# 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_7/inductor_cache/bb/cbbrf3z5zs3mby26g37h3hqek6jb44pbydizc4mmbq52gwghiytt.py
# Topologically Sorted Source Nodes: [x_compress], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_compress => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[2097152], 
    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_cat_0', 'mutated_arg_names': [], '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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 262144) % 2
    x0 = xindex % 262144
    x2 = (xindex // 524288)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (524288*x2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (262144 + x0 + (524288*x2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 2, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr0 + (x0 + (524288*x2)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr0 + (262144 + x0 + (524288*x2)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = 2.0
    tmp17 = tmp15 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp9, tmp19)
    tl.store(out_ptr0 + (x3), tmp20, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vp/cvpig5tiioo5gjhy5u4vxwsuxiinwoihxai3joaon64zjwyec7iz.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   x_1 => repeat
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_3, [4]), kwargs = {})
triton_poi_fused_repeat_1 = async_compile.triton('triton_poi_fused_repeat_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bx/cbxfs6slagmrrwwqr4aswhlzhjxr5ysvyvaom4sgva4am55s3lhy.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   x_1 => var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3, 4]), kwargs = {correction: 0, keepdim: True})
triton_red_fused__native_batch_norm_legit_2 = async_compile.triton('triton_red_fused__native_batch_norm_legit_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.reduction(
    size_hints=[128, 8192],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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_red_fused__native_batch_norm_legit_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 3, '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}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_2(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 128
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (8192*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = triton_helpers.welford_reduce(
            tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0
        )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(
        tmp2_mean, tmp2_m2, tmp2_weight, 1
    )
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp3, xmask)
    tl.store(out_ptr2 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/er/cerl5at3krblbvplgpf7b7cx6mcfh6oicu4azokdq7wwdkmyyk3u.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   x_1 => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3, 4]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_per_fused__native_batch_norm_legit_3 = async_compile.triton('triton_per_fused__native_batch_norm_legit_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.persistent_reduction(
    size_hints=[4, 32],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (32*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (32*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + (32*x0)), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tmp16 = 262144.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp20, xmask)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kx/ckx42tpuyndb5gvhpvafnh7qyd5sgmhlww4fuulgch45zdvhwh3f.py
# Topologically Sorted Source Nodes: [scale], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   scale => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
triton_poi_fused_sigmoid_4 = async_compile.triton('triton_poi_fused_sigmoid_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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)
    x2 = xindex
    x1 = (xindex // 262144)
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/k4/ck4iij64xszzsv7pymvjkxrqh4nbcwe6zrz72hxmjnd7m73teljj.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %view_3), kwargs = {})
triton_poi_fused_mul_5 = async_compile.triton('triton_poi_fused_mul_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 262144
    x2 = (xindex // 524288)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x0 + (262144*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 64, 64, 64), (524288, 262144, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 2, 3, 3, 3), (54, 27, 9, 3, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_compress], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, buf0, 2097152, grid=grid(2097152), stream=stream0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 64, 64, 64), (262144, 262144, 4096, 64, 1))
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_1.run(primals_3, buf2, 4, grid=grid(4), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_1.run(primals_4, buf3, 4, grid=grid(4), stream=stream0)
        del primals_4
        buf4 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 128, 1), torch.float32)
        buf5 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 128, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_2.run(buf1, buf4, buf5, buf6, 128, 8192, grid=grid(128), stream=stream0)
        buf7 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 1, 1, 1), torch.float32)
        buf8 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.float32)
        buf10 = reinterpret_tensor(buf8, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_3.run(buf10, buf4, buf5, buf6, buf7, 4, 32, grid=grid(4), stream=stream0)
        del buf4
        del buf5
        del buf6
        buf11 = empty_strided_cuda((4, 1, 64, 64, 64), (262144, 1048576, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scale], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_4.run(buf1, buf7, buf10, buf2, buf3, buf11, 1048576, grid=grid(1048576), stream=stream0)
        buf12 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        triton_poi_fused_mul_5.run(primals_1, buf11, buf12, 2097152, grid=grid(2097152), stream=stream0)
        del buf11
    return (buf12, primals_1, primals_2, buf0, buf1, buf2, buf3, buf7, 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, 2, 64, 64, 64), (524288, 262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 2, 3, 3, 3), (54, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 BasicConv3D(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
        super(BasicConv3D, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv3d(in_planes, out_planes, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.bn = nn.InstanceNorm3d(out_planes, eps=1e-05, momentum=0.01,
            affine=True)
        self.relu = nn.LeakyReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class ChannelPool3D(nn.Module):

