# 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_4/inductor_cache/eg/cegzlxprjxbpbhkrnicdmhevqyswqdi2z6v56qachpguxj55ccdy.py
# Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv1d => convolution
#   x => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_convolution_elu_0 = async_compile.triton('triton_poi_fused_convolution_elu_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=[8192], 
    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_convolution_elu_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_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7680
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 60) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pg/cpgla52fcocbo35tk4vis5525rclwz56xza2htyhyblkyj6hgkru.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_1 => _low_memory_max_pool2d_with_offsets, getitem_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_1', '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr1 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wx/cwxr2kzymqkougldifsjwhbdlo25caehgpqd3ir3q5gh4at4ocup.py
# Topologically Sorted Source Nodes: [conv1d_1, x_2], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv1d_1 => convolution_1
#   x_2 => expm1_1, gt_1, mul_3, mul_5, where_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%squeeze, %primals_4, %primals_5, [2], [0], [1], False, [0], 1), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_convolution_elu_2 = async_compile.triton('triton_poi_fused_convolution_elu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_convolution_elu_2', '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_elu_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 13) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jb/cjb2gcdxqycz66ruwyhgmanlinlf2lfqqar726qp7byuh4nktfzx.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_3 => _low_memory_max_pool2d_with_offsets_1, getitem_3
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze_1, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_3', '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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (13*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (13*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4j/c4jyphllh2uhqcoyenvjqp4pppj57n6iciwlchn7pikfx75moz62.py
# Topologically Sorted Source Nodes: [conv1d_2, x_4], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   conv1d_2 => convolution_2
#   x_4 => expm1_2, gt_2, mul_6, mul_8, where_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%squeeze_2, %primals_6, %primals_7, [2], [0], [1], False, [0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_6 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 1.0), kwargs = {})
#   %expm1_2 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_6,), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_2, 1.0), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mul_6, %mul_8), kwargs = {})
triton_poi_fused_convolution_elu_4 = async_compile.triton('triton_poi_fused_convolution_elu_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=[128], 
    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_convolution_elu_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_elu_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3) % 8
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vl/cvlaap5z4k2nyic5ehmmjwcrkvp37p2buhd5cb75xjgjjysli7kc.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_5 => _low_memory_max_pool2d_with_offsets_2, getitem_5
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze_2, [1, 2], [1, 1], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_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_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (3*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + (3*x1)), xmask)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hz/chzd2a4r6x5imgu36ldesaomi5ohb4verc75eiv2hgs63267hn53.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_7 => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_6 = async_compile.triton('triton_poi_fused__softmax_6', '''
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=[16], 
    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__softmax_6', '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__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yi/cyimmhe2apz2qc7an46ymzpnjxqdhkuh2jxg2fpmps2ztwubttgw.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_7 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_7 = async_compile.triton('triton_poi_fused__softmax_7', '''
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=[16], 
    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__softmax_7', '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__softmax_7(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (32, 1, 5), (5, 5, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_4, (16, 32, 5), (160, 5, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (8, 16, 2), (32, 2, 1))
    assert_size_stride(primals_7, (8, ), (1, ))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 60), (1920, 60, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 32, 60), (1920, 60, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.elu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_elu_0.run(buf1, primals_2, buf2, 7680, grid=grid(7680), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((4, 32, 1, 30), (960, 30, 30, 1), torch.int8)
        buf4 = empty_strided_cuda((4, 32, 1, 30), (960, 30, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf2, buf3, buf4, 3840, grid=grid(3840), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (4, 32, 30), (960, 30, 1), 0), primals_4, stride=(2,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 13), (208, 13, 1))
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 16, 13), (208, 13, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d_1, x_2], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_2.run(buf6, primals_5, buf7, 832, grid=grid(832), stream=stream0)
        del primals_5
        buf8 = empty_strided_cuda((4, 16, 1, 6), (96, 6, 6, 1), torch.int8)
        buf9 = empty_strided_cuda((4, 16, 1, 6), (96, 6, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf7, buf8, buf9, 384, grid=grid(384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 16, 6), (96, 6, 1), 0), primals_6, stride=(2,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf10, (4, 8, 3), (24, 3, 1))
        buf11 = buf10; del buf10  # reuse
        buf12 = empty_strided_cuda((4, 8, 3), (24, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d_2, x_4], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_4.run(buf11, primals_7, buf12, 96, grid=grid(96), stream=stream0)
        del primals_7
        buf13 = empty_strided_cuda((4, 8, 1, 2), (16, 2, 2, 1), torch.int8)
        buf14 = empty_strided_cuda((4, 8, 1, 2), (16, 2, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf12, buf13, buf14, 64, grid=grid(64), stream=stream0)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf14, (4, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf15)
        del primals_9
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_6.run(buf15, buf16, 16, grid=grid(16), stream=stream0)
        buf17 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_7.run(buf16, buf17, 16, grid=grid(16), stream=stream0)
        del buf16
    return (buf17, primals_1, primals_3, primals_4, primals_6, buf1, reinterpret_tensor(buf2, (4, 32, 1, 60), (1920, 60, 60, 1), 0), buf3, reinterpret_tensor(buf4, (4, 32, 30), (960, 30, 1), 0), buf6, reinterpret_tensor(buf7, (4, 16, 1, 13), (208, 13, 13, 1), 0), buf8, reinterpret_tensor(buf9, (4, 16, 6), (96, 6, 1), 0), buf11, reinterpret_tensor(buf12, (4, 8, 1, 3), (24, 3, 3, 1), 0), buf13, reinterpret_tensor(buf14, (4, 16), (16, 1), 0), buf17, 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((32, 1, 5), (5, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64), (64, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 32, 5), (160, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, 16, 2), (32, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from torch.nn import functional as F
import torch.utils.data


