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

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


# kernel path: runs/run_shard_4/inductor_cache/p3/cp32zuxrp2cknaaat4l46gcxlkrjzggsmqqhfyznul7wqfb4ebec.py
# Topologically Sorted Source Nodes: [branch1x1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   branch1x1 => convolution
# Graph fragment:
#   %convolution : [num_users=4] = 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_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1024], 
    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_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pb/cpbadr4mlklsm2wsqfjoz2cymcfug6mdrrrgmdl7w4mjrll45aku.py
# Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   branch3x3_1 => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[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_convolution_1', '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 24
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/em/cemc3jqja2kwvgm7onb76we2dehvtipmlyp7igln4trye3m6kju4.py
# Topologically Sorted Source Nodes: [branch_pool], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   branch_pool => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%primals_3, [3, 3], [1, 1], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_2 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[256], 
    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_avg_pool2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-3) + x4), tmp23 & xmask, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-1) + x4), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + ((-1)*x0) + ((-1)*x1) + (x0*x1) + (((5) * ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5)))*((5) * ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5)))) + ((-1)*x0*((5) * ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5)))) + ((-1)*x1*((5) * ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5)))) + ((5) * ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5))) + ((5) * ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5)))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + (x4), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/x6/cx6rdu5zhcf7lsqauiipv2bughhyspmkntbyzpcsxcjmj4rhue2q.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_2, %convolution_5, %convolution_6], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5632
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 88
    x0 = xindex % 16
    x2 = (xindex // 1408)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (256*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 40, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-16) + x1)) + (384*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-16) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 64, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (16*((-40) + x1)) + (384*x2)), tmp18 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr4 + ((-40) + x1), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp18, tmp21, tmp22)
    tmp24 = tmp0 >= tmp16
    tmp25 = tl.full([1], 88, tl.int64)
    tmp26 = tmp0 < tmp25
    tmp27 = tl.load(in_ptr5 + (x0 + (16*((-64) + x1)) + (384*x2)), tmp24 & xmask, other=0.0)
    tmp28 = tl.load(in_ptr6 + ((-64) + x1), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp24, tmp29, tmp30)
    tmp32 = tl.where(tmp18, tmp23, tmp31)
    tmp33 = tl.where(tmp9, tmp14, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tl.store(out_ptr0 + (x3), tmp34, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (24, 16, 5, 5), (400, 25, 5, 1))
    assert_size_stride(primals_5, (24, ), (1, ))
    assert_size_stride(primals_6, (24, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (24, ), (1, ))
    assert_size_stride(primals_8, (24, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_9, (24, ), (1, ))
    assert_size_stride(primals_10, (24, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (24, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [branch1x1], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [branch1x1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 1024, grid=grid(1024), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [branch5x5_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 24, 4, 4), (384, 16, 4, 1))
        # Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf1, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 24, 4, 4), (384, 16, 4, 1))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf4, primals_7, 1536, grid=grid(1536), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [branch3x3_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 24, 4, 4), (384, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [branch_pool], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_2.run(primals_3, buf6, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [branch_pool_1], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 24, 4, 4), (384, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 88, 4, 4), (1408, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf1, buf2, primals_5, buf5, primals_9, buf7, primals_11, buf8, 5632, grid=grid(5632), stream=stream0)
        del buf2
        del buf5
        del buf7
        del primals_11
        del primals_5
        del primals_9
    return (buf8, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf4, buf6, )


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((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((24, 16, 5, 5), (400, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((24, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((24, 24, 3, 3), (216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((24, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn.functional as F


class InceptionA(torch.nn.Module):

    def __init__(self, in_channels):
        super(InceptionA, self).__init__()
        self.branch1x1 = torch.nn.Conv2d(in_channels, 16, kernel_size=(1, 1))
        self.branch_pool = torch.nn.Conv2d(in_channels, 24, kernel_size=(1, 1))
        self.branch5x5 = torch.nn.Conv2d(16, 24, kernel_size=(5, 5), padding=2)
        self.branch3x3_2 = torch.nn.Conv2d(16, 24, kernel_size=(3, 3),
            padding=1)
        self.branch3x3_3 = torch.nn.Conv2d(24, 24, kernel_size=(3, 3),
            padding=1)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)
        branch5x5 = self.branch1x1(x)
        branch5x5 = self.branch5x5(branch5x5)
        branch3x3 = self.branch1x1(x)
        branch3x3 = self.branch3x3_2(branch3x3)
        branch3x3 = self.branch3x3_3(branch3x3)
        branch_pool = F.avg_pool2d(x, kernel_size=(3, 3), stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)
        outputs = [branch1x1, branch5x5, branch3x3, branch_pool]
        return torch.cat(outputs, dim=1)


