# 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_7/inductor_cache/qt/cqtn54x53z5e5qza53u36hhsftfflvdqwoao2xnj7xnw5ocdcx3f.py
# Topologically Sorted Source Nodes: [add, add_1, output_2, output_3], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   output_2 => add_2
#   output_3 => amax
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %view_5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %permute_3), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %primals_10), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_2, [1], True), kwargs = {})
triton_poi_fused__softmax_add_0 = async_compile.triton('triton_poi_fused__softmax_add_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=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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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__softmax_add_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask)
    tmp5 = tl.load(in_ptr3 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp10 + tmp3
    tmp12 = tmp11 + tmp6
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 + tmp3
    tmp18 = tmp17 + tmp6
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 + tmp3
    tmp24 = tmp23 + tmp6
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tl.store(out_ptr0 + (x0), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ia/ciady5wligt2qz2veuk2j2mq4euwgkz2msjneq6me7apizr4flue.py
# Topologically Sorted Source Nodes: [add, add_1, output_2, output_3], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   output_2 => add_2
#   output_3 => exp, sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %view_5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %permute_3), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %primals_10), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_add_1 = async_compile.triton('triton_poi_fused__softmax_add_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=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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_1', 'mutated_arg_names': ['in_out_ptr0'], '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__softmax_add_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_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tl.store(in_out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/k6/ck6fz3qsfeqgn5jtm4ugikmu7cwvvlq3jpttijbb5kdniicwtyz6.py
# Topologically Sorted Source Nodes: [output_3], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   output_3 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=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__softmax_2', '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__softmax_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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, primals_10 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (150, 4), (4, 1))
    assert_size_stride(primals_4, (150, ), (1, ))
    assert_size_stride(primals_5, (150, 4), (4, 1))
    assert_size_stride(primals_6, (150, ), (1, ))
    assert_size_stride(primals_7, (1, 150), (150, 1))
    assert_size_stride(primals_8, (1, 150), (150, 1))
    assert_size_stride(primals_9, (1, 150, 150), (22500, 150, 1))
    assert_size_stride(primals_10, (1, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 150), (150, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_s], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, primals_1, reinterpret_tensor(primals_3, (4, 150), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_3
        del primals_4
        buf1 = empty_strided_cuda((16, 150), (150, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_t], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 150), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(primals_7, reinterpret_tensor(buf0, (150, 4), (1, 150), 0), out=buf2)
        buf3 = empty_strided_cuda((1, 1, 16), (16, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_d], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_8, (1, 1, 150), (150, 150, 1), 0), reinterpret_tensor(buf1, (1, 150, 16), (0, 1, 150), 0), out=buf3)
        buf4 = empty_strided_cuda((1, 4, 150), (600, 150, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (1, 4, 150), (600, 150, 1), 0), primals_9, out=buf4)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf4, (4, 1, 150), (150, 150, 1), 0), reinterpret_tensor(buf1, (4, 150, 4), (600, 1, 150), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [add, add_1, output_2, output_3], Original ATen: [aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_0.run(buf5, buf3, buf2, primals_10, buf6, 4, grid=grid(4), stream=stream0)
        buf7 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [add, add_1, output_2, output_3], Original ATen: [aten.add, aten._softmax]
        triton_poi_fused__softmax_add_1.run(buf7, buf5, buf2, primals_10, buf6, 16, grid=grid(16), stream=stream0)
        del buf2
        del buf6
        del primals_10
        buf8 = reinterpret_tensor(buf5, (4, 4), (4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [output_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf7, buf8, 16, grid=grid(16), stream=stream0)
        del buf7
    return (buf8, primals_1, primals_7, buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, buf8, reinterpret_tensor(buf4, (4, 150, 1), (150, 1, 150), 0), reinterpret_tensor(primals_9, (1, 150, 150), (22500, 1, 150), 0), reinterpret_tensor(primals_8, (1, 150, 1), (150, 1, 150), 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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((150, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((150, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((150, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((150, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 150), (150, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 150), (150, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, 150, 150), (22500, 150, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, 1), (1, 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, primals_10])
    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.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn


class BiaffineAttention(nn.Module):
    """
    Adopted from NeuroNLP2:
        https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attention.py

