# 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_8/inductor_cache/dr/cdrhqqr2wo5vcik7n5w45bks3xpn7ysi3wkc5ars2jbaewyslk77.py
# Topologically Sorted Source Nodes: [sum_1, mask, float_1, sum_3], Original ATen: [aten.sum, aten.ne, aten._to_copy]
# Source node to ATen node mapping:
#   float_1 => convert_element_type
#   mask => ne
#   sum_1 => sum_1
#   sum_3 => sum_3
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%primals_2, [-1]), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%sum_1, 0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.float32), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%convert_element_type, [-1], True), kwargs = {})
triton_poi_fused__to_copy_ne_sum_0 = async_compile.triton('triton_poi_fused__to_copy_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: '*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__to_copy_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__to_copy_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
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 != tmp7
    tmp9 = tmp8.to(tl.float32)
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 != tmp7
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 != tmp7
    tmp28 = tmp27.to(tl.float32)
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 != tmp7
    tmp38 = tmp37.to(tl.float32)
    tmp39 = tmp29 + tmp38
    tl.store(out_ptr0 + (x0), tmp39, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lm/clmt5mbedo5grcrkmo2uvic4dqtz4mqyzrlxck3c6av65pmadwjh.py
# Topologically Sorted Source Nodes: [sum_2, add, mean], Original ATen: [aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean => div
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%primals_2, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, 1e-12), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, %add), kwargs = {})
triton_poi_fused_add_div_sum_1 = async_compile.triton('triton_poi_fused_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: '*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_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_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
    x4 = (xindex // 4)
    x5 = 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 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = 1e-12
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 / tmp9
    tl.store(out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tq/ctqlhx2zl2wzrwgwnabtpbtujabavnisiqizppafpaa2x2b4tqtv.py
# Topologically Sorted Source Nodes: [mul, mul_1, add_1], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_mul_2 = async_compile.triton('triton_poi_fused_add_mul_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_add_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_add_mul_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tl.store(out_ptr0 + (x2), tmp5, 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [sum_1, mask, float_1, sum_3], Original ATen: [aten.sum, aten.ne, aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_ne_sum_0.run(primals_2, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sum_2, add, mean], Original ATen: [aten.sum, aten.add, aten.div]
        triton_poi_fused_add_div_sum_1.run(primals_2, buf0, buf1, 64, grid=grid(64), stream=stream0)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, add_1], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_2.run(primals_1, buf2, buf3, 256, grid=grid(256), stream=stream0)
        del buf2
        del primals_1
    return (buf3, reinterpret_tensor(buf1, (16, 4), (4, 1), 0), )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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
from torch import nn


class BehaviorAggregator(nn.Module):

    def __init__(self, embedding_dim, gamma=0.5, aggregator='mean',
        dropout_rate=0.0):
        super(BehaviorAggregator, self).__init__()
        self.aggregator = aggregator
        self.gamma = gamma
        self.W_v = nn.Linear(embedding_dim, embedding_dim, bias=False)
        if self.aggregator in ['cross_attention', 'self_attention']:
            self.W_k = nn.Sequential(nn.Linear(embedding_dim, embedding_dim
                ), nn.Tanh())
            self.dropout = nn.Dropout(dropout_rate
                ) if dropout_rate > 0 else None
            if self.aggregator == 'self_attention':
                self.W_q = nn.Parameter(torch.Tensor(embedding_dim, 1))
                nn.init.xavier_normal_(self.W_q)

    def forward(self, id_emb, sequence_emb):
        out = id_emb
        if self.aggregator == 'mean':
            out = self.average_pooling(sequence_emb)
        elif self.aggregator == 'cross_attention':
            out = self.cross_attention(id_emb, sequence_emb)
        elif self.aggregator == 'self_attention':
            out = self.self_attention(sequence_emb)
        return self.gamma * id_emb + (1 - self.gamma) * out

    def cross_attention(self, id_emb, sequence_emb):
        key = self.W_k(sequence_emb)
        mask = sequence_emb.sum(dim=-1) == 0
        attention = torch.bmm(key, id_emb.unsqueeze(-1)).squeeze(-1)
        attention = self.masked_softmax(attention, mask)
        if self.dropout is not None:
            attention = self.dropout(attention)
        output = torch.bmm(attention.unsqueeze(1), sequence_emb).squeeze(1)
        return self.W_v(output)

