# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/te/ctez6v53ld4k45ykiwq5ndlpz3jrny2atstdxadog7gcnit65abw.py
# Topologically Sorted Source Nodes: [mul, dsttype_node_relation_attention], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   dsttype_node_relation_attention => sum_1
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %arg1_1), kwargs = {})
#   %sum_1 : [num_users=3] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1], True), kwargs = {})
triton_poi_fused_mul_sum_0 = async_compile.triton('triton_poi_fused_mul_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=[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_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_sum_0(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
    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_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + (x0), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2i/c2ir37xoljdhwkg75h2sbixyvepyluu4socgnd4wiw7oq57nugu4.py
# Topologically Sorted Source Nodes: [leaky_relu, dsttype_node_relation_attention_1], Original ATen: [aten.leaky_relu, aten._softmax]
# Source node to ATen node mapping:
#   dsttype_node_relation_attention_1 => amax, exp, sub
#   leaky_relu => gt, mul_1, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sum_1, %mul_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_leaky_relu_1 = async_compile.triton('triton_poi_fused__softmax_leaky_relu_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: '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_leaky_relu_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__softmax_leaky_relu_1(in_ptr0, 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
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp6 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp11 = tmp10 > tmp1
    tmp12 = tmp10 * tmp3
    tmp13 = tl.where(tmp11, tmp10, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 > tmp1
    tmp17 = tmp15 * tmp3
    tmp18 = tl.where(tmp16, tmp15, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/f5/cf5347orlkm5ylmh4iouw6qvowwebewwg66dnqrhzt7tg7a4irp5.py
# Topologically Sorted Source Nodes: [dsttype_node_relation_attention_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   dsttype_node_relation_attention_1 => div, sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), 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=[64], 
    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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), 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')


# kernel path: runs/run_shard_6/inductor_cache/af/cafnizcuehobeophh3e6mmdetxkitfomc7vxb4m5zc4zqmm42u5i.py
# Topologically Sorted Source Nodes: [dsttype_node_relation_attention_1, mul_1, dsttype_node_features_1], Original ATen: [aten._softmax, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   dsttype_node_features_1 => sum_3
#   dsttype_node_relation_attention_1 => div, sum_2
#   mul_1 => mul_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %div), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [0]), kwargs = {})
triton_poi_fused__softmax_mul_sum_3 = async_compile.triton('triton_poi_fused__softmax_mul_sum_3', '''
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__softmax_mul_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_sum_3(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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, dsttype_node_relation_attention], Original ATen: [aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [leaky_relu, dsttype_node_relation_attention_1], Original ATen: [aten.leaky_relu, aten._softmax]
        triton_poi_fused__softmax_leaky_relu_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [dsttype_node_relation_attention_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [dsttype_node_relation_attention_1, mul_1, dsttype_node_features_1], Original ATen: [aten._softmax, aten.mul, aten.sum]
        triton_poi_fused__softmax_mul_sum_3.run(arg0_1, buf2, buf3, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf2
    return (reinterpret_tensor(buf3, (4, 16), (16, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


class RelationCrossing(nn.Module):

    def __init__(self, in_feats: 'int', out_feats: 'int', num_heads: 'int',
        dropout: 'float'=0.0, negative_slope: 'float'=0.2):
        """

        Description
        ----------
        Relation crossing layer

        Parameters
        ----------
        in_feats : pair of ints
            input feature size
        out_feats : int
            output feature size
        num_heads : int
            number of heads in Multi-Head Attention
        dropout : float
            optional, dropout rate, defaults: 0.0
        negative_slope : float
            optional, negative slope rate, defaults: 0.2
        """
        super(RelationCrossing, self).__init__()
        self._in_feats = in_feats
        self._out_feats = out_feats
        self._num_heads = num_heads
        self.dropout = nn.Dropout(dropout)
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, dsttype_node_features: 'torch.Tensor',
        relations_crossing_attention_weight: 'nn.Parameter'):
        """
        Parameters
        ----------
        dsttype_node_features:
            a tensor of (dsttype_node_relations_num, num_dst_nodes, n_heads * hidden_dim)
        relations_crossing_attention_weight:
            Parameter the shape is (n_heads, hidden_dim)
        Returns:
        ----------
        output_features: Tensor

        """
        if len(dsttype_node_features) == 1:
            dsttype_node_features = dsttype_node_features.squeeze(dim=0)
        else:
            dsttype_node_features = dsttype_node_features.reshape(
                dsttype_node_features.shape[0], -1, self._num_heads, self.
                _out_feats)
            dsttype_node_relation_attention = (dsttype_node_features *
                relations_crossing_attention_weight).sum(dim=-1, keepdim=True)
            dsttype_node_relation_attention = F.softmax(self.leaky_relu(
                dsttype_node_relation_attention), dim=0)
            dsttype_node_features = (dsttype_node_features *
                dsttype_node_relation_attention).sum(dim=0)
            dsttype_node_features = self.dropout(dsttype_node_features)
            dsttype_node_features = dsttype_node_features.reshape(-1, self.
                _num_heads * self._out_feats)
        return dsttype_node_features


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


def get_init_inputs():
    return [[], {'in_feats': 4, 'out_feats': 4, 'num_heads': 4}]

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._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_mul_sum_0(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
    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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_1(in_ptr0, 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
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp11 = tmp10 > tmp1
    tmp12 = tmp10 * tmp3
    tmp13 = tl.where(tmp11, tmp10, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 > tmp1
    tmp17 = tmp15 * tmp3
    tmp18 = tl.where(tmp16, tmp15, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, 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
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_sum_3(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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sum_0[grid(64)](arg0_1, arg1_1, buf0, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_leaky_relu_1[grid(64)](buf0, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = buf0
        del buf0
        triton_poi_fused__softmax_2[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        triton_poi_fused__softmax_mul_sum_3[grid(64)](arg0_1, buf2, buf3, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf2
    return reinterpret_tensor(buf3, (4, 16), (16, 1), 0),


class RelationCrossingNew(nn.Module):

    def __init__(self, in_feats: 'int', out_feats: 'int', num_heads: 'int',
        dropout: 'float'=0.0, negative_slope: 'float'=0.2):
        """

        Description
        ----------
        Relation crossing layer

        Parameters
        ----------
        in_feats : pair of ints
            input feature size
        out_feats : int
            output feature size
        num_heads : int
            number of heads in Multi-Head Attention
        dropout : float
            optional, dropout rate, defaults: 0.0
        negative_slope : float
            optional, negative slope rate, defaults: 0.2
        """
        super(RelationCrossingNew, self).__init__()
        self._in_feats = in_feats
        self._out_feats = out_feats
        self._num_heads = num_heads
        self.dropout = nn.Dropout(dropout)
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
