# 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/dg/cdgsdqqvkke7xwl5f3y4wse55on55jonq2akapygqqfdobxlnfzx.py
# Topologically Sorted Source Nodes: [z, output], Original ATen: [aten.cat, aten._log_softmax]
# Source node to ATen node mapping:
#   output => amax, exp, sub, sum_1
#   z => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution],), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%cat, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat, %amax), kwargs = {})
#   %exp : [num_users=1] = 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, [1], True), kwargs = {})
triton_poi_fused__log_softmax_cat_0 = async_compile.triton('triton_poi_fused__log_softmax_cat_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=[32], 
    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__log_softmax_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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__log_softmax_cat_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp20 = tl.load(in_ptr1 + (1))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp32 = tl.load(in_ptr1 + (2))
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK])
    tmp44 = tl.load(in_ptr1 + (3))
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 8, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr0 + (x0 + (16*((-4) + x1))), tmp11 & xmask, other=0.0)
    tmp15 = tmp14 + tmp7
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp10, tmp17)
    tmp19 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tl.load(in_ptr0 + (4 + x0 + (16*((-4) + x1))), tmp11 & xmask, other=0.0)
    tmp26 = tmp25 + tmp21
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp11, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp24, tmp28)
    tmp30 = triton_helpers.maximum(tmp18, tmp29)
    tmp31 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp34 = tmp31 + tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp4, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + (8 + x0 + (16*((-4) + x1))), tmp11 & xmask, other=0.0)
    tmp38 = tmp37 + tmp33
    tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
    tmp40 = tl.where(tmp11, tmp38, tmp39)
    tmp41 = tl.where(tmp4, tmp36, tmp40)
    tmp42 = triton_helpers.maximum(tmp30, tmp41)
    tmp43 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp46 = tmp43 + tmp45
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp4, tmp46, tmp47)
    tmp49 = tl.load(in_ptr0 + (12 + x0 + (16*((-4) + x1))), tmp11 & xmask, other=0.0)
    tmp50 = tmp49 + tmp45
    tmp51 = tl.full(tmp50.shape, 0.0, tmp50.dtype)
    tmp52 = tl.where(tmp11, tmp50, tmp51)
    tmp53 = tl.where(tmp4, tmp48, tmp52)
    tmp54 = triton_helpers.maximum(tmp42, tmp53)
    tmp55 = tmp18 - tmp54
    tmp56 = tl_math.exp(tmp55)
    tmp57 = tmp29 - tmp54
    tmp58 = tl_math.exp(tmp57)
    tmp59 = tmp56 + tmp58
    tmp60 = tmp41 - tmp54
    tmp61 = tl_math.exp(tmp60)
    tmp62 = tmp59 + tmp61
    tmp63 = tmp53 - tmp54
    tmp64 = tl_math.exp(tmp63)
    tmp65 = tmp62 + tmp64
    tl.store(out_ptr0 + (x2), tmp54, xmask)
    tl.store(out_ptr1 + (x2), tmp65, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hu/chufshtfgijunlmszzic7i4cwvtzymt6wqmgz4g6trkzjukgnhg5.py
# Topologically Sorted Source Nodes: [z, output], Original ATen: [aten.cat, aten._log_softmax]
# Source node to ATen node mapping:
#   output => log, sub, sub_1
#   z => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution],), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat, %amax), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
triton_poi_fused__log_softmax_cat_1 = async_compile.triton('triton_poi_fused__log_softmax_cat_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: '*fp32', 3: '*fp32', 4: '*fp32', 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__log_softmax_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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__log_softmax_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp19 = tl.load(in_ptr2 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr3 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x3 + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr0 + (x3 + (16*((-4) + x2))), tmp10 & xmask, other=0.0)
    tmp14 = tl.load(in_ptr1 + (x1), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp20 = tmp18 - tmp19
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp20 - tmp22
    tl.store(out_ptr0 + (x4), tmp23, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x0], 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, 4, 2, 2), (16, 4, 2, 1))
        buf1 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        buf2 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z, output], Original ATen: [aten.cat, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_cat_0.run(buf0, primals_2, buf1, buf2, 32, grid=grid(32), stream=stream0)
        buf3 = empty_strided_cuda((8, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z, output], Original ATen: [aten.cat, aten._log_softmax]
        triton_poi_fused__log_softmax_cat_1.run(buf0, primals_2, buf1, buf2, buf3, 128, grid=grid(128), stream=stream0)
        del buf0
        del buf1
        del buf2
        del primals_2
    return (buf3, primals_1, primals_3, buf3, )


