# 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/s2/cs2yhv2fasr5vosqzjvuzmixontsq433wfyzz6it6qjns5pkay3s.py
# Topologically Sorted Source Nodes: [mlp_input], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   mlp_input => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %cat_1, %abs_1, %mul], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[2048], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 4, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + ((4*x1) + x0), tmp7, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp0 >= tmp5
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp12 = tl.where(tmp6, tmp8, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 16, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = (-8) + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp5
    tmp22 = tmp21 & tmp18
    tmp23 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp22, eviction_policy='evict_last', other=0.0)
    tmp24 = tmp19 >= tmp5
    tmp25 = tmp19 < tmp3
    tmp26 = tmp24 & tmp18
    tmp27 = tl.load(in_ptr3 + ((4*x1) + ((-4) + ((-8) + x0))), tmp26, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.where(tmp21, tmp23, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp18, tmp28, tmp29)
    tmp31 = tmp0 >= tmp16
    tmp32 = tl.full([1], 24, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tmp31 & tmp33
    tmp35 = (-16) + x0
    tmp36 = tmp35 >= tmp1
    tmp37 = tmp35 < tmp5
    tmp38 = tmp37 & tmp34
    tmp39 = tl.load(in_ptr0 + ((4*x1) + ((-16) + x0)), tmp38, eviction_policy='evict_last', other=0.0)
    tmp40 = tmp35 >= tmp5
    tmp41 = tmp35 < tmp3
    tmp42 = tmp40 & tmp34
    tmp43 = tl.load(in_ptr1 + ((4*x1) + ((-4) + ((-16) + x0))), tmp42, eviction_policy='evict_last', other=0.0)
    tmp44 = tl.where(tmp37, tmp39, tmp43)
    tmp45 = tl.load(in_ptr2 + ((4*x1) + ((-16) + x0)), tmp38, eviction_policy='evict_last', other=0.0)
    tmp46 = tl.load(in_ptr3 + ((4*x1) + ((-4) + ((-16) + x0))), tmp42, eviction_policy='evict_last', other=0.0)
    tmp47 = tl.where(tmp37, tmp45, tmp46)
    tmp48 = tmp44 - tmp47
    tmp49 = tl_math.abs(tmp48)
    tmp50 = tl.full(tmp49.shape, 0.0, tmp49.dtype)
    tmp51 = tl.where(tmp34, tmp49, tmp50)
    tmp52 = tmp0 >= tmp32
    tmp53 = tl.full([1], 32, tl.int64)
    tmp54 = tmp0 < tmp53
    tmp55 = (-24) + x0
    tmp56 = tmp55 >= tmp1
    tmp57 = tmp55 < tmp5
    tmp58 = tmp57 & tmp52
    tmp59 = tl.load(in_ptr0 + ((4*x1) + ((-24) + x0)), tmp58, eviction_policy='evict_last', other=0.0)
    tmp60 = tmp55 >= tmp5
    tmp61 = tmp55 < tmp3
    tmp62 = tmp60 & tmp52
    tmp63 = tl.load(in_ptr1 + ((4*x1) + ((-4) + ((-24) + x0))), tmp62, eviction_policy='evict_last', other=0.0)
    tmp64 = tl.where(tmp57, tmp59, tmp63)
    tmp65 = tl.load(in_ptr2 + ((4*x1) + ((-24) + x0)), tmp58, eviction_policy='evict_last', other=0.0)
    tmp66 = tl.load(in_ptr3 + ((4*x1) + ((-4) + ((-24) + x0))), tmp62, eviction_policy='evict_last', other=0.0)
    tmp67 = tl.where(tmp57, tmp65, tmp66)
    tmp68 = tmp64 * tmp67
    tmp69 = tl.full(tmp68.shape, 0.0, tmp68.dtype)
    tmp70 = tl.where(tmp52, tmp68, tmp69)
    tmp71 = tl.where(tmp34, tmp51, tmp70)
    tmp72 = tl.where(tmp18, tmp30, tmp71)
    tmp73 = tl.where(tmp4, tmp14, tmp72)
    tl.store(out_ptr0 + (x2), tmp73, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/sy/csyq3f2s27ausr5srhudvxwlyubeknzzno766mzdsi5o67nwnhkp.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   relu => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_7,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/hr/chrkejvm3c2syakhmn4sig4kxeujt4keymqjwm5clokafee7y72f.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => getitem
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {})
triton_poi_fused_max_2 = async_compile.triton('triton_poi_fused_max_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=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_max_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
    x3 = xindex % 64
    x4 = (xindex // 4)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp7 = tmp5 * tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp10 = tl.sigmoid(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp15 = tl.sigmoid(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tl.store(out_ptr0 + (x5), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (8, 32), (32, 1))
    assert_size_stride(primals_7, (8, ), (1, ))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [global_head], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [global_tail], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (16, 4, 4), (16, 4, 1), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mlp_input], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_4, buf0, primals_5, buf1, buf2, 2048, grid=grid(2048), stream=stream0)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 8), (1, 32), 0), out=buf3)
        del primals_6
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 8), (128, 32, 8, 1), 0); del buf3  # reuse
        buf7 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf4, primals_7, buf7, 512, grid=grid(512), stream=stream0)
        del primals_7
        buf5 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [pred], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf4, (64, 8), (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        triton_poi_fused_max_2.run(buf5, primals_1, buf6, 256, grid=grid(256), stream=stream0)
    return (buf6, primals_1, reinterpret_tensor(buf2, (64, 32), (32, 1), 0), reinterpret_tensor(buf4, (64, 8), (8, 1), 0), buf5, primals_8, buf7, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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
import torch.nn.functional as F


