# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/3h/c3hb5fafbcmv2m4qbvvh5vgmr2pggdyl3lwp65qrsk4qzpkczpxs.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256, 64], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (36*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (4*x2) + (144*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/sy/csy24qokcw4rc3y66qehxpsywieeq4zr5dt3icxfx73i5mpw6bhu.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[16, 65536], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 65536
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (65536*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (4*x2) + (262144*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ez/cez3evzykqew3uxhxdg4qlmhff7rrk235fvmrq5piuws2hm62b32.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_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, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_2', '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_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8192
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (1024*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/dd/cddy2xs2uderg6rhu3vap3su355lmjpkrmadmh5gnbcfg2frfd5z.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_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=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ih/cihu7ohoiwwrblocurozhw6ihpzbq4oc43mseo4n6wd7ronp74tw.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_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=[32768, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', '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_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 32768
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ul/culqydgza5j25lmiqzax4of6c3sod2nsny5frud5eybn7rdrgyde.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [3, 3], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_5 = async_compile.triton('triton_poi_fused_convolution_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=[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_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1806336
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kn/cknft3vd6ol54xgaluqqw4eonwqpvlvph7jfr25wyzpd5njywn5k.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_2 => 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], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_6 = async_compile.triton('triton_poi_fused_convolution_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_6', '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_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 860672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/jy/cjyi3hbyac22pmabptnsl4ir6vbjpqlgzkjmsxclwopxmu43fvgl.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
#   x_3 => _low_memory_max_pool2d_with_offsets, getitem_1
#   x_4 => relu_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 = (%convolution_1, [2, 2], [2, 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 = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_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=[262144], 
    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_relu_7', '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_max_pool2d_with_indices_relu_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 204800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128) % 20
    x2 = (xindex // 2560) % 20
    x3 = (xindex // 51200)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x1) + (10496*x2) + (215168*x3)), None)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + (256*x1) + (10496*x2) + (215168*x3)), None)
    tmp7 = tl.load(in_ptr0 + (5248 + x0 + (256*x1) + (10496*x2) + (215168*x3)), None)
    tmp12 = tl.load(in_ptr0 + (5376 + x0 + (256*x1) + (10496*x2) + (215168*x3)), None)
    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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x4), tmp15, None)
    tl.store(out_ptr1 + (x4), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/i2/ci2d3nn6zxqd6ds5dsriktd27mqqzfvyf66lwupe62lipfsv7qzb.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_5 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_8 = async_compile.triton('triton_poi_fused_convolution_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_8', '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_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cj/ccje35ylxbasbhxmcbiemgf7dppuef2uwwjhu6hhwsyowjjrzq7j.py
# Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
#   x_6 => _low_memory_max_pool2d_with_offsets_1, getitem_3
#   x_7 => relu_2
# 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 = (%convolution_2, [2, 2], [2, 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 = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_9 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_9', '''
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: '*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_relu_9', '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_max_pool2d_with_indices_relu_9(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 41472
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = (xindex // 128) % 9
    x2 = (xindex // 1152)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x1) + (4608*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + (256*x1) + (4608*x2)), xmask)
    tmp7 = tl.load(in_ptr0 + (2304 + x0 + (256*x1) + (4608*x2)), xmask)
    tmp12 = tl.load(in_ptr0 + (2432 + x0 + (256*x1) + (4608*x2)), 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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x3), tmp15, xmask)
    tl.store(out_ptr1 + (x3), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/eh/ceh4ywgmd6g34fhmofgtq2f3unb4xa4ehcr4jj6lrkfkxka474ff.py
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_8 => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_convolution_10', '''
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: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_10', '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_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tz/ctzqqdejxvr2tcbd2fm3vxc57w2nohqs7ymko67yfjn4tuaa7gfv.py
# Topologically Sorted Source Nodes: [x_9, x_10], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_10 => relu_3
#   x_9 => _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 = (%convolution_3, [2, 2], [2, 2], [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 = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_4,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*i1', 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_relu_threshold_backward_11', '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_max_pool2d_with_indices_relu_threshold_backward_11(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x1 = (xindex // 256) % 3
    x2 = (xindex // 768) % 3
