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

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


# kernel path: runs/run_shard_7/inductor_cache/7o/c7o5ttyweofz42kjophypjktbctv6rlvcw3xdxc4lcl7kzkau6rb.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, 4096], 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_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 = 4096
    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 % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (262144*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/cg/ccgjbjra22jzcgrej4cregu4qnsuv4valsbpzazlnyx6d4zadmny.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=[4096, 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_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 = 4096
    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 % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kx/ckx2rnidyqb5tiful7teg7va3kagir3uo3nzo2l5v3pauvflocqb.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4194304], 
    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_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_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4194304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    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_7/inductor_cache/x5/cx5qujuv6gwtcmd2b4lxiksdatv6sbycpa65uwp6lwoeccocixrf.py
# Topologically Sorted Source Nodes: [conv2d_1, y1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   y1 => relu
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_relu_3', '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_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    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_7/inductor_cache/rs/crsroft2dnjstjmujk22hdbv2nm4xqzsa7oadwnpsfyyfwwaxs7h.py
# Topologically Sorted Source Nodes: [y3, y3_1, relu__2], Original ATen: [aten.convolution, aten.add, aten.relu]
# Source node to ATen node mapping:
#   relu__2 => relu_2
#   y3 => convolution_3
#   y3_1 => add
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_3, %convolution), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
triton_poi_fused_add_convolution_relu_4 = async_compile.triton('triton_poi_fused_add_convolution_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384, 256], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 256
    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 % 4096
    y1 = (yindex // 4096)
    tmp0 = tl.load(in_ptr0 + (x2 + (256*y3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (256*y3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1, 1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (y0 + (4096*x2) + (1048576*y1)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dk/cdkiztm7wyidqz3pgzmmqv4fvqrvyeaiurispq5dgyjngbmv4iwr.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.threshold_backward]
# Source node to ATen node mapping:
# Graph fragment:
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_threshold_backward_5 = async_compile.triton('triton_poi_fused_threshold_backward_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=[1024, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_threshold_backward_5', '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_threshold_backward_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 4096
    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 = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (y0 + (256*x2) + (1048576*y1)), tmp2, 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 = args
    args.clear()
    assert_size_stride(primals_1, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_2, (256, ), (1, ))
    assert_size_stride(primals_3, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_9, (256, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_3, buf0, 256, 4096, grid=grid(256, 4096), stream=stream0)
        del primals_3
        buf1 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_6, buf1, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf3, primals_2, 4194304, grid=grid(4194304), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, y1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf5, primals_5, 1048576, grid=grid(1048576), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, y2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf7, primals_7, 1048576, grid=grid(1048576), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [y3], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y3, y3_1, relu__2], Original ATen: [aten.convolution, aten.add, aten.relu]
        triton_poi_fused_add_convolution_relu_4.run(buf8, primals_9, buf3, buf9, 16384, 256, grid=grid(16384, 256), stream=stream0)
        del buf8
        del primals_9
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.bool)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.threshold_backward]
        triton_poi_fused_threshold_backward_5.run(buf9, buf10, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
    return (buf9, primals_1, buf0, primals_4, buf1, primals_8, buf3, buf5, buf7, buf10, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 64, 64, 64), (262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((256, ), (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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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 % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 262144 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_1(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 % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_2(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 % 256
    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_convolution_relu_3(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_add_convolution_relu_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 256
    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 % 4096
    y1 = yindex // 4096
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1, 1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (y0 + 4096 * x2 + 1048576 * y1), tmp6, xmask)


@triton.jit
def triton_poi_fused_threshold_backward_5(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp2, None)


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, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_9, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(256, 4096)](primals_3, buf0, 256, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_6, buf1, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(4194304)](buf3, primals_2, 
            4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf5, primals_5,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf7, primals_7,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_convolution_relu_4[grid(16384, 256)](buf8,
            primals_9, buf3, buf9, 16384, 256, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf8
        del primals_9
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 
            256), torch.bool)
        triton_poi_fused_threshold_backward_5[grid(1024, 4096)](buf9, buf10,
            1024, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
    return (buf9, primals_1, buf0, primals_4, buf1, primals_8, buf3, buf5,
        buf7, buf10)


class Bottleneck_v2New(nn.Module):

    def __init__(self):
        super(Bottleneck_v2New, self).__init__()
        self.conv = nn.Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1),
            bias=True)
        self.conv1 = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1),
            bias=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1),
            padding=(1, 1), bias=True)
        self.conv3 = nn.Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1),
            bias=True)

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