# 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_6/inductor_cache/2g/c2gvlks5sbqkuwqyiccjiusco7ue56iwpq73p5hjzatsoypvg7ws.py
# Topologically Sorted Source Nodes: [x_gap, x_gap_1], Original ATen: [aten.sum, aten.mean]
# Source node to ATen node mapping:
#   x_gap => sum_1
#   x_gap_1 => mean
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_1, [1]), kwargs = {})
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%sum_1, [-1, -2], True), kwargs = {})
triton_poi_fused_mean_sum_0 = async_compile.triton('triton_poi_fused_mean_sum_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mean_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/eu/ceubdeonzenw4whfvdyk5v5niod23dclvkhtadg3obzmszby7poj.py
# Topologically Sorted Source Nodes: [x_gap_2, x_gap_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_gap_2 => convolution_1
#   x_gap_3 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mean, %primals_3, %primals_4, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[128], 
    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_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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, 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, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vh/cvhjvm6rcoyoxhvafv6lxzjgpllhlxtmi5rqribcbve52zweula5.py
# Topologically Sorted Source Nodes: [x_attn], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_attn => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_5, %primals_6, [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=[32], 
    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 = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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_6/inductor_cache/k6/ck6wsgrojv53fw7vlpp76xhvi7fna3wu44b6eljn3hqix5ugpqoa.py
# Topologically Sorted Source Nodes: [mul, out], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   out => sum_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view_5), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_mul_sum_3 = async_compile.triton('triton_poi_fused_mul_sum_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_3', '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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1)), xmask)
    tmp3 = tl.load(in_ptr1 + (x0 + (8*x1)), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x0 + (8*x1)), xmask)
    tmp13 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp3 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp4 - tmp5
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp7 / tmp10
    tmp12 = tmp2 * tmp11
    tmp14 = triton_helpers.maximum(tmp1, tmp13)
    tmp15 = tmp9 / tmp10
    tmp16 = tmp14 * tmp15
    tmp17 = tmp12 + tmp16
    tl.store(out_ptr0 + (x2), tmp17, 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 = args
    args.clear()
    assert_size_stride(primals_1, (8, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (32, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (8, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_6, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=2, bias=None)
        assert_size_stride(buf0, (4, 8, 1, 1), (8, 1, 1, 1))
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_gap, x_gap_1], Original ATen: [aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mean_sum_0.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [x_gap_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 1, 1), (32, 1, 1, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_gap_2, x_gap_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_4, 128, grid=grid(128), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [x_attn], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 8, 1, 1), (8, 1, 1, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_attn], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf5, primals_6, 32, grid=grid(32), stream=stream0)
        del primals_6
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, out], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_3.run(buf0, buf5, buf6, 16, grid=grid(16), stream=stream0)
    return (buf6, primals_1, primals_2, primals_3, primals_5, buf0, buf1, buf3, buf5, )


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((8, 2, 4, 4), (32, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel


class RadixSoftmax(nn.Module):

    def __init__(self, radix, cardinality):
        super(RadixSoftmax, self).__init__()
        self.radix = radix
        self.cardinality = cardinality

    def forward(self, x):
        batch = x.size(0)
        if self.radix > 1:
            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
            x = F.softmax(x, dim=1)
            x = x.reshape(batch, -1)
        else:
            x = torch.sigmoid(x)
        return x


class SplitAttnConv2d(nn.Module):
    """Split-Attention Conv2d
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=False, radix=2,
        reduction_factor=4, act_layer=nn.ReLU, norm_layer=None, drop_block=
        None, **kwargs):
        super(SplitAttnConv2d, self).__init__()
        self.radix = radix
        self.drop_block = drop_block
        mid_chs = out_channels * radix
        attn_chs = max(in_channels * radix // reduction_factor, 32)
        self.conv = nn.Conv2d(in_channels, mid_chs, kernel_size, stride,
            padding, dilation, groups=groups * radix, bias=bias, **kwargs)
        self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
        self.act0 = act_layer(inplace=True)
        self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
        self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
        self.act1 = act_layer(inplace=True)
        self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
        self.rsoftmax = RadixSoftmax(radix, groups)

    def forward(self, x):
        x = self.conv(x)
        if self.bn0 is not None:
            x = self.bn0(x)
        if self.drop_block is not None:
            x = self.drop_block(x)
        x = self.act0(x)
        B, RC, H, W = x.shape
        if self.radix > 1:
            x = x.reshape((B, self.radix, RC // self.radix, H, W))
            x_gap = x.sum(dim=1)
        else:
            x_gap = x
        x_gap = F.adaptive_avg_pool2d(x_gap, 1)
        x_gap = self.fc1(x_gap)
        if self.bn1 is not None:
            x_gap = self.bn1(x_gap)
        x_gap = self.act1(x_gap)
        x_attn = self.fc2(x_gap)
        x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
        if self.radix > 1:
            out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))
                ).sum(dim=1)
        else:
            out = x * x_attn
        return out.contiguous()


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
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_mean_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp3 = tl.load(in_ptr1 + (x0 + 8 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x0 + 8 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp3 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp4 - tmp5
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp7 / tmp10
    tmp12 = tmp2 * tmp11
    tmp14 = triton_helpers.maximum(tmp1, tmp13)
    tmp15 = tmp9 / tmp10
    tmp16 = tmp14 * tmp15
    tmp17 = tmp12 + tmp16
    tl.store(out_ptr0 + x2, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (8, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (32, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (8, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_6, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=2, bias=None)
        assert_size_stride(buf0, (4, 8, 1, 1), (8, 1, 1, 1))
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sum_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 1, 1), (32, 1, 1, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(128)](buf3, primals_4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf4 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 8, 1, 1), (8, 1, 1, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(32)](buf5, primals_6, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_mul_sum_3[grid(16)](buf0, buf5, buf6, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
    return (buf6, primals_1, primals_2, primals_3, primals_5, buf0, buf1,
        buf3, buf5)


class RadixSoftmax(nn.Module):

    def __init__(self, radix, cardinality):
        super(RadixSoftmax, self).__init__()
        self.radix = radix
        self.cardinality = cardinality

    def forward(self, x):
        batch = x.size(0)
        if self.radix > 1:
            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
            x = F.softmax(x, dim=1)
            x = x.reshape(batch, -1)
        else:
            x = torch.sigmoid(x)
        return x


class SplitAttnConv2dNew(nn.Module):
    """Split-Attention Conv2d
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=False, radix=2,
        reduction_factor=4, act_layer=nn.ReLU, norm_layer=None, drop_block=
        None, **kwargs):
        super(SplitAttnConv2dNew, self).__init__()
        self.radix = radix
        self.drop_block = drop_block
        mid_chs = out_channels * radix
        attn_chs = max(in_channels * radix // reduction_factor, 32)
        self.conv = nn.Conv2d(in_channels, mid_chs, kernel_size, stride,
            padding, dilation, groups=groups * radix, bias=bias, **kwargs)
        self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
        self.act0 = act_layer(inplace=True)
        self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
        self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
        self.act1 = act_layer(inplace=True)
        self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
        self.rsoftmax = RadixSoftmax(radix, groups)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
