# 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/vb/cvbie2qzppig2lfiaox6fsifr4m3szei3bwub2h4jhezugadmtam.py
# Topologically Sorted Source Nodes: [querys_1], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   querys_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %getitem_1],), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[256], 
    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_stack_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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 32)
    x0 = xindex % 8
    x1 = (xindex // 8) % 4
    x3 = xindex
    tmp0 = x2
    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 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (8 + x0 + (16*x1) + (64*((-4) + x2))), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/d7/cd7xa5d4yg5y7exr6s4sr25rd6okj4v7452l7cyhxnqr3mcd4qhj.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_15, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_15, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), 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=[128], 
    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_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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kx/ckxzcazhsdasvh5sdcvshdrxriufwxfrn25tt7nuks5deb2u6ei5.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => 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=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=[128], 
    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 = 128
    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')


# kernel path: runs/run_shard_7/inductor_cache/rg/crg347qwcf7vy2xko33empdwam2lpkatpcrrqyfrcymucxldnijc.py
# Topologically Sorted Source Nodes: [result_4], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   result_4 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%squeeze_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_relu_threshold_backward_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp11 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((8*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 16, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (128 + (8*x1) + ((-8) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full([1], 0, tl.int32)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp15 = 0.0
    tmp16 = tmp14 <= tmp15
    tl.store(in_out_ptr0 + (x2), tmp14, xmask)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', 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, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [querys], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [keys], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [values], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_4, out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((8, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [querys_1], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, buf3, 256, grid=grid(256), stream=stream0)
        buf4 = reinterpret_tensor(buf0, (8, 4, 8), (32, 8, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [keys_1], Original ATen: [aten.stack]
        triton_poi_fused_stack_0.run(buf1, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((8, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [inner_product], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf3, reinterpret_tensor(buf4, (8, 8, 4), (32, 1, 8), 0), out=buf5)
        buf6 = empty_strided_cuda((2, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf5, buf6, 128, grid=grid(128), stream=stream0)
        buf7 = reinterpret_tensor(buf5, (2, 4, 4, 4), (64, 16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf6, buf7, 128, grid=grid(128), stream=stream0)
        del buf6
        buf8 = reinterpret_tensor(buf1, (8, 4, 8), (32, 8, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [values_1], Original ATen: [aten.stack]
        triton_poi_fused_stack_0.run(buf2, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = reinterpret_tensor(buf2, (8, 4, 8), (32, 8, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (8, 4, 4), (16, 4, 1), 0), buf8, out=buf9)
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tensordot_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_5, out=buf10)
        del primals_5
        buf11 = reinterpret_tensor(buf10, (4, 4, 16), (64, 16, 1), 0); del buf10  # reuse
        buf12 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [result_4], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf11, buf9, buf12, 256, grid=grid(256), stream=stream0)
        del buf9
    return (buf11, buf7, buf7, buf12, reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), reinterpret_tensor(buf8, (8, 8, 4), (32, 1, 8), 0), reinterpret_tensor(buf3, (8, 8, 4), (32, 1, 8), 0), buf4, )


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, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 16), (16, 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
import torch.nn.functional as F
from sklearn.metrics import *


class InteractingLayer(nn.Module):
    """A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
      Input shape
            - A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
      Output shape
            - 3D tensor with shape:``(batch_size,field_size,att_embedding_size * head_num)``.
      Arguments
            - **in_features** : Positive integer, dimensionality of input features.
            - **att_embedding_size**: int.The embedding size in multi-head self-attention network.
            - **head_num**: int.The head number in multi-head  self-attention network.
            - **use_res**: bool.Whether or not use standard residual connections before output.
            - **seed**: A Python integer to use as random seed.
      References
            - [Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.](https://arxiv.org/abs/1810.11921)
    """

    def __init__(self, in_features, att_embedding_size=8, head_num=2,
        use_res=True, seed=1024, device='cpu'):
        super(InteractingLayer, self).__init__()
        if head_num <= 0:
            raise ValueError('head_num must be a int > 0')
        self.att_embedding_size = att_embedding_size
        self.head_num = head_num
        self.use_res = use_res
        self.seed = seed
        embedding_size = in_features
        self.W_Query = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        self.W_key = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        self.W_Value = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        if self.use_res:
            self.W_Res = nn.Parameter(torch.Tensor(embedding_size, self.
                att_embedding_size * self.head_num))
        for tensor in self.parameters():
            nn.init.normal_(tensor, mean=0.0, std=0.05)
        self

