# 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_6/inductor_cache/lp/clp5td7lbqtje3pt7v6xbcp766swgazqemomz2nzsxtdtmjesxht.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = 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 = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[262144], 
    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_relu_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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/el/celk5adxq2zxhymud4napv6zuj52qpmoxz7wanhqayimjnku6r23.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_1 => cat
# Graph fragment:
#   %cat : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %convolution_1], 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=[524288], 
    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 = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 32
    x0 = xindex % 4096
    x2 = (xindex // 131072)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 32, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-16) + x1)) + (65536*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-16) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/xq/cxqtpoguh5q4lo7aqfwpnku6psmnd6vobmblp2f7t5p2vhk4zkez.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_2 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %convolution_2], 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=[1048576], 
    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 = 786432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 48
    x0 = xindex % 4096
    x2 = (xindex // 196608)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (131072*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 48, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-32) + x1)) + (65536*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-32) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/sj/csjg2l7n7qdes27kdgvihemqiptcame7dsxapgvl7acr5yl7mnct.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_3 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%cat_1, %convolution_3], 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=[1048576], 
    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 = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 64
    x0 = xindex % 4096
    x2 = (xindex // 262144)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 48, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (196608*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 64, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-48) + x1)) + (65536*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-48) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yz/cyzylkdlkpucjaq3h4cacl42tq4z6bgxuiyx4ieis4qfq4hjhblf.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_1 => convolution_4
#   input_2 => relu_1
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_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=[524288], 
    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_relu_4', '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_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gy/cgybnbhivv6t3vqzcx7fdsc65yls2agg5sbpwn7byk6wweoeh5vd.py
# Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   input_5 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_5', '''
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=[16384], 
    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_5', '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_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    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_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, None)
''', 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, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (32, ), (1, ))
    assert_size_stride(primals_12, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (16, ), (1, ))
    assert_size_stride(primals_14, (1, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (1, ), (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, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 262144, grid=grid(262144), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf1, buf2, primals_5, buf3, 524288, grid=grid(524288), stream=stream0)
        del buf2
        del primals_5
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf5 = empty_strided_cuda((4, 48, 64, 64), (196608, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf3, buf4, primals_7, buf5, 786432, grid=grid(786432), stream=stream0)
        del buf4
        del primals_7
        # Topologically Sorted Source Nodes: [out_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, 16, 64, 64), (65536, 4096, 64, 1))
        buf7 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf5, buf6, primals_9, buf7, 1048576, grid=grid(1048576), stream=stream0)
        del buf6
        del primals_9
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf9, primals_11, 524288, grid=grid(524288), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [input_3, input_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf11, primals_13, 262144, grid=grid(262144), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf13, primals_15, 16384, grid=grid(16384), stream=stream0)
        del primals_15
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, buf1, buf3, buf5, buf7, buf9, buf11, )


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((16, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 48, 3, 3), (432, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((1, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((1, ), (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, primals_14, primals_15])
    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 Decoder(nn.Module):

    def __init__(self):
        super(Decoder, self).__init__()
        self.layers = nn.Sequential()
        self.layers.add_module('Conv3', nn.Conv2d(64, 32, 3, 1, 1))
        self.layers.add_module('Act3', nn.ReLU(inplace=True))
        self.layers.add_module('Conv4', nn.Conv2d(32, 16, 3, 1, 1))
        self.layers.add_module('Act4', nn.ReLU(inplace=True))
        self.layers.add_module('Conv5', nn.Conv2d(16, 1, 3, 1, 1))

    def forward(self, x):
        return self.layers(x)


class Encoder(nn.Module):

    def __init__(self):
        super(Encoder, self).__init__()
        self.Conv1 = nn.Conv2d(1, 16, 3, 1, 1)
        self.Relu = nn.ReLU(inplace=True)
        self.layers = nn.ModuleDict({'DenseConv1': nn.Conv2d(16, 16, 3, 1, 
            1), 'DenseConv2': nn.Conv2d(32, 16, 3, 1, 1), 'DenseConv3': nn.
            Conv2d(48, 16, 3, 1, 1)})