    x3 = (xindex // 2304)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (512*x1) + (3584*x2) + (12544*x3)), xmask)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + (512*x1) + (3584*x2) + (12544*x3)), xmask)
    tmp7 = tl.load(in_ptr0 + (1792 + x0 + (512*x1) + (3584*x2) + (12544*x3)), xmask)
    tmp12 = tl.load(in_ptr0 + (2048 + x0 + (512*x1) + (3584*x2) + (12544*x3)), 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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x4), tmp15, xmask)
    tl.store(out_ptr1 + (x4), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fi/cfiknf75zoxbghefcstkrwsedoti4jetbbq7q7d5zwvbwxlwf3di.py
# Topologically Sorted Source Nodes: [x_9, x_10, x_11], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.mean]
# Source node to ATen node mapping:
#   x_10 => relu_3
#   x_11 => mean
#   x_9 => _low_memory_max_pool2d_with_offsets_2
# 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 = (%convolution_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_4,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%relu_3, [-1, -2], True), kwargs = {})
triton_per_fused_max_pool2d_with_indices_mean_relu_12 = async_compile.triton('triton_per_fused_max_pool2d_with_indices_mean_relu_12', '''
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=[1024, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_pool2d_with_indices_mean_relu_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, '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_max_pool2d_with_indices_mean_relu_12(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    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 = rindex < rnumel
    r2 = rindex % 3
    r3 = (rindex // 3)
    x0 = xindex % 256
    x1 = (xindex // 256)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (512*r2) + (3584*r3) + (12544*x1)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + (512*r2) + (3584*r3) + (12544*x1)), rmask & xmask, other=0.0)
    tmp3 = tl.load(in_ptr0 + (1792 + x0 + (512*r2) + (3584*r3) + (12544*x1)), rmask & xmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (2048 + x0 + (512*r2) + (3584*r3) + (12544*x1)), rmask & xmask, other=0.0)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = 9.0
    tmp14 = tmp12 / tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x4), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6d/c6djolesr5764ayw7zadg5ffepmoyeg6hchcrqm25r35xqpvlaiy.py
# Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_14 => relu_4
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_11), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_13 = async_compile.triton('triton_poi_fused_relu_13', '''
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=[2048], 
    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_relu_13', '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_relu_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 6, 6), (144, 36, 6, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 256, 256), (262144, 65536, 256, 1))
    assert_size_stride(primals_4, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_5, (128, ), (1, ))
    assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (256, ), (1, ))
    assert_size_stride(primals_10, (512, 256), (256, 1))
    assert_size_stride(primals_11, (512, ), (1, ))
    assert_size_stride(primals_12, (1, 512), (512, 1))
    assert_size_stride(primals_13, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4, 6, 6), (144, 1, 24, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 256, 36, grid=grid(256, 36), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 256, 256), (262144, 1, 1024, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 16, 65536, grid=grid(16, 65536), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_4, buf2, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_6, buf3, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_8, buf4, 32768, 9, grid=grid(32768, 9), stream=stream0)
        del primals_8
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf1, buf0, stride=(3, 3), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 64, 84, 84), (451584, 1, 5376, 64))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf6, primals_2, 1806336, grid=grid(1806336), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, buf2, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 128, 41, 41), (215168, 1, 5248, 128))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf8, primals_5, 860672, grid=grid(860672), stream=stream0)
        del primals_5
        buf9 = empty_strided_cuda((4, 128, 20, 20), (51200, 1, 2560, 128), torch.int8)
        buf10 = empty_strided_cuda((4, 128, 20, 20), (51200, 1, 2560, 128), torch.float32)
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_7.run(buf8, buf9, buf10, 204800, grid=grid(204800), stream=stream0)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 128, 18, 18), (41472, 1, 2304, 128))
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_8.run(buf12, primals_7, 165888, grid=grid(165888), stream=stream0)
        del primals_7
        buf13 = empty_strided_cuda((4, 128, 9, 9), (10368, 1, 1152, 128), torch.int8)
        buf14 = empty_strided_cuda((4, 128, 9, 9), (10368, 1, 1152, 128), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_9.run(buf12, buf13, buf14, 41472, grid=grid(41472), stream=stream0)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 256, 7, 7), (12544, 1, 1792, 256))
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf16, primals_9, 50176, grid=grid(50176), stream=stream0)
        del primals_9
        buf17 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256), torch.int8)
        buf24 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256), torch.bool)
        # Topologically Sorted Source Nodes: [x_9, x_10], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_11.run(buf16, buf17, buf24, 9216, grid=grid(9216), stream=stream0)
        buf18 = empty_strided_cuda((4, 256, 1, 1), (256, 1, 1024, 1024), torch.float32)
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [x_9, x_10, x_11], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.mean]
        triton_per_fused_max_pool2d_with_indices_mean_relu_12.run(buf19, buf16, 1024, 9, grid=grid(1024), stream=stream0)
        buf20 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf19, (4, 256), (256, 1), 0), reinterpret_tensor(primals_10, (256, 512), (1, 256), 0), out=buf20)
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.relu]
        triton_poi_fused_relu_13.run(buf21, primals_11, 2048, grid=grid(2048), stream=stream0)
        del primals_11
        buf23 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, buf21, reinterpret_tensor(primals_12, (512, 1), (1, 512), 0), alpha=1, beta=1, out=buf23)
        del primals_13
    return (buf23, buf0, buf1, buf2, buf3, buf4, buf6, buf8, buf9, buf10, buf12, buf13, buf14, buf16, buf17, reinterpret_tensor(buf19, (4, 256), (256, 1), 0), buf21, primals_12, primals_10, buf24, )


