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 import 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__unsafe_view_clone_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (16 * x1 + 64 * (y0 // 16) + y0 % 16), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(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
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x4 = xindex // 256
    x5 = xindex // 16 % 16
    x2 = xindex // 16 % 4
    x6 = xindex // 4 % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x5 + 16 * x0 + 64 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x6 + 16 * x0 + 64 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x7, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_squeeze_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x6 = xindex % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x6 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(out_ptr0 + x7, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0[grid(64, 4)](primals_1, buf0,
            64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_2, (4, 1), (1, 1
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_1, buf2, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_2[grid(256)](buf1, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        buf4 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_3[grid(1024)](buf4, primals_3, primals_1,
            buf5, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (256, 1), (1, 1), 0)
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf5, (256, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 1), 4), out=buf6)
        buf7 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf6, (64, 4, 1), (4, 1, 1), 0), out=buf7)
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_squeeze_4[grid(1024)](buf7, primals_3, buf4,
            primals_1, buf8, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del buf7
        del primals_1
        del primals_3
    return buf8, reinterpret_tensor(buf2, (64, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf5, (4, 256), (1, 4), 0), reinterpret_tensor(
        primals_2, (1, 4), (1, 1), 4), reinterpret_tensor(buf0, (4, 64), (1,
        4), 0)


class CrossNetNew(nn.Module):
    """The Cross Network part of Deep&Cross Network model,
    which leans both low and high degree cross feature.
      Input shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Output shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Arguments
        - **in_features** : Positive integer, dimensionality of input features.
        - **layer_num**: Positive integer, the cross layer number
        - **parameterization**: string, ``"vector"``  or ``"matrix"`` ,  way to parameterize the cross network.
        - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix
        - **seed**: A Python integer to use as random seed.
      References
        - [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123)
        - [Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020.](https://arxiv.org/abs/2008.13535)
    """

    def __init__(self, in_features, layer_num=2, parameterization='vector',
        seed=1024, device='cpu'):
        super(CrossNetNew, self).__init__()
        self.layer_num = layer_num
        self.parameterization = parameterization
        if self.parameterization == 'vector':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, 1))
        elif self.parameterization == 'matrix':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, in_features))
        else:
            raise ValueError("parameterization should be 'vector' or 'matrix'")
        self.bias = nn.Parameter(torch.Tensor(self.layer_num, in_features, 1))
        for i in range(self.kernels.shape[0]):
            nn.init.xavier_normal_(self.kernels[i])
        for i in range(self.bias.shape[0]):
            nn.init.zeros_(self.bias[i])
        self

    def forward(self, input_0):
        primals_2 = self.kernels
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.015407