# 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/qq/cqqn7tlxdcm4zb7nkm6w3hjzdwit4umwk6atofainwhrrupkrsjm.py
# Topologically Sorted Source Nodes: [c1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   c1 => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [2, 2, 2], [1, 1, 1], [1, 1, 1], False, [0, 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=[524288], 
    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), 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': '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 287496
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 35937) % 2
    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_1/inductor_cache/xy/cxyzctptkdsxqlvj32ltxgeyuj6muiau4zpk5uhfrfouxvofx2qc.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   relu => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 39304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/gz/cgzzo3xju3tdjrx6efdxvn6wv6nu5tsxbwq7tuljz2kbr2zzgsyq.py
# Topologically Sorted Source Nodes: [c2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   c2 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2, 2], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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=[8192], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 729) % 2
    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_1/inductor_cache/2p/c2po63std6rjzg6ljkbrgb2n2zg33pihyp4z5fibow5ksg7fxi7y.py
# Topologically Sorted Source Nodes: [relu_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   relu_1 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
triton_poi_fused_relu_3 = async_compile.triton('triton_poi_fused_relu_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: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_3', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_3(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ut/cutnjnx2sw3uybvonjc366w7fm3mquokhchuwm6cyfhokvs7fmbs.py
# Topologically Sorted Source Nodes: [c3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   c3 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [2, 2, 2], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_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=[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_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 27) % 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_1/inductor_cache/r5/cr5wxjjtqa6q6k54wwtrmhpssb57zvdcjgf2l2jlinkcajaj5vef.py
# Topologically Sorted Source Nodes: [l1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   l1 => relu_2
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_5 = async_compile.triton('triton_poi_fused_relu_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=[32], 
    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_relu_5', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, 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 + (x2), tmp4, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64, 64), (262144, 262144, 4096, 64, 1))
    assert_size_stride(primals_2, (2, 1, 2, 2, 2), (8, 8, 4, 2, 1))
    assert_size_stride(primals_3, (2, ), (1, ))
    assert_size_stride(primals_4, (2, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_5, (2, ), (1, ))
    assert_size_stride(primals_6, (4, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (8, 4), (4, 1))
    assert_size_stride(primals_9, (8, ), (1, ))
    assert_size_stride(primals_10, (1, 8), (8, 1))
    assert_size_stride(primals_11, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [c1], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 33, 33, 33), (71874, 35937, 1089, 33, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [c1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_3, 287496, grid=grid(287496), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [p1], Original ATen: [aten.max_pool3d_with_indices]
        buf2 = torch.ops.aten.max_pool3d_with_indices.default(buf1, [2, 2, 2], [2, 2, 2], [1, 1, 1])
        buf3 = buf2[0]
        buf4 = buf2[1]
        del buf2
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf5, 39304, grid=grid(39304), stream=stream0)
        # Topologically Sorted Source Nodes: [c2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 2, 9, 9, 9), (1458, 729, 81, 9, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [c2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf7, primals_5, 5832, grid=grid(5832), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [p2], Original ATen: [aten.max_pool3d_with_indices]
        buf8 = torch.ops.aten.max_pool3d_with_indices.default(buf7, [2, 2, 2], [2, 2, 2], [1, 1, 1])
        buf9 = buf8[0]
        buf10 = buf8[1]
        del buf8
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [relu_1], Original ATen: [aten.relu]
        triton_poi_fused_relu_3.run(buf11, 1000, grid=grid(1000), stream=stream0)
        # Topologically Sorted Source Nodes: [c3], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [c3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf13, primals_7, 432, grid=grid(432), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [p3], Original ATen: [aten.max_pool3d_with_indices]
        buf14 = torch.ops.aten.max_pool3d_with_indices.default(buf13, [2, 2, 2], [2, 2, 2])
        buf15 = buf14[0]
        buf16 = buf14[1]
        del buf14
        buf17 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf15, (4, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 8), (1, 4), 0), out=buf17)
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [l1], Original ATen: [aten.relu]
        triton_poi_fused_relu_5.run(buf18, primals_9, 32, grid=grid(32), stream=stream0)
        del primals_9
        buf20 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf18, reinterpret_tensor(primals_10, (8, 1), (1, 8), 0), alpha=1, beta=1, out=buf20)
        del primals_11
    return (buf20, primals_1, primals_2, primals_4, primals_6, buf1, buf4, buf5, buf7, buf10, buf11, buf13, buf16, reinterpret_tensor(buf15, (4, 4), (4, 1), 0), buf18, primals_10, primals_8, )


