# 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_1/inductor_cache/lq/clqobdukzpbkgeirgnvgulpmn2isxuyjfio7deusrshagkjvl4li.py
# Topologically Sorted Source Nodes: [sub, mul, sub_1, scores], Original ATen: [aten.rsub, aten.mul, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   mul => mul
#   scores => amax, exp, sub_2, sum_1
#   sub => sub
#   sub_1 => sub_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_4), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 1e+20), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub_1, [1], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_mul_rsub_sub_0 = async_compile.triton('triton_poi_fused__softmax_mul_rsub_sub_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_mul_rsub_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_rsub_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = 1e+20
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 - tmp8
    tmp11 = tmp10 + tmp2
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp7
    tmp15 = tmp11 - tmp14
    tmp16 = triton_helpers.maximum(tmp9, tmp15)
    tmp18 = tmp17 + tmp2
    tmp20 = tmp5 - tmp19
    tmp21 = tmp20 * tmp7
    tmp22 = tmp18 - tmp21
    tmp23 = triton_helpers.maximum(tmp16, tmp22)
    tmp25 = tmp24 + tmp2
    tmp27 = tmp5 - tmp26
    tmp28 = tmp27 * tmp7
    tmp29 = tmp25 - tmp28
    tmp30 = triton_helpers.maximum(tmp23, tmp29)
    tmp31 = tmp9 - tmp30
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp15 - tmp30
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tmp36 = tmp22 - tmp30
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp35 + tmp37
    tmp39 = tmp29 - tmp30
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp38 + tmp40
    tl.store(out_ptr0 + (x0), tmp30, xmask)
    tl.store(out_ptr1 + (x0), tmp41, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/da/cdaw666cppfgx6wcwv4ckhvc524daustk2y2woennedczz4cimzv.py
# Topologically Sorted Source Nodes: [sub, mul, sub_1, scores], Original ATen: [aten.rsub, aten.mul, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   mul => mul
#   scores => div, exp, sub_2
#   sub => sub
#   sub_1 => sub_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_4), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 1e+20), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %mul), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_mul_rsub_sub_1 = async_compile.triton('triton_poi_fused__softmax_mul_rsub_sub_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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__softmax_mul_rsub_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_rsub_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + (x2), xmask)
    tmp10 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = 1e+20
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 - tmp8
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(in_out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/m2/cm25t6aoec4n6sgspbcsocz27g2angvsw2gvg7feqwzkrgwwi6dq.py
# Topologically Sorted Source Nodes: [context], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   context => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%expand, [1]), kwargs = {})
triton_poi_fused_sum_2 = async_compile.triton('triton_poi_fused_sum_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sum_2(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
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, sub_1, scores], Original ATen: [aten.rsub, aten.mul, aten.sub, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_mul_rsub_sub_0.run(buf0, primals_3, primals_4, buf1, buf2, 4, grid=grid(4), stream=stream0)
        buf3 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub, mul, sub_1, scores], Original ATen: [aten.rsub, aten.mul, aten.sub, aten._softmax]
        triton_poi_fused__softmax_mul_rsub_sub_1.run(buf3, primals_3, primals_4, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del buf1
        del buf2
        del primals_3
        del primals_4
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [context], Original ATen: [aten.sum]
        triton_poi_fused_sum_2.run(buf3, buf4, 16, grid=grid(16), stream=stream0)
    return (buf4, buf3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), 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, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from torch import nn
from torch.nn import functional as F


class SelfAttn(nn.Module):

    def __init__(self, denc, dropout=0, enc_trans=None, dec_trans=None):
        super().__init__()
        self.scorer = nn.Linear(denc, 1)
        self.dropout = nn.Dropout(dropout)
        self.enc_trans = enc_trans or nn.Identity()

    def forward(self, enc, enc_mask):
        enc_trans = self.dropout(self.enc_trans(enc))
        raw_scores = self.scorer(enc).squeeze(2)
        scores = F.softmax(raw_scores - (1 - enc_mask) * 1e+20, dim=1)
        context = scores.unsqueeze(2).expand_as(enc_trans).sum(1)
        return context, scores


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


def get_init_inputs():
    return [[], {'denc': 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__softmax_mul_rsub_sub_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = 1e+20
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 - tmp8
    tmp11 = tmp10 + tmp2
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp7
    tmp15 = tmp11 - tmp14
    tmp16 = triton_helpers.maximum(tmp9, tmp15)
    tmp18 = tmp17 + tmp2
    tmp20 = tmp5 - tmp19
    tmp21 = tmp20 * tmp7
    tmp22 = tmp18 - tmp21
    tmp23 = triton_helpers.maximum(tmp16, tmp22)
    tmp25 = tmp24 + tmp2
    tmp27 = tmp5 - tmp26
    tmp28 = tmp27 * tmp7
    tmp29 = tmp25 - tmp28
    tmp30 = triton_helpers.maximum(tmp23, tmp29)
    tmp31 = tmp9 - tmp30
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp15 - tmp30
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tmp36 = tmp22 - tmp30
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp35 + tmp37
    tmp39 = tmp29 - tmp30
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp38 + tmp40
    tl.store(out_ptr0 + x0, tmp30, xmask)
    tl.store(out_ptr1 + x0, tmp41, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_rsub_sub_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp10 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = 1e+20
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 - tmp8
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(in_out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_sum_2(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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_mul_rsub_sub_0[grid(4)](buf0, primals_3,
            primals_4, buf1, buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
        del buf0
        triton_poi_fused__softmax_mul_rsub_sub_1[grid(16)](buf3, primals_3,
            primals_4, buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del buf2
        del primals_3
        del primals_4
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_sum_2[grid(16)](buf3, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf4, buf3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf3


class SelfAttnNew(nn.Module):

    def __init__(self, denc, dropout=0, enc_trans=None, dec_trans=None):
        super().__init__()
        self.scorer = nn.Linear(denc, 1)
        self.dropout = nn.Dropout(dropout)
        self.enc_trans = enc_trans or nn.Identity()

    def forward(self, input_0, input_1):
        primals_2 = self.scorer.weight
        primals_3 = self.scorer.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
