# 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_8/inductor_cache/3f/c3f7oez4j4wxbuu6bog5mkjwdjsnvrf7fjahrsqwplvpx3ixzqgk.py
# Topologically Sorted Source Nodes: [sum_2, ne, non_padding_length], Original ATen: [aten.sum, aten.ne]
# Source node to ATen node mapping:
#   ne => ne
#   non_padding_length => sum_3
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [-1]), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%sum_2, 0), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%ne, [1], True), 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=[16], 
    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, 2), 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 = 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 + ((4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (16 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (17 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (18 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (19 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (32 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (33 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (34 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (35 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (48 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (49 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (50 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (51 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 != tmp7
    tmp9 = tmp8.to(tl.int64)
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 != tmp7
    tmp18 = tmp17.to(tl.int64)
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 != tmp7
    tmp28 = tmp27.to(tl.int64)
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 != tmp7
    tmp38 = tmp37.to(tl.int64)
    tmp39 = tmp29 + tmp38
    tl.store(out_ptr0 + (x2), tmp39, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/gl/cglxvmucducvpcm57a7uhmoa6p5m3vn2l4axsvldj37wbmsg3vs4.py
# Topologically Sorted Source Nodes: [sum_pooling_matrix, float_1, add, embedding_vec], Original ATen: [aten.sum, aten._to_copy, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   embedding_vec => div
#   float_1 => convert_element_type
#   sum_pooling_matrix => sum_1
# Graph fragment:
#   %sum_1 : [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 = (%sum_3, torch.float32), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 1e-12), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add), kwargs = {})
triton_poi_fused__to_copy_add_div_sum_1 = async_compile.triton('triton_poi_fused__to_copy_add_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=[64], 
    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_add_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_add_div_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp7 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp7.to(tl.float32)
    tmp9 = 1e-12
    tmp10 = tmp8 + tmp9
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr0 + (x4), tmp11, 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, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.int64)
        # Topologically Sorted Source Nodes: [sum_2, ne, non_padding_length], Original ATen: [aten.sum, aten.ne]
        stream0 = get_raw_stream(0)
        triton_poi_fused_ne_sum_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sum_pooling_matrix, float_1, add, embedding_vec], Original ATen: [aten.sum, aten._to_copy, aten.add, aten.div]
        triton_poi_fused__to_copy_add_div_sum_1.run(arg0_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf0
    return (buf1, )


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, 4), (64, 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
from torch import nn


class MaskedAveragePooling(nn.Module):

    def __init__(self):
        super(MaskedAveragePooling, self).__init__()

    def forward(self, embedding_matrix):
        sum_pooling_matrix = torch.sum(embedding_matrix, dim=1)
        non_padding_length = (embedding_matrix.sum(dim=-1) != 0).sum(dim=1,
            keepdim=True)
        embedding_vec = sum_pooling_matrix / (non_padding_length.float() + 
            1e-12)
        return embedding_vec


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


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

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 import 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_ne_sum_0(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x1), xmask, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x1), xmask, eviction_policy
        ='evict_last')
    tmp10 = tl.load(in_ptr0 + (16 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (17 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (18 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (19 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (32 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (33 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (34 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (35 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (48 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (49 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (50 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (51 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 != tmp7
    tmp9 = tmp8.to(tl.int64)
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 != tmp7
    tmp18 = tmp17.to(tl.int64)
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 != tmp7
    tmp28 = tmp27.to(tl.int64)
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 != tmp7
    tmp38 = tmp37.to(tl.int64)
    tmp39 = tmp29 + tmp38
    tl.store(out_ptr0 + x2, tmp39, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_div_sum_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp7 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp7.to(tl.float32)
    tmp9 = 1e-12
    tmp10 = tmp8 + tmp9
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr0 + x4, tmp11, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_ne_sum_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__to_copy_add_div_sum_1[grid(64)](arg0_1, buf0,
            buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf0
    return buf1,


class MaskedAveragePoolingNew(nn.Module):

    def __init__(self):
        super(MaskedAveragePoolingNew, self).__init__()

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