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((64, 4, 6, 6), (144, 36, 6, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 256, 256), (262144, 65536, 256, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((512, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((1, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
from torch import nn


class LongCNN(nn.Module):

    def __init__(self, num_channels, input_shape, name, conv_sizes=[64, 128,
        128, 256], lin_size=512):
        super(LongCNN, self).__init__()
        self.name = name
        self.relu = nn.ReLU(inplace=True)
        self.do1 = nn.Dropout(p=0.25)
        self.do2 = nn.Dropout(p=0.25)
        self.conv1 = nn.Conv2d(num_channels, out_channels=conv_sizes[0],
            kernel_size=6, stride=3)
        self.conv2 = nn.Conv2d(in_channels=conv_sizes[0], out_channels=
            conv_sizes[1], kernel_size=4, stride=2)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(in_channels=conv_sizes[1], out_channels=
            conv_sizes[2], kernel_size=3)
        self.conv4 = nn.Conv2d(in_channels=conv_sizes[2], out_channels=
            conv_sizes[3], kernel_size=3)
        self.pool2 = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(in_features=conv_sizes[-1], out_features=lin_size)
        self.fc2 = nn.Linear(in_features=lin_size, out_features=1)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.pool1(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.pool1(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.pool1(x)
        x = self.relu(x)
        x = self.pool2(x)
        x = torch.flatten(x, start_dim=1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

    @property
    def save_path(self):
        return self.name + '.chkpt'


def get_inputs():
    return [torch.rand([4, 4, 256, 256])]


def get_init_inputs():
    return [[], {'num_channels': 4, 'input_shape': 4, 'name': 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 import 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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 36 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 144 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 65536 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 262144 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1024 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 860672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_7(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128 % 20
    x2 = xindex // 2560 % 20
    x3 = xindex // 51200
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 10496 * x2 + 215168 * x3), None)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 10496 * x2 + 215168 *
        x3), None)
    tmp7 = tl.load(in_ptr0 + (5248 + x0 + 256 * x1 + 10496 * x2 + 215168 *
        x3), None)
    tmp12 = tl.load(in_ptr0 + (5376 + x0 + 256 * x1 + 10496 * x2 + 215168 *
        x3), None)
    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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + x4, tmp15, None)
    tl.store(out_ptr1 + x4, tmp18, None)


@triton.jit
def triton_poi_fused_convolution_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_9(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 41472
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = xindex // 128 % 9
    x2 = xindex // 1152
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 4608 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 4608 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (2304 + x0 + 256 * x1 + 4608 * x2), xmask)
    tmp12 = tl.load(in_ptr0 + (2432 + x0 + 256 * x1 + 4608 * x2), 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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + x3, tmp15, xmask)
    tl.store(out_ptr1 + x3, tmp18, xmask)


@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_11(in_ptr0
    , out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x1 = xindex // 256 % 3
    x2 = xindex // 768 % 3
    x3 = xindex // 2304
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 3584 * x2 + 12544 * x3), xmask)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 3584 * x2 + 12544 * x3),
        xmask)
    tmp7 = tl.load(in_ptr0 + (1792 + x0 + 512 * x1 + 3584 * x2 + 12544 * x3
        ), xmask)
    tmp12 = tl.load(in_ptr0 + (2048 + x0 + 512 * x1 + 3584 * x2 + 12544 *
        x3), 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)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + x4, tmp15, xmask)
    tl.store(out_ptr1 + x4, tmp20, xmask)