    def forward(self, x):
        x = self.Relu(self.Conv1(x))
        for i in range(len(self.layers)):
            out = self.layers['DenseConv' + str(i + 1)](x)
            x = torch.cat([x, out], 1)
        return x


class architecture(nn.Module):

    def __init__(self):
        super(architecture, self).__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()

    def forward(self, x):
        return self.decoder(self.encoder(x))


def get_inputs():
    return [torch.rand([4, 1, 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 import triton_helpers
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 32
    x0 = xindex % 4096
    x2 = xindex // 131072
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 32, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-16 + x1) + 65536 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-16 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 48
    x0 = xindex % 4096
    x2 = xindex // 196608
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 131072 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 48, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-32 + x1) + 65536 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-32 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 64
    x0 = xindex % 4096
    x2 = xindex // 262144
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 48, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 196608 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-48 + x1) + 65536 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-48 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_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_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, None)


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, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (16,), (1,))
    assert_size_stride(primals_14, (1, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (1,), (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, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(262144)](buf1, primals_2, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(524288)](buf1, buf2, primals_5, buf3, 
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del buf2
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf5 = empty_strided_cuda((4, 48, 64, 64), (196608, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_2[grid(786432)](buf3, buf4, primals_7, buf5, 
            786432, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf4
        del primals_7
        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, 16, 64, 64), (65536, 4096, 64, 1))
        buf7 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_3[grid(1048576)](buf5, buf6, primals_9, buf7, 
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf6
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_4[grid(524288)](buf9, primals_11,
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_0[grid(262144)](buf11, primals_13,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_13
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_5[grid(16384)](buf13, primals_15, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, buf1, buf3, buf5, buf7, buf9, buf11
        )


class Decoder(nn.Module):

    def __init__(self):
        super(Decoder, self).__init__()
        self.layers = nn.Sequential()
        self.layers.add_module('Conv3', nn.Conv2d(64, 32, 3, 1, 1))
        self.layers.add_module('Act3', nn.ReLU(inplace=True))
        self.layers.add_module('Conv4', nn.Conv2d(32, 16, 3, 1, 1))
        self.layers.add_module('Act4', nn.ReLU(inplace=True))
        self.layers.add_module('Conv5', nn.Conv2d(16, 1, 3, 1, 1))

    def forward(self, x):
        return self.layers(x)


class Encoder(nn.Module):

    def __init__(self):
        super(Encoder, self).__init__()
        self.Conv1 = nn.Conv2d(1, 16, 3, 1, 1)
        self.Relu = nn.ReLU(inplace=True)
        self.layers = nn.ModuleDict({'DenseConv1': nn.Conv2d(16, 16, 3, 1, 
            1), 'DenseConv2': nn.Conv2d(32, 16, 3, 1, 1), 'DenseConv3': nn.
            Conv2d(48, 16, 3, 1, 1)})

    def forward(self, x):
        x = self.Relu(self.Conv1(x))
        for i in range(len(self.layers)):
            out = self.layers['DenseConv' + str(i + 1)](x)
            x = torch.cat([x, out], 1)
        return x


class architectureNew(nn.Module):

    def __init__(self):
        super(architectureNew, self).__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()

    def forward(self, input_0):
        primals_1 = self.encoder.Conv1.weight
        primals_2 = self.encoder.Conv1.bias
        primals_4 = self.encoder.layers.DenseConv1.weight
        primals_5 = self.encoder.layers.DenseConv1.bias
        primals_6 = self.encoder.layers.DenseConv2.weight
        primals_7 = self.encoder.layers.DenseConv2.bias
        primals_8 = self.encoder.layers.DenseConv3.weight
        primals_9 = self.encoder.layers.DenseConv3.bias
        primals_10 = self.decoder.layers.Conv3.weight
        primals_11 = self.decoder.layers.Conv3.bias
        primals_12 = self.decoder.layers.Conv4.weight
        primals_13 = self.decoder.layers.Conv4.bias
        primals_14 = self.decoder.layers.Conv5.weight
        primals_15 = self.decoder.layers.Conv5.bias
        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, primals_14,
            primals_15])
        return output[0]