    def forward(self, inputs):
        if len(inputs.shape) != 3:
            raise ValueError(
                'Unexpected inputs dimensions %d, expect to be 3 dimensions' %
                len(inputs.shape))
        querys = torch.tensordot(inputs, self.W_Query, dims=([-1], [0]))
        keys = torch.tensordot(inputs, self.W_key, dims=([-1], [0]))
        values = torch.tensordot(inputs, self.W_Value, dims=([-1], [0]))
        querys = torch.stack(torch.split(querys, self.att_embedding_size,
            dim=2))
        keys = torch.stack(torch.split(keys, self.att_embedding_size, dim=2))
        values = torch.stack(torch.split(values, self.att_embedding_size,
            dim=2))
        inner_product = torch.einsum('bnik,bnjk->bnij', querys, keys)
        self.normalized_att_scores = F.softmax(inner_product, dim=-1)
        result = torch.matmul(self.normalized_att_scores, values)
        result = torch.cat(torch.split(result, 1), dim=-1)
        result = torch.squeeze(result, dim=0)
        if self.use_res:
            result += torch.tensordot(inputs, self.W_Res, dims=([-1], [0]))
        result = F.relu(result)
        return result


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


def get_init_inputs():
    return [[], {'in_features': 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
from sklearn.metrics import *
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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 32
    x0 = xindex % 8
    x1 = xindex // 8 % 4
    x3 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + 16 * x1 + 64 * (-4 + x2)), tmp6 &
        xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    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)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp11 = tl.load(in_out_ptr0 + x2, xmask)
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (8 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 16, tl.int64)
    tmp9 = tl.load(in_ptr0 + (128 + 8 * x1 + (-8 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full([1], 0, tl.int32)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp15 = 0.0
    tmp16 = tmp14 <= tmp15
    tl.store(in_out_ptr0 + x2, tmp14, xmask)
    tl.store(out_ptr0 + x2, tmp16, xmask)


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, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_4, out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((8, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(256)](buf0, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (8, 4, 8), (32, 8, 1), 0)
        del buf0
        triton_poi_fused_stack_0[grid(256)](buf1, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((8, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf4, (8, 8, 4), (32, 1,
            8), 0), out=buf5)
        buf6 = empty_strided_cuda((2, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(128)](buf5, buf6, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (2, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_2[grid(128)](buf6, buf7, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf6
        buf8 = reinterpret_tensor(buf1, (8, 4, 8), (32, 8, 1), 0)
        del buf1
        triton_poi_fused_stack_0[grid(256)](buf2, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (8, 4, 8), (32, 8, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf7, (8, 4, 4), (16, 4, 1), 
            0), buf8, out=buf9)
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_5, out=buf10)
        del primals_5
        buf11 = reinterpret_tensor(buf10, (4, 4, 16), (64, 16, 1), 0)
        del buf10
        buf12 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(256)](buf11, buf9,
            buf12, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
    return buf11, buf7, buf7, buf12, reinterpret_tensor(primals_1, (4, 16),
        (1, 4), 0), reinterpret_tensor(buf8, (8, 8, 4), (32, 1, 8), 0
        ), reinterpret_tensor(buf3, (8, 8, 4), (32, 1, 8), 0), buf4


class InteractingLayerNew(nn.Module):
    """A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
      Input shape
            - A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
      Output shape
            - 3D tensor with shape:``(batch_size,field_size,att_embedding_size * head_num)``.
      Arguments
            - **in_features** : Positive integer, dimensionality of input features.
            - **att_embedding_size**: int.The embedding size in multi-head self-attention network.
            - **head_num**: int.The head number in multi-head  self-attention network.
            - **use_res**: bool.Whether or not use standard residual connections before output.
            - **seed**: A Python integer to use as random seed.
      References
            - [Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.](https://arxiv.org/abs/1810.11921)
    """

    def __init__(self, in_features, att_embedding_size=8, head_num=2,
        use_res=True, seed=1024, device='cpu'):
        super(InteractingLayerNew, self).__init__()
        if head_num <= 0:
            raise ValueError('head_num must be a int > 0')
        self.att_embedding_size = att_embedding_size
        self.head_num = head_num
        self.use_res = use_res
        self.seed = seed
        embedding_size = in_features
        self.W_Query = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        self.W_key = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        self.W_Value = nn.Parameter(torch.Tensor(embedding_size, self.
            att_embedding_size * self.head_num))
        if self.use_res:
            self.W_Res = nn.Parameter(torch.Tensor(embedding_size, self.
                att_embedding_size * self.head_num))
        for tensor in self.parameters():
            nn.init.normal_(tensor, mean=0.0, std=0.05)
        self

    def forward(self, input_0):
        primals_2 = self.W_Query
        primals_3 = self.W_key
        primals_4 = self.W_Value
        primals_5 = self.W_Res
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
