# 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/c4/cc4vdw5yatwuk2yw4r2yb75zt4ka2baffjb6g3p2wocglahzk7af.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [2, 2], [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_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_relu_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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 163840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 10
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tf/ctfbk2wlzsn6azkyps5b2cejhydlhaanvxa5pksgepgdwxduzy23.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten._softmax]
# Source node to ATen node mapping:
#   x_1 => convolution_1
#   x_2 => amax, exp, sub, sum_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%convolution_1, [-3], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-3], True), kwargs = {})
triton_poi_fused__softmax_convolution_1 = async_compile.triton('triton_poi_fused__softmax_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_convolution_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = (xindex // 4096)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (20480*x1)), None)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (4096 + x0 + (20480*x1)), None)
    tmp5 = tl.load(in_ptr1 + (1))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (8192 + x0 + (20480*x1)), None)
    tmp10 = tl.load(in_ptr1 + (2))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (12288 + x0 + (20480*x1)), None)
    tmp15 = tl.load(in_ptr1 + (3))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + (16384 + x0 + (20480*x1)), None)
    tmp20 = tl.load(in_ptr1 + (4))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp17 = tmp14 + tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp22 = tmp19 + tmp21
    tmp23 = triton_helpers.maximum(tmp18, tmp22)
    tmp24 = tmp3 - tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp7 - tmp23
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp12 - tmp23
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp17 - tmp23
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp22 - tmp23
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tl.store(out_ptr0 + (x2), tmp23, None)
    tl.store(out_ptr1 + (x2), tmp37, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gr/cgrpxovkeot5vyf6h7qolhzqemjwcolp5nbteyl6ib4dlwzyzpmz.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten._softmax]
# Source node to ATen node mapping:
#   x_1 => convolution_1
#   x_2 => div, exp, sub
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_convolution_2 = async_compile.triton('triton_poi_fused__softmax_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], '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__softmax_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 81920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 5
    x0 = xindex % 4096
    x2 = (xindex // 20480)
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + (4096*x2)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x0 + (4096*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(in_out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (10, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (10, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (5, 10, 5, 5), (250, 25, 5, 1))
    assert_size_stride(primals_5, (5, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 64, 64), (40960, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 163840, grid=grid(163840), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_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, 5, 64, 64), (20480, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten._softmax]
        triton_poi_fused__softmax_convolution_1.run(buf2, primals_5, buf3, buf4, 16384, grid=grid(16384), stream=stream0)
        buf5 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten._softmax]
        triton_poi_fused__softmax_convolution_2.run(buf5, primals_5, buf3, buf4, 81920, grid=grid(81920), stream=stream0)
        del buf3
        del buf4
        del primals_5
    return (buf5, primals_1, primals_3, primals_4, buf1, 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((10, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((5, 10, 5, 5), (250, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
import torch.nn.functional as F


class DummyNet(nn.Module):

    def __init__(self):
        super(DummyNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(10, 5, kernel_size=5, padding=2)
        self.softmax = nn.Softmax2d()

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.conv2(x)
        x = self.softmax(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
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_relu_0(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)
    x3 = xindex
    x1 = xindex // 4096 % 10
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, None)


@triton.jit
def triton_poi_fused__softmax_convolution_1(in_ptr0, in_ptr1, 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 % 4096
    x1 = xindex // 4096
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 20480 * x1), None)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (4096 + x0 + 20480 * x1), None)
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (8192 + x0 + 20480 * x1), None)
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (12288 + x0 + 20480 * x1), None)
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + (16384 + x0 + 20480 * x1), None)
    tmp20 = tl.load(in_ptr1 + 4)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp17 = tmp14 + tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp22 = tmp19 + tmp21
    tmp23 = triton_helpers.maximum(tmp18, tmp22)
    tmp24 = tmp3 - tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp7 - tmp23
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp12 - tmp23
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp17 - tmp23
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp22 - tmp23
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tl.store(out_ptr0 + x2, tmp23, None)
    tl.store(out_ptr1 + x2, tmp37, None)


@triton.jit
def triton_poi_fused__softmax_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 5
    x0 = xindex % 4096
    x2 = xindex // 20480
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 4096 * x2), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr2 + (x0 + 4096 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(in_out_ptr0 + x3, tmp7, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (10, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (5, 10, 5, 5), (250, 25, 5, 1))
    assert_size_stride(primals_5, (5,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 64, 64), (40960, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(163840)](buf1, primals_2, 
            163840, XBLOCK=1024, 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, 5, 64, 64), (20480, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1),
            torch.float32)
        buf4 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1),
            torch.float32)
        triton_poi_fused__softmax_convolution_1[grid(16384)](buf2,
            primals_5, buf3, buf4, 16384, XBLOCK=256, num_warps=4, num_stages=1
            )
        buf5 = buf2
        del buf2
        triton_poi_fused__softmax_convolution_2[grid(81920)](buf5,
            primals_5, buf3, buf4, 81920, XBLOCK=512, num_warps=8, num_stages=1
            )
        del buf3
        del buf4
        del primals_5
    return buf5, primals_1, primals_3, primals_4, buf1, buf5


class DummyNetNew(nn.Module):

    def __init__(self):
        super(DummyNetNew, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(10, 5, kernel_size=5, padding=2)
        self.softmax = nn.Softmax2d()

    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_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