    def self_attention(self, sequence_emb):
        key = self.W_k(sequence_emb)
        mask = sequence_emb.sum(dim=-1) == 0
        attention = torch.matmul(key, self.W_q).squeeze(-1)
        attention = self.masked_softmax(attention, mask)
        if self.dropout is not None:
            attention = self.dropout(attention)
        output = torch.bmm(attention.unsqueeze(1), sequence_emb).squeeze(1)
        return self.W_v(output)

    def average_pooling(self, sequence_emb):
        mask = sequence_emb.sum(dim=-1) != 0
        mean = sequence_emb.sum(dim=1) / (mask.float().sum(dim=-1, keepdim=
            True) + 1e-12)
        return self.W_v(mean)

    def masked_softmax(self, X, mask):
        X = X.masked_fill_(mask, 0)
        e_X = torch.exp(X)
        return e_X / (e_X.sum(dim=1, keepdim=True) + 1e-12)


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


def get_init_inputs():
    return [[], {'embedding_dim': 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 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__to_copy_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
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 != tmp7
    tmp9 = tmp8.to(tl.float32)
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 != tmp7
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 != tmp7
    tmp28 = tmp27.to(tl.float32)
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 != tmp7
    tmp38 = tmp37.to(tl.float32)
    tmp39 = tmp29 + tmp38
    tl.store(out_ptr0 + x0, tmp39, xmask)


@triton.jit
def triton_poi_fused_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
    x4 = xindex // 4
    x5 = 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 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = 1e-12
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 / tmp9
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tl.store(out_ptr0 + x2, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_ne_sum_0[grid(16)](primals_2, 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_add_div_sum_1[grid(64)](primals_2, buf0, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_2[grid(256)](primals_1, buf2, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        del primals_1
    return buf3, reinterpret_tensor(buf1, (16, 4), (4, 1), 0)


class BehaviorAggregatorNew(nn.Module):

    def __init__(self, embedding_dim, gamma=0.5, aggregator='mean',
        dropout_rate=0.0):
        super(BehaviorAggregatorNew, self).__init__()
        self.aggregator = aggregator
        self.gamma = gamma
        self.W_v = nn.Linear(embedding_dim, embedding_dim, bias=False)
        if self.aggregator in ['cross_attention', 'self_attention']:
            self.W_k = nn.Sequential(nn.Linear(embedding_dim, embedding_dim
                ), nn.Tanh())
            self.dropout = nn.Dropout(dropout_rate
                ) if dropout_rate > 0 else None
            if self.aggregator == 'self_attention':
                self.W_q = nn.Parameter(torch.Tensor(embedding_dim, 1))
                nn.init.xavier_normal_(self.W_q)

    def cross_attention(self, id_emb, sequence_emb):
        key = self.W_k(sequence_emb)
        mask = sequence_emb.sum(dim=-1) == 0
        attention = torch.bmm(key, id_emb.unsqueeze(-1)).squeeze(-1)
        attention = self.masked_softmax(attention, mask)
        if self.dropout is not None:
            attention = self.dropout(attention)
        output = torch.bmm(attention.unsqueeze(1), sequence_emb).squeeze(1)
        return self.W_v(output)

    def self_attention(self, sequence_emb):
        key = self.W_k(sequence_emb)
        mask = sequence_emb.sum(dim=-1) == 0
        attention = torch.matmul(key, self.W_q).squeeze(-1)
        attention = self.masked_softmax(attention, mask)
        if self.dropout is not None:
            attention = self.dropout(attention)
        output = torch.bmm(attention.unsqueeze(1), sequence_emb).squeeze(1)
        return self.W_v(output)

    def average_pooling(self, sequence_emb):
        mask = sequence_emb.sum(dim=-1) != 0
        mean = sequence_emb.sum(dim=1) / (mask.float().sum(dim=-1, keepdim=
            True) + 1e-12)
        return self.W_v(mean)

    def masked_softmax(self, X, mask):
        X = X.masked_fill_(mask, 0)
        e_X = torch.exp(X)
        return e_X / (e_X.sum(dim=1, keepdim=True) + 1e-12)

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