class DomainCNN(torch.nn.Module):

    def __init__(self, domains):
        super(DomainCNN, self).__init__()
        self.conv1 = torch.nn.Conv1d(1, 32, kernel_size=5)
        self.pool1 = torch.nn.MaxPool1d(kernel_size=2)
        self.conv2 = torch.nn.Conv1d(32, 16, kernel_size=5, stride=2)
        self.pool2 = torch.nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv3 = torch.nn.Conv1d(16, 8, kernel_size=2, stride=2)
        self.pool3 = torch.nn.MaxPool1d(kernel_size=2, stride=1)
        self.fc1 = torch.nn.Linear(8 * 2, domains)

    def forward(self, x):
        x = F.elu(self.conv1(x))
        x = self.pool1(x)
        x = F.elu(self.conv2(x))
        x = self.pool2(x)
        x = F.elu(self.conv3(x))
        x = self.pool3(x)
        x = x.view(-1, 8 * 2)
        m = torch.nn.Softmax(1)
        x = m(self.fc1(x))
        return x


def get_inputs():
    return [torch.rand([4, 1, 64])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
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_elu_0(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 7680
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 60 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 3840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_2(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 13 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 13 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 13 * x1), xmask, eviction_policy
        ='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_4(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 3 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 3 * x1), xmask)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (32, 1, 5), (5, 5, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_4, (16, 32, 5), (160, 5, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (8, 16, 2), (32, 2, 1))
    assert_size_stride(primals_7, (8,), (1,))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 60), (1920, 60, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 32, 60), (1920, 60, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_elu_0[grid(7680)](buf1, primals_2,
            buf2, 7680, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 32, 1, 30), (960, 30, 30, 1), torch.int8)
        buf4 = empty_strided_cuda((4, 32, 1, 30), (960, 30, 30, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(3840)](buf2, buf3,
            buf4, 3840, XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (4, 32, 
            30), (960, 30, 1), 0), primals_4, stride=(2,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf5, (4, 16, 13), (208, 13, 1))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 16, 13), (208, 13, 1), torch.float32)
        triton_poi_fused_convolution_elu_2[grid(832)](buf6, primals_5, buf7,
            832, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf8 = empty_strided_cuda((4, 16, 1, 6), (96, 6, 6, 1), torch.int8)
        buf9 = empty_strided_cuda((4, 16, 1, 6), (96, 6, 6, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(384)](buf7, buf8,
            buf9, 384, XBLOCK=128, num_warps=4, num_stages=1)
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 16,
            6), (96, 6, 1), 0), primals_6, stride=(2,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf10, (4, 8, 3), (24, 3, 1))
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 8, 3), (24, 3, 1), torch.float32)
        triton_poi_fused_convolution_elu_4[grid(96)](buf11, primals_7,
            buf12, 96, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf13 = empty_strided_cuda((4, 8, 1, 2), (16, 2, 2, 1), torch.int8)
        buf14 = empty_strided_cuda((4, 8, 1, 2), (16, 2, 2, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_5[grid(64)](buf12, buf13,
            buf14, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf14, (4, 16),
            (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf15)
        del primals_9
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_6[grid(16)](buf15, buf16, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        triton_poi_fused__softmax_7[grid(16)](buf16, buf17, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf16
    return (buf17, primals_1, primals_3, primals_4, primals_6, buf1,
        reinterpret_tensor(buf2, (4, 32, 1, 60), (1920, 60, 60, 1), 0),
        buf3, reinterpret_tensor(buf4, (4, 32, 30), (960, 30, 1), 0), buf6,
        reinterpret_tensor(buf7, (4, 16, 1, 13), (208, 13, 13, 1), 0), buf8,
        reinterpret_tensor(buf9, (4, 16, 6), (96, 6, 1), 0), buf11,
        reinterpret_tensor(buf12, (4, 8, 1, 3), (24, 3, 3, 1), 0), buf13,
        reinterpret_tensor(buf14, (4, 16), (16, 1), 0), buf17, primals_8)


class DomainCNNNew(torch.nn.Module):

    def __init__(self, domains):
        super(DomainCNNNew, self).__init__()
        self.conv1 = torch.nn.Conv1d(1, 32, kernel_size=5)
        self.pool1 = torch.nn.MaxPool1d(kernel_size=2)
        self.conv2 = torch.nn.Conv1d(32, 16, kernel_size=5, stride=2)
        self.pool2 = torch.nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv3 = torch.nn.Conv1d(16, 8, kernel_size=2, stride=2)
        self.pool3 = torch.nn.MaxPool1d(kernel_size=2, stride=1)
        self.fc1 = torch.nn.Linear(8 * 2, domains)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.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])
        return output[0]