    Bi-Affine attention layer.
    """

    def __init__(self, input_size_encoder, input_size_decoder, hidden_size=
        150, num_labels=1, biaffine=True, **kwargs):
        """
        Args:
            input_size_encoder: int
                the dimension of the encoder input.
            input_size_decoder: int
                the dimension of the decoder input.
            num_labels: int
                the number of labels of the crf layer
            biaffine: bool
                if apply bi-affine parameter.
            **kwargs:
        """
        super(BiaffineAttention, self).__init__()
        self.input_size_encoder = input_size_encoder
        self.input_size_decoder = input_size_decoder
        self.hidden_size = hidden_size
        self.linear_encoder = torch.nn.Linear(self.input_size_encoder, self
            .hidden_size)
        self.linear_decoder = torch.nn.Linear(self.input_size_decoder, self
            .hidden_size)
        self.num_labels = num_labels
        self.biaffine = biaffine
        self.W_d = Parameter(torch.Tensor(self.num_labels, self.hidden_size))
        self.W_e = Parameter(torch.Tensor(self.num_labels, self.hidden_size))
        self.b = Parameter(torch.Tensor(1, self.num_labels))
        if self.biaffine:
            self.U = Parameter(torch.Tensor(self.num_labels, self.
                hidden_size, self.hidden_size))
        else:
            self.register_parameter('U', None)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_normal_(self.W_d)
        nn.init.xavier_normal_(self.W_e)
        nn.init.constant_(self.b, 0.0)
        if self.biaffine:
            nn.init.xavier_normal_(self.U)

    def forward(self, input_s, input_t, mask_d=None, mask_e=None):
        """
        Args:
            input_s: Tensor
                the student input tensor with shape = [batch, input_size]
            input_t: Tensor
                the teacher input tensor with shape = [batch, num_teachers, input_size]
            mask_d: None
            mask_e: None
        Returns: Tensor
            the energy tensor with shape = [batch, length]
        """
        assert input_s.size(0) == input_t.size(0
            ), 'batch sizes of encoder and decoder are requires to be equal.'
        input_s.size()
        _, _num_teachers, _ = input_t.size()
        input_s = self.linear_encoder(input_s)
        input_t = self.linear_decoder(input_t)
        out_e = torch.matmul(self.W_e, input_s.transpose(1, 0)).transpose(1, 0)
        out_d = torch.einsum('nd,bnd->bn', self.W_d, input_t)
        if self.biaffine:
            output = torch.einsum('bd,nde->bne', input_s, self.U)
            output = torch.einsum('bne,bne->bn', output, input_t)
            output = output + out_d + out_e + self.b
        else:
            output = out_d + out_d + self.b
        if mask_d is not None and mask_e is not None:
            output = output * mask_d.unsqueeze(1).unsqueeze(3
                ) * mask_e.unsqueeze(1).unsqueeze(2)
        output = torch.nn.functional.softmax(output, 1)
        return output


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


def get_init_inputs():
    return [[], {'input_size_encoder': 4, 'input_size_decoder': 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
import torch.nn as nn
import torch.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.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_add_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask)
    tmp5 = tl.load(in_ptr3 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp10 + tmp3
    tmp12 = tmp11 + tmp6
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 + tmp3
    tmp18 = tmp17 + tmp6
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 + tmp3
    tmp24 = tmp23 + tmp6
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tl.store(out_ptr0 + x0, tmp25, xmask)


