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 *
import torch.onnx
import torch as torch
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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 8
    x0 = xindex % 2
    x1 = xindex // 2 % 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 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (2 + x0 + 4 * x1 + 16 * (-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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp11 = tl.load(in_out_ptr0 + x2, xmask)
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr0 + (32 + 2 * x1 + (-2 + 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 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, 4), (4, 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, 4), (4, 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, 2), (8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(64)](buf0, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (8, 4, 2), (8, 2, 1), 0)
        del buf0
        triton_poi_fused_stack_0[grid(64)](buf1, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((8, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf4, (8, 2, 4), (8, 1,
            2), 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, 2), (8, 2, 1), 0)
        del buf1
        triton_poi_fused_stack_0[grid(64)](buf2, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (8, 4, 2), (8, 2, 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, 4), (4, 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, 4), (16, 4, 1), 0)
        del buf10
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(64)](buf11, buf9,
            buf12, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf9
    return buf11, buf7, buf7, buf12, reinterpret_tensor(primals_1, (4, 16),
        (1, 4), 0), reinterpret_tensor(buf8, (8, 2, 4), (8, 1, 2), 0
        ), reinterpret_tensor(buf3, (8, 2, 4), (8, 1, 2), 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,embedding_size)``.
      Arguments
            - **in_features** : Positive integer, dimensionality of input features.
            - **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, embedding_size, head_num=2, use_res=True, scaling=
        False, seed=1024, device='cpu'):
        super(InteractingLayerNew, self).__init__()
        if head_num <= 0:
            raise ValueError('head_num must be a int > 0')
        if embedding_size % head_num != 0:
            raise ValueError(
                'embedding_size is not an integer multiple of head_num!')
        self.att_embedding_size = embedding_size // head_num
        self.head_num = head_num
        self.use_res = use_res
        self.scaling = scaling
        self.seed = seed
        self.W_Query = nn.Parameter(torch.Tensor(embedding_size,
            embedding_size))
        self.W_key = nn.Parameter(torch.Tensor(embedding_size, embedding_size))
        self.W_Value = nn.Parameter(torch.Tensor(embedding_size,
            embedding_size))
        if self.use_res:
            self.W_Res = nn.Parameter(torch.Tensor(embedding_size,
                embedding_size))
        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]


Measured latency_ms: 0.110334