class MLP(nn.Module):

    def __init__(self, indim, hs, outdim, mlp_drop):
        super().__init__()
        """
        eh, et, |eh-et|, eh*et
        """
        indim = 4 * indim
        self.linear1 = nn.Linear(indim, 2 * hs)
        self.linear2 = nn.Linear(2 * hs, outdim)
        self.drop = nn.Dropout(mlp_drop)

    def forward(self, head_rep, tail_rep):
        """
        :param head_rep: (?, hs)
        :param tail_rep: (?, hs)
        :param doc_rep: (1, hs)
        :return: logits (?, outdim)
        """
        mlp_input = [head_rep, tail_rep, torch.abs(head_rep - tail_rep), 
            head_rep * tail_rep]
        mlp_input = torch.cat(mlp_input, -1)
        h = self.drop(F.relu(self.linear1(mlp_input)))
        return self.linear2(h)


class EntityClassifier(nn.Module):

    def __init__(self, hs, num_class, mlp_drop):
        super().__init__()
        indim = 2 * hs
        self.classifier = MLP(indim, hs, num_class, mlp_drop)

    def forward(self, global_head, global_tail, local_head, local_tail,
        path2ins):
        ins2path = torch.transpose(path2ins, 0, 1)
        global_head = torch.matmul(path2ins, global_head)
        global_tail = torch.matmul(path2ins, global_tail)
        head_rep, tail_rep = [], []
        head_rep.append(local_head)
        tail_rep.append(local_tail)
        head_rep.append(global_head)
        tail_rep.append(global_tail)
        head_rep = torch.cat(head_rep, dim=-1)
        tail_rep = torch.cat(tail_rep, dim=-1)
        pred = self.classifier(head_rep, tail_rep)
        pred = pred.squeeze(-1)
        pred = torch.sigmoid(pred)
        pred = pred.unsqueeze(0)
        ins2path = ins2path.unsqueeze(-1)
        pred = torch.max(pred * ins2path, dim=1)[0]
        return pred


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


def get_init_inputs():
    return [[], {'hs': 4, 'num_class': 4, 'mlp_drop': 0.5}]

