# AOT ID: ['0_inference']
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/2l/c2lskgt456tssjss33age2ge3rxkx423h7s6kanacrjo7kblt4m4.py
# Topologically Sorted Source Nodes: [ne, len_tmp, ne_1, sum_2], Original ATen: [aten.ne, aten.sum]
# Source node to ATen node mapping:
#   len_tmp => sum_1
#   ne => ne
#   ne_1 => ne_1
#   sum_2 => sum_2
# Graph fragment:
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg0_1, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%ne, [2]), kwargs = {})
#   %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%sum_1, 0), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%ne_1, [1]), kwargs = {})
triton_poi_fused_ne_sum_0 = async_compile.triton('triton_poi_fused_ne_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_ne_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_ne_sum_0(in_ptr0, out_ptr0, 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 + (16*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp62 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp66 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int64)
    tmp5 = tmp4 != tmp1
    tmp6 = tmp5.to(tl.int64)
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 != tmp1
    tmp10 = tmp9.to(tl.int64)
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 != tmp1
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp11 + tmp14
    tmp16 = tl.full([1], 0, tl.int64)
    tmp17 = tmp15 != tmp16
    tmp18 = tmp17.to(tl.int64)
    tmp20 = tmp19 != tmp1
    tmp21 = tmp20.to(tl.int64)
    tmp23 = tmp22 != tmp1
    tmp24 = tmp23.to(tl.int64)
    tmp25 = tmp21 + tmp24
    tmp27 = tmp26 != tmp1
    tmp28 = tmp27.to(tl.int64)
    tmp29 = tmp25 + tmp28
    tmp31 = tmp30 != tmp1
    tmp32 = tmp31.to(tl.int64)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp33 != tmp16
    tmp35 = tmp34.to(tl.int64)
    tmp36 = tmp18 + tmp35
    tmp38 = tmp37 != tmp1
    tmp39 = tmp38.to(tl.int64)
    tmp41 = tmp40 != tmp1
    tmp42 = tmp41.to(tl.int64)
    tmp43 = tmp39 + tmp42
    tmp45 = tmp44 != tmp1
    tmp46 = tmp45.to(tl.int64)
    tmp47 = tmp43 + tmp46
    tmp49 = tmp48 != tmp1
    tmp50 = tmp49.to(tl.int64)
    tmp51 = tmp47 + tmp50
    tmp52 = tmp51 != tmp16
    tmp53 = tmp52.to(tl.int64)
    tmp54 = tmp36 + tmp53
    tmp56 = tmp55 != tmp1
    tmp57 = tmp56.to(tl.int64)
    tmp59 = tmp58 != tmp1
    tmp60 = tmp59.to(tl.int64)
    tmp61 = tmp57 + tmp60
    tmp63 = tmp62 != tmp1
    tmp64 = tmp63.to(tl.int64)
    tmp65 = tmp61 + tmp64
    tmp67 = tmp66 != tmp1
    tmp68 = tmp67.to(tl.int64)
    tmp69 = tmp65 + tmp68
    tmp70 = tmp69 != tmp16
    tmp71 = tmp70.to(tl.int64)
    tmp72 = tmp54 + tmp71
    tl.store(out_ptr0 + (x0), tmp72, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yw/cyweluarz5kplvpgbfpvpwel67ohfhi5kugx7pjyaikjat2lg4ps.py
# Topologically Sorted Source Nodes: [out, aspect_len, div], Original ATen: [aten.sum, aten._to_copy, aten.div]
# Source node to ATen node mapping:
#   aspect_len => convert_element_type
#   div => div
#   out => sum_3
# Graph fragment:
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [1]), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%unsqueeze, torch.float32), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %convert_element_type), kwargs = {})
triton_poi_fused__to_copy_div_sum_1 = async_compile.triton('triton_poi_fused__to_copy_div_sum_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: '*i64', 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__to_copy_div_sum_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__to_copy_div_sum_1(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 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp6 / tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [ne, len_tmp, ne_1, sum_2], Original ATen: [aten.ne, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_ne_sum_0.run(arg0_1, buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, aspect_len, div], Original ATen: [aten.sum, aten._to_copy, aten.div]
        triton_poi_fused__to_copy_div_sum_1.run(arg0_1, buf0, buf1, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del buf0
    return (reinterpret_tensor(buf1, (4, 4, 4), (4, 0, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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


class AspectMean(nn.Module):

    def __init__(self, max_sen_len):
        """
        :param max_sen_len: maximum length of sentence
        """
        super(AspectMean, self).__init__()
        self.max_sen_len = max_sen_len

    def forward(self, aspect):
        """

