# 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/fz/cfz4tzgiddaw2fy5bh2rj32s5huz55cu55x2vyxubcar7zzoef6e.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%primals_2, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_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=[64], 
    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__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/75/c75au4thfzsrnhpvv73ven7brk7fsciagbh6n4qikx4yw2fujr6f.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => exp, log, sub_1, sum_1
# Graph fragment:
#   %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 = {})
#   %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_1 = async_compile.triton('triton_poi_fused__log_softmax_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=[64], 
    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__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + (x3), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sz/csz66vykacf427j2te67saiuy5cjrxvpgacucheupaafqk4r3w3z.py
# Topologically Sorted Source Nodes: [log_softmax, x, x_1], Original ATen: [aten._log_softmax, aten.add, aten.logsumexp]
# Source node to ATen node mapping:
#   log_softmax => exp, log, sub_1, sum_1
#   x => add
#   x_1 => abs_1, add_1, amax_1, eq, exp_1, full_default, log_1, sub_2, sum_2, where
# Graph fragment:
#   %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 = {})
#   %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 = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %sub_1), kwargs = {})
#   %amax_1 : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%add, [2], True), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%amax_1,), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%abs_1, inf), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %amax_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %where), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [2]), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log_1, %squeeze), kwargs = {})
triton_poi_fused__log_softmax_add_logsumexp_2 = async_compile.triton('triton_poi_fused__log_softmax_add_logsumexp_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: '*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__log_softmax_add_logsumexp_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_add_logsumexp_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex % 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tl_math.abs(tmp14)
    tmp16 = float("inf")
    tmp17 = tmp15 == tmp16
    tmp18 = 0.0
    tmp19 = tl.where(tmp17, tmp18, tmp14)
    tmp20 = tmp2 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp5 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp9 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp13 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp31 + tmp19
    tl.store(in_out_ptr0 + (x3), tmp32, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(primals_2, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [log_softmax, x, x_1], Original ATen: [aten._log_softmax, aten.add, aten.logsumexp]
        triton_poi_fused__log_softmax_add_logsumexp_2.run(buf4, primals_1, buf1, 256, grid=grid(256), stream=stream0)
        del buf1
    return (buf4, primals_1, primals_2, buf4, )


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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 numpy as np
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable as Variable


class Sum(nn.Module):

    def __init__(self, in_channels, in_features, out_channels, dropout=0.0):
        """
        Create a Sum layer.

        Args:
            in_channels (int): Number of output channels from the previous layer.
            in_features (int): Number of input features.
            out_channels (int): Multiplicity of a sum node for a given scope set.
            dropout (float, optional): Dropout percentage.
        """
        super(Sum, self).__init__()
        self.in_channels = in_channels
        self.in_features = in_features
        self.out_channels = out_channels
        self.dropout = dropout
        assert out_channels > 0, 'Number of output channels must be at least 1, but was %s.' % out_channels
        in_features = int(in_features)
        ws = torch.randn(in_features, in_channels, out_channels)
        self.sum_weights = nn.Parameter(ws)
        self._bernoulli_dist = torch.distributions.Bernoulli(probs=dropout)
        self.out_shape = f'(N, {in_features}, C_in)'

    def forward(self, x):
        """
        Sum layer foward pass.

        Args:
            x: Input of shape [batch, in_features, in_channels].

        Returns:
            torch.Tensor: Output of shape [batch, in_features, out_channels]
        """
        if self.dropout > 0.0:
            r = self._bernoulli_dist.sample(x.shape).type(torch.bool)
            x[r] = np.NINF
        x = x.unsqueeze(3) + F.log_softmax(self.sum_weights, dim=1)
        x = torch.logsumexp(x, dim=2)
        return x

    def __repr__(self):
        return (
            'Sum(in_channels={}, in_features={}, out_channels={}, dropout={}, out_shape={})'
            .format(self.in_channels, self.in_features, self.out_channels,
            self.dropout, self.out_shape))


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


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

import torch
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
from torch import nn
from torch.autograd import Variable as Variable
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_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x3, tmp13, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_logsumexp_2(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tl_math.abs(tmp14)
    tmp16 = float('inf')
    tmp17 = tmp15 == tmp16
    tmp18 = 0.0
    tmp19 = tl.where(tmp17, tmp18, tmp14)
    tmp20 = tmp2 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp5 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp9 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp13 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp31 + tmp19
    tl.store(in_out_ptr0 + x3, tmp32, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(64)](primals_2, buf0, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_1[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = buf3
        del buf3
        triton_poi_fused__log_softmax_add_logsumexp_2[grid(256)](buf4,
            primals_1, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
    return buf4, primals_1, primals_2, buf4


class SumNew(nn.Module):

    def __init__(self, in_channels, in_features, out_channels, dropout=0.0):
        """
        Create a Sum layer.

        Args:
            in_channels (int): Number of output channels from the previous layer.
            in_features (int): Number of input features.
            out_channels (int): Multiplicity of a sum node for a given scope set.
            dropout (float, optional): Dropout percentage.
        """
        super(SumNew, self).__init__()
        self.in_channels = in_channels
        self.in_features = in_features
        self.out_channels = out_channels
        self.dropout = dropout
        assert out_channels > 0, 'Number of output channels must be at least 1, but was %s.' % out_channels
        in_features = int(in_features)
        ws = torch.randn(in_features, in_channels, out_channels)
        self.sum_weights = nn.Parameter(ws)
        self._bernoulli_dist = torch.distributions.Bernoulli(probs=dropout)
        self.out_shape = f'(N, {in_features}, C_in)'

    def __repr__(self):
        return (
            'Sum(in_channels={}, in_features={}, out_channels={}, dropout={}, out_shape={})'
            .format(self.in_channels, self.in_features, self.out_channels,
            self.dropout, self.out_shape))

    def forward(self, input_0):
        primals_2 = self.sum_weights
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]