    def forward(self, x):
        return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
            .unsqueeze(1)), dim=1)


class SpatialGate3D(nn.Module):

    def __init__(self):
        super(SpatialGate3D, self).__init__()
        kernel_size = 3
        self.compress = ChannelPool3D()
        self.spatial = BasicConv3D(2, 1, kernel_size, stride=1, padding=(
            kernel_size - 1) // 2, relu=False)

    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = torch.sigmoid_(x_out)
        return x * scale


def get_inputs():
    return [torch.rand([4, 2, 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
from torch._inductor.runtime.triton_helpers import libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, 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 // 262144 % 2
    x0 = xindex % 262144
    x2 = xindex // 524288
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 524288 * x2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (262144 + x0 + 524288 * x2), tmp4,
        eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp13 = tl.load(in_ptr0 + (x0 + 524288 * x2), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp14 = tl.load(in_ptr0 + (262144 + x0 + 524288 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = 2.0
    tmp17 = tmp15 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp9, tmp19)
    tl.store(out_ptr0 + x3, tmp20, None)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_2(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 8192 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)
    tl.store(out_ptr2 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_3(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 32 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 32 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + 32 * x0), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp12[:, None]
    tmp16 = 262144.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 262144
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tl.store(out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 262144
    x2 = xindex // 524288
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x0 + 262144 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 64, 64, 64), (524288, 262144, 4096,
        64, 1))
    assert_size_stride(primals_2, (1, 2, 3, 3, 3), (54, 27, 9, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 4096,
            64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(2097152)](primals_1, buf0, 2097152,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 64, 64, 64), (262144, 262144, 4096,
            64, 1))
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_repeat_1[grid(4)](primals_3, buf2, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_repeat_1[grid(4)](primals_4, buf3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        buf5 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        triton_red_fused__native_batch_norm_legit_2[grid(128)](buf1, buf4,
            buf5, buf6, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf7 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 1, 1, 1), torch.
            float32)
        buf8 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        buf10 = reinterpret_tensor(buf8, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0)
        del buf8
        triton_per_fused__native_batch_norm_legit_3[grid(4)](buf10, buf4,
            buf5, buf6, buf7, 4, 32, XBLOCK=1, num_warps=2, num_stages=1)
        del buf4
        del buf5
        del buf6
        buf11 = empty_strided_cuda((4, 1, 64, 64, 64), (262144, 1048576, 
            4096, 64, 1), torch.float32)
        triton_poi_fused_sigmoid_4[grid(1048576)](buf1, buf7, buf10, buf2,
            buf3, buf11, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 
            4096, 64, 1), torch.float32)
        triton_poi_fused_mul_5[grid(2097152)](primals_1, buf11, buf12, 
            2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf11
    return buf12, primals_1, primals_2, buf0, buf1, buf2, buf3, buf7, buf10


class BasicConv3D(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
        super(BasicConv3D, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv3d(in_planes, out_planes, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.bn = nn.InstanceNorm3d(out_planes, eps=1e-05, momentum=0.01,
            affine=True)
        self.relu = nn.LeakyReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class ChannelPool3D(nn.Module):

    def forward(self, x):
        return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
            .unsqueeze(1)), dim=1)


class SpatialGate3DNew(nn.Module):

    def __init__(self):
        super(SpatialGate3DNew, self).__init__()
        kernel_size = 3
        self.compress = ChannelPool3D()
        self.spatial = BasicConv3D(2, 1, kernel_size, stride=1, padding=(
            kernel_size - 1) // 2, relu=False)

    def forward(self, input_0):
        primals_2 = self.spatial.conv.weight
        primals_3 = self.spatial.bn.weight
        primals_4 = self.spatial.bn.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