        :param aspect: size: [batch_size, max_asp_len, embed_size]
        :return: aspect mean embedding, size: [batch_size, max_sen_len, embed_size]
        """
        len_tmp = torch.sum(aspect != 0, dim=2)
        aspect_len = torch.sum(len_tmp != 0, dim=1).unsqueeze(dim=1).float()
        out = aspect.sum(dim=1)
        out = out.div(aspect_len).unsqueeze(dim=1).expand(-1, self.
            max_sen_len, -1)
        return out


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


def get_init_inputs():
    return [[], {'max_sen_len': 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_ne_sum_0(in_ptr0, out_ptr0, 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 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp55 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp62 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp66 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int64)
    tmp5 = tmp4 != tmp1
    tmp6 = tmp5.to(tl.int64)
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 != tmp1
    tmp10 = tmp9.to(tl.int64)
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 != tmp1
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp11 + tmp14
    tmp16 = tl.full([1], 0, tl.int64)
    tmp17 = tmp15 != tmp16
    tmp18 = tmp17.to(tl.int64)
    tmp20 = tmp19 != tmp1
    tmp21 = tmp20.to(tl.int64)
    tmp23 = tmp22 != tmp1
    tmp24 = tmp23.to(tl.int64)
    tmp25 = tmp21 + tmp24
    tmp27 = tmp26 != tmp1
    tmp28 = tmp27.to(tl.int64)
    tmp29 = tmp25 + tmp28
    tmp31 = tmp30 != tmp1
    tmp32 = tmp31.to(tl.int64)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp33 != tmp16
    tmp35 = tmp34.to(tl.int64)
    tmp36 = tmp18 + tmp35
    tmp38 = tmp37 != tmp1
    tmp39 = tmp38.to(tl.int64)
    tmp41 = tmp40 != tmp1
    tmp42 = tmp41.to(tl.int64)
    tmp43 = tmp39 + tmp42
    tmp45 = tmp44 != tmp1
    tmp46 = tmp45.to(tl.int64)
    tmp47 = tmp43 + tmp46
    tmp49 = tmp48 != tmp1
    tmp50 = tmp49.to(tl.int64)
    tmp51 = tmp47 + tmp50
    tmp52 = tmp51 != tmp16
    tmp53 = tmp52.to(tl.int64)
    tmp54 = tmp36 + tmp53
    tmp56 = tmp55 != tmp1
    tmp57 = tmp56.to(tl.int64)
    tmp59 = tmp58 != tmp1
    tmp60 = tmp59.to(tl.int64)
    tmp61 = tmp57 + tmp60
    tmp63 = tmp62 != tmp1
    tmp64 = tmp63.to(tl.int64)
    tmp65 = tmp61 + tmp64
    tmp67 = tmp66 != tmp1
    tmp68 = tmp67.to(tl.int64)
    tmp69 = tmp65 + tmp68
    tmp70 = tmp69 != tmp16
    tmp71 = tmp70.to(tl.int64)
    tmp72 = tmp54 + tmp71
    tl.store(out_ptr0 + x0, tmp72, xmask)


@triton.jit
def triton_poi_fused__to_copy_div_sum_1(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 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp6 / tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_ne_sum_0[grid(4)](arg0_1, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__to_copy_div_sum_1[grid(16)](arg0_1, buf0, buf1, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del buf0
    return reinterpret_tensor(buf1, (4, 4, 4), (4, 0, 1), 0),


class AspectMeanNew(nn.Module):

    def __init__(self, max_sen_len):
        """
        :param max_sen_len: maximum length of sentence
        """
        super(AspectMeanNew, self).__init__()
        self.max_sen_len = max_sen_len

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