@triton.jit
def triton_poi_fused__softmax_add_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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tl.store(in_out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (150, 4), (4, 1))
    assert_size_stride(primals_4, (150,), (1,))
    assert_size_stride(primals_5, (150, 4), (4, 1))
    assert_size_stride(primals_6, (150,), (1,))
    assert_size_stride(primals_7, (1, 150), (150, 1))
    assert_size_stride(primals_8, (1, 150), (150, 1))
    assert_size_stride(primals_9, (1, 150, 150), (22500, 150, 1))
    assert_size_stride(primals_10, (1, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 150), (150, 1), torch.float32)
        extern_kernels.addmm(primals_4, primals_1, reinterpret_tensor(
            primals_3, (4, 150), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_3
        del primals_4
        buf1 = empty_strided_cuda((16, 150), (150, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 150), (1, 4),
            0), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_7, reinterpret_tensor(buf0, (150, 4), (1,
            150), 0), out=buf2)
        buf3 = empty_strided_cuda((1, 1, 16), (16, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_8, (1, 1, 150), (150,
            150, 1), 0), reinterpret_tensor(buf1, (1, 150, 16), (0, 1, 150),
            0), out=buf3)
        buf4 = empty_strided_cuda((1, 4, 150), (600, 150, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (1, 4, 150), (600, 150,
            1), 0), primals_9, out=buf4)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (4, 1, 150), (150, 150,
            1), 0), reinterpret_tensor(buf1, (4, 150, 4), (600, 1, 150), 0),
            out=buf5)
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_0[grid(4)](buf5, buf3, buf2,
            primals_10, buf6, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0)
        del buf3
        triton_poi_fused__softmax_add_1[grid(16)](buf7, buf5, buf2,
            primals_10, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf2
        del buf6
        del primals_10
        buf8 = reinterpret_tensor(buf5, (4, 4), (4, 1), 0)
        del buf5
        triton_poi_fused__softmax_2[grid(16)](buf7, buf8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf7
    return buf8, primals_1, primals_7, buf0, reinterpret_tensor(primals_2,
        (16, 4), (4, 1), 0), buf1, buf8, reinterpret_tensor(buf4, (4, 150, 
        1), (150, 1, 150), 0), reinterpret_tensor(primals_9, (1, 150, 150),
        (22500, 1, 150), 0), reinterpret_tensor(primals_8, (1, 150, 1), (
        150, 1, 150), 0)


class BiaffineAttentionNew(nn.Module):
    """
    Adopted from NeuroNLP2:
        https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attention.py

    Bi-Affine attention layer.
    """

    def __init__(self, input_size_encoder, input_size_decoder, hidden_size=
        150, num_labels=1, biaffine=True, **kwargs):
        """
        Args:
            input_size_encoder: int
                the dimension of the encoder input.
            input_size_decoder: int
                the dimension of the decoder input.
            num_labels: int
                the number of labels of the crf layer
            biaffine: bool
                if apply bi-affine parameter.
            **kwargs:
        """
        super(BiaffineAttentionNew, self).__init__()
        self.input_size_encoder = input_size_encoder
        self.input_size_decoder = input_size_decoder
        self.hidden_size = hidden_size
        self.linear_encoder = torch.nn.Linear(self.input_size_encoder, self
            .hidden_size)
        self.linear_decoder = torch.nn.Linear(self.input_size_decoder, self
            .hidden_size)
        self.num_labels = num_labels
        self.biaffine = biaffine
        self.W_d = Parameter(torch.Tensor(self.num_labels, self.hidden_size))
        self.W_e = Parameter(torch.Tensor(self.num_labels, self.hidden_size))
        self.b = Parameter(torch.Tensor(1, self.num_labels))
        if self.biaffine:
            self.U = Parameter(torch.Tensor(self.num_labels, self.
                hidden_size, self.hidden_size))
        else:
            self.register_parameter('U', None)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_normal_(self.W_d)
        nn.init.xavier_normal_(self.W_e)
        nn.init.constant_(self.b, 0.0)
        if self.biaffine:
            nn.init.xavier_normal_(self.U)

    def forward(self, input_0, input_1):
        primals_7 = self.W_d
        primals_8 = self.W_e
        primals_10 = self.b
        primals_9 = self.U
        primals_3 = self.linear_encoder.weight
        primals_4 = self.linear_encoder.bias
        primals_5 = self.linear_decoder.weight
        primals_6 = self.linear_decoder.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