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((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 Net(nn.Module):

    def __init__(self, Cin, Cout):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(Cin, Cout, (3, 3))

    def forward(self, x):
        x0 = self.conv1(x)
        x1 = self.conv1(x)
        z = torch.cat([x0, x1])
        output = F.log_softmax(z, dim=1)
        return output


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


def get_init_inputs():
    return [[], {'Cin': 4, 'Cout': 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
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__log_softmax_cat_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp20 = tl.load(in_ptr1 + 1)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp32 = tl.load(in_ptr1 + 2)
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK])
    tmp44 = tl.load(in_ptr1 + 3)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1)), tmp11 & xmask, other=0.0)
    tmp15 = tmp14 + tmp7
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp10, tmp17)
    tmp19 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tl.load(in_ptr0 + (4 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp26 = tmp25 + tmp21
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp11, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp24, tmp28)
    tmp30 = triton_helpers.maximum(tmp18, tmp29)
    tmp31 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp34 = tmp31 + tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp4, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + (8 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp38 = tmp37 + tmp33
    tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
    tmp40 = tl.where(tmp11, tmp38, tmp39)
    tmp41 = tl.where(tmp4, tmp36, tmp40)
    tmp42 = triton_helpers.maximum(tmp30, tmp41)
    tmp43 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp46 = tmp43 + tmp45
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp4, tmp46, tmp47)
    tmp49 = tl.load(in_ptr0 + (12 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp50 = tmp49 + tmp45
    tmp51 = tl.full(tmp50.shape, 0.0, tmp50.dtype)
    tmp52 = tl.where(tmp11, tmp50, tmp51)
    tmp53 = tl.where(tmp4, tmp48, tmp52)
    tmp54 = triton_helpers.maximum(tmp42, tmp53)
    tmp55 = tmp18 - tmp54
    tmp56 = tl_math.exp(tmp55)
    tmp57 = tmp29 - tmp54
    tmp58 = tl_math.exp(tmp57)
    tmp59 = tmp56 + tmp58
    tmp60 = tmp41 - tmp54
    tmp61 = tl_math.exp(tmp60)
    tmp62 = tmp59 + tmp61
    tmp63 = tmp53 - tmp54
    tmp64 = tl_math.exp(tmp63)
    tmp65 = tmp62 + tmp64
    tl.store(out_ptr0 + x2, tmp54, xmask)
    tl.store(out_ptr1 + x2, tmp65, xmask)


@triton.jit
def triton_poi_fused__log_softmax_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp19 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x3 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr0 + (x3 + 16 * (-4 + x2)), tmp10 & xmask, other=0.0)
    tmp14 = tl.load(in_ptr1 + x1, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp20 = tmp18 - tmp19
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp20 - tmp22
    tl.store(out_ptr0 + x4, tmp23, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 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, 4, 2, 2), (16, 4, 2, 1))
        buf1 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        buf2 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_cat_0[grid(32)](buf0, primals_2, buf1,
            buf2, 32, XBLOCK=32, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((8, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused__log_softmax_cat_1[grid(128)](buf0, primals_2,
            buf1, buf2, buf3, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del primals_2
    return buf3, primals_1, primals_3, buf3


class NetNew(nn.Module):

    def __init__(self, Cin, Cout):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(Cin, Cout, (3, 3))

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
