_3 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_11, %primals_12, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %convolution_3), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_3, %primals_3), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_4 = async_compile.triton('triton_poi_fused__prelu_kernel_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: '*fp32', 5: '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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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__prelu_kernel_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, 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')
    tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, 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, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, 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, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_3, buf1, primals_4, buf2, 512, grid=grid(512), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1), padding=(1, 1), 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 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf4, primals_6, 256, grid=grid(256), stream=stream0)
        del primals_6
        buf5 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf2, buf4, primals_7, buf5, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf7, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf5, buf7, primals_10, buf8, 1024, grid=grid(1024), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf10 = buf9; del buf9  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_3, out_6, add], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_4.run(buf10, primals_12, primals_13, primals_3, buf11, 256, grid=grid(256), stream=stream0)
        del primals_12
    return (buf11, primals_1, primals_3, primals_4, primals_5, primals_7, primals_8, primals_10, primals_11, primals_13, buf1, buf2, buf4, buf5, buf7, buf8, buf10, )


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, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = 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, primals_10, primals_11, primals_12, primals_13])
    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


class DenoisingBlock(nn.Module):

    def __init__(self, in_channels, inner_channels, out_channels):
        super(DenoisingBlock, self).__init__()
        self.conv_0 = nn.Conv2d(in_channels, inner_channels, 3, padding=1)
        self.conv_1 = nn.Conv2d(in_channels + inner_channels,
            inner_channels, 3, padding=1)
        self.conv_2 = nn.Conv2d(in_channels + 2 * inner_channels,
            inner_channels, 3, padding=1)
        self.conv_3 = nn.Conv2d(in_channels + 3 * inner_channels,
            out_channels, 3, padding=1)
        self.actv_0 = nn.PReLU(inner_channels)
        self.actv_1 = nn.PReLU(inner_channels)
        self.actv_2 = nn.PReLU(inner_channels)
        self.actv_3 = nn.PReLU(out_channels)

    def forward(self, x):
        out_0 = self.actv_0(self.conv_0(x))
        out_0 = torch.cat([x, out_0], 1)
        out_1 = self.actv_1(self.conv_1(out_0))
        out_1 = torch.cat([out_0, out_1], 1)
        out_2 = self.actv_2(self.conv_2(out_1))
        out_2 = torch.cat([out_1, out_2], 1)
        out_3 = self.actv_3(self.conv_3(out_2))
        return out_3 + x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'inner_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
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_convolution_0(in_out_ptr0, in_ptr0, 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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 128 * x2), tmp4 & xmask, other=0.0
        )
    tmp6 = tmp0 >= tmp3
    tl.full([1], 12, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + (-8 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 12, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 192 * x2), tmp4 & xmask, other=0.0
        )
    tmp6 = tmp0 >= tmp3
    tl.full([1], 16, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = 0.0
    tmp11 = tmp9 > tmp10
    tmp12 = tl.load(in_ptr2 + (-12 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp12 * tmp9
    tmp14 = tl.where(tmp11, tmp9, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_4(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, 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')
    tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, 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, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](primals_3, buf1, primals_4, buf2,
            512, XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(1, 1), 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 = buf3
        del buf3
        triton_poi_fused_convolution_0[grid(256)](buf4, primals_6, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_2[grid(768)](buf2, buf4, primals_7, buf5, 768,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_0[grid(256)](buf7, primals_9, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_3[grid(1024)](buf5, buf7, primals_10, buf8, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_4[grid(256)](buf10,
            primals_12, primals_13, primals_3, buf11, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_12
    return (buf11, primals_1, primals_3, primals_4, primals_5, primals_7,
        primals_8, primals_10, primals_11, primals_13, buf1, buf2, buf4,
        buf5, buf7, buf8, buf10)


class DenoisingBlockNew(nn.Module):

    def __init__(self, in_channels, inner_channels, out_channels):
        super(DenoisingBlockNew, self).__init__()
        self.conv_0 = nn.Conv2d(in_channels, inner_channels, 3, padding=1)
        self.conv_1 = nn.Conv2d(in_channels + inner_channels,
            inner_channels, 3, padding=1)
        self.conv_2 = nn.Conv2d(in_channels + 2 * inner_channels,
            inner_channels, 3, padding=1)
        self.conv_3 = nn.Conv2d(in_channels + 3 * inner_channels,
            out_channels, 3, padding=1)
        self.actv_0 = nn.PReLU(inner_channels)
        self.actv_1 = nn.PReLU(inner_channels)
        self.actv_2 = nn.PReLU(inner_channels)
        self.actv_3 = nn.PReLU(out_channels)

    def forward(self, input_0):
        primals_1 = self.conv_0.weight
        primals_2 = self.conv_0.bias
        primals_5 = self.conv_1.weight
        primals_4 = self.conv_1.bias
        primals_8 = self.conv_2.weight
        primals_6 = self.conv_2.bias
        primals_11 = self.conv_3.weight
        primals_7 = self.conv_3.bias
        primals_9 = self.actv_0.weight
        primals_10 = self.actv_1.weight
        primals_12 = self.actv_2.weight
        primals_13 = self.actv_3.weight
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
