# 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_4/inductor_cache/3l/c3lo77c7wjxasxrhtr6wesb72ods2d2rxnxhbfieun7j2wukm3wn.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_4, %primals_3], -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=[128], 
    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_cat_0', '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bd/cbd5vrzay2jbzmubtgdwxlp37vuwvz6hkpnirlmnp5x7bwlay5gq.py
# Topologically Sorted Source Nodes: [att_weigths], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_weigths => amax, clone, exp, sub, sum_1
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp10 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp17 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 + tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 + tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 + tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp19, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dr/cdrn6bnobxdzhbn4umg6cz5cycfofs62kppoaiycjgik6nkueiql.py
# Topologically Sorted Source Nodes: [att_weigths], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_weigths => clone, div, exp, sub
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=2] = 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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 6), 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, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = (yindex // 4)
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr4 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(out_ptr0 + (y0 + (4*x2) + (16*y1)), tmp9, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten._trilinear]
        buf0 = torch.ops.aten._trilinear.default(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), primals_1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        del primals_1
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_4, primals_3, buf2, 128, grid=grid(128), stream=stream0)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [att_weigths], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf1, primals_2, buf3, buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_weigths], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf1, primals_2, buf3, buf4, buf5, buf6, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf1
        del buf4
        del buf5
        del primals_2
        buf7 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [att_out], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf6, primals_3, out=buf7)
    return (buf7, primals_3, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(buf2, (16, 8), (8, 1), 0), buf6, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class BiaffineAttention(nn.Module):

    def __init__(self, in1_features, in2_features, num_label, bias=True):
        super(BiaffineAttention, self).__init__()
        self.bilinear = nn.Bilinear(in1_features, in2_features, num_label,
            bias=bias)
        self.linear = nn.Linear(in1_features + in2_features, num_label,
            bias=False)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, head, dep):
        """
        :param head: [batch, seq_len, hidden] 输入特征1, 即label-head
        :param dep: [batch, seq_len, hidden] 输入特征2, 即label-dep
        :return output: [batch, seq_len, num_cls] 每个元素对应类别的概率图
        """
        output = self.bilinear(head, dep)
        biaff_score = output + self.linear(torch.cat((head, dep), dim=-1))
        biaff_score = biaff_score.transpose(1, 2)
        att_weigths = self.softmax(biaff_score)
        att_out = torch.bmm(att_weigths, dep)
        return att_out


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


def get_init_inputs():
    return [[], {'in1_features': 4, 'in2_features': 4, 'num_label': 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
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, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp17 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 + tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 + tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 + tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp19, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = yindex // 4
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr4 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(out_ptr0 + (y0 + 4 * x2 + 16 * y1), tmp9, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._trilinear.default(reinterpret_tensor(
            primals_4, (16, 4), (4, 1), 0), primals_1, reinterpret_tensor(
            primals_3, (16, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        del primals_1
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_4, primals_3, buf2, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf1, primals_2, buf3, buf4,
            buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(16, 4)](buf1, primals_2, buf3,
            buf4, buf5, buf6, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1,
            num_stages=1)
        del buf1
        del buf4
        del buf5
        del primals_2
        buf7 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(buf6, primals_3, out=buf7)
    return buf7, primals_3, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (16, 8), (8, 1), 0), buf6


class BiaffineAttentionNew(nn.Module):

    def __init__(self, in1_features, in2_features, num_label, bias=True):
        super(BiaffineAttentionNew, self).__init__()
        self.bilinear = nn.Bilinear(in1_features, in2_features, num_label,
            bias=bias)
        self.linear = nn.Linear(in1_features + in2_features, num_label,
            bias=False)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0, input_1):
        primals_1 = self.bilinear.weight
        primals_2 = self.bilinear.bias
        primals_5 = self.linear.weight
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