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
import torch.nn as nn
import torch.nn.functional as F
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 4, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + (4 * x1 + x0), tmp7, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp0 >= tmp5
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tl.where(tmp6, tmp8, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 16, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = -8 + x0
    tmp21 = tmp19 < tmp5
    tmp22 = tmp21 & tmp18
    tmp23 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp22, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tmp19 >= tmp5
    tmp26 = tmp24 & tmp18
    tmp27 = tl.load(in_ptr3 + (4 * x1 + (-4 + (-8 + x0))), tmp26,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tl.where(tmp21, tmp23, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp18, tmp28, tmp29)
    tmp31 = tmp0 >= tmp16
    tmp32 = tl.full([1], 24, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tmp31 & tmp33
    tmp35 = -16 + x0
    tmp37 = tmp35 < tmp5
    tmp38 = tmp37 & tmp34
    tmp39 = tl.load(in_ptr0 + (4 * x1 + (-16 + x0)), tmp38, eviction_policy
        ='evict_last', other=0.0)
    tmp40 = tmp35 >= tmp5
    tmp42 = tmp40 & tmp34
    tmp43 = tl.load(in_ptr1 + (4 * x1 + (-4 + (-16 + x0))), tmp42,
        eviction_policy='evict_last', other=0.0)
    tmp44 = tl.where(tmp37, tmp39, tmp43)
    tmp45 = tl.load(in_ptr2 + (4 * x1 + (-16 + x0)), tmp38, eviction_policy
        ='evict_last', other=0.0)
    tmp46 = tl.load(in_ptr3 + (4 * x1 + (-4 + (-16 + x0))), tmp42,
        eviction_policy='evict_last', other=0.0)
    tmp47 = tl.where(tmp37, tmp45, tmp46)
    tmp48 = tmp44 - tmp47
    tmp49 = tl_math.abs(tmp48)
    tmp50 = tl.full(tmp49.shape, 0.0, tmp49.dtype)
    tmp51 = tl.where(tmp34, tmp49, tmp50)
    tmp52 = tmp0 >= tmp32
    tl.full([1], 32, tl.int64)
    tmp55 = -24 + x0
    tmp57 = tmp55 < tmp5
    tmp58 = tmp57 & tmp52
    tmp59 = tl.load(in_ptr0 + (4 * x1 + (-24 + x0)), tmp58, eviction_policy
        ='evict_last', other=0.0)
    tmp60 = tmp55 >= tmp5
    tmp62 = tmp60 & tmp52
    tmp63 = tl.load(in_ptr1 + (4 * x1 + (-4 + (-24 + x0))), tmp62,
        eviction_policy='evict_last', other=0.0)
    tmp64 = tl.where(tmp57, tmp59, tmp63)
    tmp65 = tl.load(in_ptr2 + (4 * x1 + (-24 + x0)), tmp58, eviction_policy
        ='evict_last', other=0.0)
    tmp66 = tl.load(in_ptr3 + (4 * x1 + (-4 + (-24 + x0))), tmp62,
        eviction_policy='evict_last', other=0.0)
    tmp67 = tl.where(tmp57, tmp65, tmp66)
    tmp68 = tmp64 * tmp67
    tmp69 = tl.full(tmp68.shape, 0.0, tmp68.dtype)
    tmp70 = tl.where(tmp52, tmp68, tmp69)
    tmp71 = tl.where(tmp34, tmp51, tmp70)
    tmp72 = tl.where(tmp18, tmp30, tmp71)
    tmp73 = tl.where(tmp4, tmp14, tmp72)
    tl.store(out_ptr0 + x2, tmp73, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_max_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
    x3 = xindex % 64
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp7 = tmp5 * tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp10 = tl.sigmoid(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp15 = tl.sigmoid(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tl.store(out_ptr0 + x5, tmp18, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (8, 32), (32, 1))
    assert_size_stride(primals_7, (8,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0
            ), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(primals_3, (16, 4, 4), (16, 4, 1), 0
            ), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(2048)](primals_4, buf0, primals_5, buf1,
            buf2, 2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 32), (32, 1), 0),
            reinterpret_tensor(primals_6, (32, 8), (1, 32), 0), out=buf3)
        del primals_6
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 8), (128, 32, 8, 1), 0)
        del buf3
        buf7 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(512)](buf4,
            primals_7, buf7, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf5 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf4, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_max_2[grid(256)](buf5, primals_1, buf6, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
    return buf6, primals_1, reinterpret_tensor(buf2, (64, 32), (32, 1), 0
        ), reinterpret_tensor(buf4, (64, 8), (8, 1), 0), buf5, primals_8, buf7


class MLP(nn.Module):

    def __init__(self, indim, hs, outdim, mlp_drop):
        super().__init__()
        """
        eh, et, |eh-et|, eh*et
        """
        indim = 4 * indim
        self.linear1 = nn.Linear(indim, 2 * hs)
        self.linear2 = nn.Linear(2 * hs, outdim)
        self.drop = nn.Dropout(mlp_drop)

    def forward(self, head_rep, tail_rep):
        """
        :param head_rep: (?, hs)
        :param tail_rep: (?, hs)
        :param doc_rep: (1, hs)
        :return: logits (?, outdim)
        """
        mlp_input = [head_rep, tail_rep, torch.abs(head_rep - tail_rep), 
            head_rep * tail_rep]
        mlp_input = torch.cat(mlp_input, -1)
        h = self.drop(F.relu(self.linear1(mlp_input)))
        return self.linear2(h)


class EntityClassifierNew(nn.Module):

    def __init__(self, hs, num_class, mlp_drop):
        super().__init__()
        indim = 2 * hs
        self.classifier = MLP(indim, hs, num_class, mlp_drop)

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        primals_6 = self.classifier.linear1.weight
        primals_7 = self.classifier.linear1.bias
        primals_8 = self.classifier.linear2.weight
        primals_9 = self.classifier.linear2.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_4 = input_3
        primals_5 = input_4
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