@triton.jit
def triton_per_fused_max_pool2d_with_indices_mean_relu_12(in_out_ptr0,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex % 3
    r3 = rindex // 3
    x0 = xindex % 256
    x1 = xindex // 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * r2 + 3584 * r3 + 12544 * x1), 
        rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * r2 + 3584 * r3 + 12544 * x1),
        rmask & xmask, other=0.0)
    tmp3 = tl.load(in_ptr0 + (1792 + x0 + 512 * r2 + 3584 * r3 + 12544 * x1
        ), rmask & xmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (2048 + x0 + 512 * r2 + 3584 * r3 + 12544 * x1
        ), rmask & xmask, other=0.0)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = 9.0
    tmp14 = tmp12 / tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_relu_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 6, 6), (144, 36, 6, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 256, 256), (262144, 65536, 256, 1))
    assert_size_stride(primals_4, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (256,), (1,))
    assert_size_stride(primals_10, (512, 256), (256, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (1, 512), (512, 1))
    assert_size_stride(primals_13, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4, 6, 6), (144, 1, 24, 4), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_0[grid(256, 36)](primals_1, buf0, 256, 36, XBLOCK=
            32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 256, 256), (262144, 1, 1024, 4),
            torch.float32)
        triton_poi_fused_1[grid(16, 65536)](primals_3, buf1, 16, 65536,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_4, buf2, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_3[grid(16384, 9)](primals_6, buf3, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_4[grid(32768, 9)](primals_8, buf4, 32768, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf5 = extern_kernels.convolution(buf1, buf0, stride=(3, 3),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 64, 84, 84), (451584, 1, 5376, 64))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_relu_5[grid(1806336)](buf6, primals_2,
            1806336, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf7 = extern_kernels.convolution(buf6, buf2, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 128, 41, 41), (215168, 1, 5248, 128))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_6[grid(860672)](buf8, primals_5, 
            860672, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf9 = empty_strided_cuda((4, 128, 20, 20), (51200, 1, 2560, 128),
            torch.int8)
        buf10 = empty_strided_cuda((4, 128, 20, 20), (51200, 1, 2560, 128),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_7[grid(204800)](buf8,
            buf9, buf10, 204800, XBLOCK=1024, num_warps=4, num_stages=1)
        buf11 = extern_kernels.convolution(buf10, buf3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 128, 18, 18), (41472, 1, 2304, 128))
        buf12 = buf11
        del buf11
        triton_poi_fused_convolution_8[grid(165888)](buf12, primals_7, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf13 = empty_strided_cuda((4, 128, 9, 9), (10368, 1, 1152, 128),
            torch.int8)
        buf14 = empty_strided_cuda((4, 128, 9, 9), (10368, 1, 1152, 128),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_9[grid(41472)](buf12,
            buf13, buf14, 41472, XBLOCK=512, num_warps=4, num_stages=1)
        buf15 = extern_kernels.convolution(buf14, buf4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 256, 7, 7), (12544, 1, 1792, 256))
        buf16 = buf15
        del buf15
        triton_poi_fused_convolution_10[grid(50176)](buf16, primals_9, 
            50176, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf17 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256),
            torch.int8)
        buf24 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_11[
            grid(9216)](buf16, buf17, buf24, 9216, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf18 = empty_strided_cuda((4, 256, 1, 1), (256, 1, 1024, 1024),
            torch.float32)
        buf19 = buf18
        del buf18
        triton_per_fused_max_pool2d_with_indices_mean_relu_12[grid(1024)](buf19
            , buf16, 1024, 9, XBLOCK=32, num_warps=4, num_stages=1)
        buf20 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf19, (4, 256), (256, 1), 0),
            reinterpret_tensor(primals_10, (256, 512), (1, 256), 0), out=buf20)
        buf21 = buf20
        del buf20
        triton_poi_fused_relu_13[grid(2048)](buf21, primals_11, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf23 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf21, reinterpret_tensor(
            primals_12, (512, 1), (1, 512), 0), alpha=1, beta=1, out=buf23)
        del primals_13
    return (buf23, buf0, buf1, buf2, buf3, buf4, buf6, buf8, buf9, buf10,
        buf12, buf13, buf14, buf16, buf17, reinterpret_tensor(buf19, (4, 
        256), (256, 1), 0), buf21, primals_12, primals_10, buf24)


class LongCNNNew(nn.Module):

    def __init__(self, num_channels, input_shape, name, conv_sizes=[64, 128,
        128, 256], lin_size=512):
        super(LongCNNNew, self).__init__()
        self.name = name
        self.relu = nn.ReLU(inplace=True)
        self.do1 = nn.Dropout(p=0.25)
        self.do2 = nn.Dropout(p=0.25)
        self.conv1 = nn.Conv2d(num_channels, out_channels=conv_sizes[0],
            kernel_size=6, stride=3)
        self.conv2 = nn.Conv2d(in_channels=conv_sizes[0], out_channels=
            conv_sizes[1], kernel_size=4, stride=2)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(in_channels=conv_sizes[1], out_channels=
            conv_sizes[2], kernel_size=3)
        self.conv4 = nn.Conv2d(in_channels=conv_sizes[2], out_channels=
            conv_sizes[3], kernel_size=3)
        self.pool2 = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(in_features=conv_sizes[-1], out_features=lin_size)
        self.fc2 = nn.Linear(in_features=lin_size, out_features=1)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')

    @property
    def save_path(self):
        return self.name + '.chkpt'

    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.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.fc1.weight
        primals_11 = self.fc1.bias
        primals_12 = self.fc2.weight
        primals_13 = self.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