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, 1, 64, 64, 64), (262144, 262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, 1, 2, 2, 2), (8, 8, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = 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])
    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


class CNN3dModel(torch.nn.ModuleDict):

    def __init__(self, D_in=1, D_out=1):
        super(CNN3dModel, self).__init__()
        self.conv3d = torch.nn.Conv3d(D_in, D_in * 2, kernel_size=2, stride
            =2, padding=1)
        self.conv3d2 = torch.nn.Conv3d(D_in * 2, D_in * 2, kernel_size=2,
            stride=2, padding=1)
        self.conv3d3 = torch.nn.Conv3d(D_in * 2, D_in * 4, kernel_size=2,
            stride=2, padding=1)
        self.pool = torch.nn.MaxPool3d(kernel_size=2, padding=1)
        self.pool2 = torch.nn.MaxPool3d(kernel_size=2)
        self.relu = torch.nn.ReLU()
        self.linear = torch.nn.Linear(D_in * 4, D_in * 8)
        self.linear2 = torch.nn.Linear(D_in * 8, D_out)

    def forward(self, x):
        x = x.float()
        c1 = self.conv3d(x)
        p1 = self.pool(c1)
        c2 = self.conv3d2(self.relu(p1))
        p2 = self.pool(c2)
        c3 = self.conv3d3(self.relu(p2))
        p3 = self.pool2(c3)
        v1 = p3.view(p3.size(0), -1)
        l1 = self.relu(self.linear(v1))
        l2 = self.linear2(l1)
        return l2


def get_inputs():
    return [torch.rand([4, 1, 64, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 287496
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 35937 % 2
    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_relu_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 39304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 729 % 2
    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_relu_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 27 % 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_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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 + x2, tmp4, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64, 64), (262144, 262144, 4096,
        64, 1))
    assert_size_stride(primals_2, (2, 1, 2, 2, 2), (8, 8, 4, 2, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (2, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (4, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (8, 4), (4, 1))
    assert_size_stride(primals_9, (8,), (1,))
    assert_size_stride(primals_10, (1, 8), (8, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 33, 33, 33), (71874, 35937, 1089, 
            33, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(287496)](buf1, primals_3, 
            287496, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_3
        buf2 = torch.ops.aten.max_pool3d_with_indices.default(buf1, [2, 2, 
            2], [2, 2, 2], [1, 1, 1])
        buf3 = buf2[0]
        buf4 = buf2[1]
        del buf2
        buf5 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(39304)](buf5, 39304, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 2, 9, 9, 9), (1458, 729, 81, 9, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_2[grid(5832)](buf7, primals_5, 5832,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf8 = torch.ops.aten.max_pool3d_with_indices.default(buf7, [2, 2, 
            2], [2, 2, 2], [1, 1, 1])
        buf9 = buf8[0]
        buf10 = buf8[1]
        del buf8
        buf11 = buf9
        del buf9
        triton_poi_fused_relu_3[grid(1000)](buf11, 1000, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2, 
            2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_4[grid(432)](buf13, primals_7, 432,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf14 = torch.ops.aten.max_pool3d_with_indices.default(buf13, [2, 2,
            2], [2, 2, 2])
        buf15 = buf14[0]
        buf16 = buf14[1]
        del buf14
        buf17 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 8), (1, 4), 0), out=buf17)
        buf18 = buf17
        del buf17
        triton_poi_fused_relu_5[grid(32)](buf18, primals_9, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_9
        buf20 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf18, reinterpret_tensor(
            primals_10, (8, 1), (1, 8), 0), alpha=1, beta=1, out=buf20)
        del primals_11
    return (buf20, primals_1, primals_2, primals_4, primals_6, buf1, buf4,
        buf5, buf7, buf10, buf11, buf13, buf16, reinterpret_tensor(buf15, (
        4, 4), (4, 1), 0), buf18, primals_10, primals_8)


class CNN3dModelNew(torch.nn.ModuleDict):

    def __init__(self, D_in=1, D_out=1):
        super(CNN3dModelNew, self).__init__()
        self.conv3d = torch.nn.Conv3d(D_in, D_in * 2, kernel_size=2, stride
            =2, padding=1)
        self.conv3d2 = torch.nn.Conv3d(D_in * 2, D_in * 2, kernel_size=2,
            stride=2, padding=1)
        self.conv3d3 = torch.nn.Conv3d(D_in * 2, D_in * 4, kernel_size=2,
            stride=2, padding=1)
        self.pool = torch.nn.MaxPool3d(kernel_size=2, padding=1)
        self.pool2 = torch.nn.MaxPool3d(kernel_size=2)
        self.relu = torch.nn.ReLU()
        self.linear = torch.nn.Linear(D_in * 4, D_in * 8)
        self.linear2 = torch.nn.Linear(D_in * 8, D_out)

    def forward(self, input_0):
        primals_2 = self.conv3d.weight
        primals_3 = self.conv3d.bias
        primals_4 = self.conv3d2.weight
        primals_5 = self.conv3d2.bias
        primals_6 = self.conv3d3.weight
        primals_7 = self.conv3d3.bias
        primals_8 = self.linear.weight
        primals_9 = self.linear.bias
        primals_10 = self.linear2.weight
        primals_11 = self.linear2.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,
            primals_10, primals_11])
        return output[0]