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


def get_init_inputs():
    return [[], {'in_channels': 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
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 24
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + -1 * x0 + -1 * x1 + x0 * x1 + (5 * (5 <= 2 + x0) + (2 + x0) *
        (2 + x0 < 5)) * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5)
        ) + -1 * x0 * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5)
        ) + -1 * x1 * (5 * (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 *
        (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 * (5 <= 2 + x1) + (2 +
        x1) * (2 + x1 < 5))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + x4, tmp53, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5632
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 88
    x0 = xindex % 16
    x2 = xindex // 1408
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 256 * x2), tmp4 & xmask, other=0.0
        )
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 40, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-16 + x1) + 384 * x2), tmp9 &
        xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + (-16 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 64, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + 16 * (-40 + x1) + 384 * x2), tmp18 &
        xmask, other=0.0)
    tmp20 = tl.load(in_ptr4 + (-40 + x1), tmp18 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp18, tmp21, tmp22)
    tmp24 = tmp0 >= tmp16
    tl.full([1], 88, tl.int64)
    tmp27 = tl.load(in_ptr5 + (x0 + 16 * (-64 + x1) + 384 * x2), tmp24 &
        xmask, other=0.0)
    tmp28 = tl.load(in_ptr6 + (-64 + x1), tmp24 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp24, tmp29, tmp30)
    tmp32 = tl.where(tmp18, tmp23, tmp31)
    tmp33 = tl.where(tmp9, tmp14, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tl.store(out_ptr0 + x3, tmp34, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (24, 16, 5, 5), (400, 25, 5, 1))
    assert_size_stride(primals_5, (24,), (1,))
    assert_size_stride(primals_6, (24, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (24,), (1,))
    assert_size_stride(primals_8, (24, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_9, (24,), (1,))
    assert_size_stride(primals_10, (24, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (24,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1024)](buf1, primals_2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 24, 4, 4), (384, 16, 4, 1))
        buf3 = extern_kernels.convolution(buf1, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 24, 4, 4), (384, 16, 4, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_1[grid(1536)](buf4, primals_7, 1536,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 24, 4, 4), (384, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_avg_pool2d_2[grid(256)](primals_3, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 24, 4, 4), (384, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 88, 4, 4), (1408, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(5632)](buf1, buf2, primals_5, buf5,
            primals_9, buf7, primals_11, buf8, 5632, XBLOCK=256, num_warps=
            4, num_stages=1)
        del buf2
        del buf5
        del buf7
        del primals_11
        del primals_5
        del primals_9
    return (buf8, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf4, buf6)


class InceptionANew(torch.nn.Module):

    def __init__(self, in_channels):
        super(InceptionANew, self).__init__()
        self.branch1x1 = torch.nn.Conv2d(in_channels, 16, kernel_size=(1, 1))
        self.branch_pool = torch.nn.Conv2d(in_channels, 24, kernel_size=(1, 1))
        self.branch5x5 = torch.nn.Conv2d(16, 24, kernel_size=(5, 5), padding=2)
        self.branch3x3_2 = torch.nn.Conv2d(16, 24, kernel_size=(3, 3),
            padding=1)
        self.branch3x3_3 = torch.nn.Conv2d(24, 24, kernel_size=(3, 3),
            padding=1)

    def forward(self, input_0):
        primals_1 = self.branch1x1.weight
        primals_2 = self.branch1x1.bias
        primals_10 = self.branch_pool.weight
        primals_5 = self.branch_pool.bias
        primals_4 = self.branch5x5.weight
        primals_7 = self.branch5x5.bias
        primals_6 = self.branch3x3_2.weight
        primals_9 = self.branch3x3_2.bias
        primals_8 = self.branch3x3_3.weight
        primals_11 = self.branch3x3_3.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])
        return output[0]
