import torch
import torch.utils.data


class FactorizationMachine(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

    def forward(self, x):
        """
        :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
        """
        square_of_sum = torch.sum(x, dim=1) ** 2
        sum_of_square = torch.sum(x ** 2, dim=1)
        ix = square_of_sum - sum_of_square
        if self.reduce_sum:
            ix = torch.sum(ix, dim=1, keepdim=True)
        return 0.5 * ix


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


def get_init_inputs():
    return [[], {}]

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
import torch.utils.data
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_pow_sub_sum_0(in_out_ptr0, in_ptr0, 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 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp33 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp34 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp36 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp38 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp50 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp53 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp55 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp0 * tmp0
    tmp9 = tmp1 * tmp1
    tmp10 = tmp8 + tmp9
    tmp11 = tmp3 * tmp3
    tmp12 = tmp10 + tmp11
    tmp13 = tmp5 * tmp5
    tmp14 = tmp12 + tmp13
    tmp15 = tmp7 - tmp14
    tmp18 = tmp16 + tmp17
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 * tmp16
    tmp25 = tmp17 * tmp17
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 * tmp19
    tmp28 = tmp26 + tmp27
    tmp29 = tmp21 * tmp21
    tmp30 = tmp28 + tmp29
    tmp31 = tmp23 - tmp30
    tmp32 = tmp15 + tmp31
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 + tmp36
    tmp39 = tmp37 + tmp38
    tmp40 = tmp39 * tmp39
    tmp41 = tmp33 * tmp33
    tmp42 = tmp34 * tmp34
    tmp43 = tmp41 + tmp42
    tmp44 = tmp36 * tmp36
    tmp45 = tmp43 + tmp44
    tmp46 = tmp38 * tmp38
    tmp47 = tmp45 + tmp46
    tmp48 = tmp40 - tmp47
    tmp49 = tmp32 + tmp48
    tmp52 = tmp50 + tmp51
    tmp54 = tmp52 + tmp53
    tmp56 = tmp54 + tmp55
    tmp57 = tmp56 * tmp56
    tmp58 = tmp50 * tmp50
    tmp59 = tmp51 * tmp51
    tmp60 = tmp58 + tmp59
    tmp61 = tmp53 * tmp53
    tmp62 = tmp60 + tmp61
    tmp63 = tmp55 * tmp55
    tmp64 = tmp62 + tmp63
    tmp65 = tmp57 - tmp64
    tmp66 = tmp49 + tmp65
    tmp67 = 0.5
    tmp68 = tmp66 * tmp67
    tl.store(in_out_ptr0 + x2, tmp68, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0[grid(16)](buf1, arg0_1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
    return buf1,


class FactorizationMachineNew(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

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