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.triton_helpers import libdevice
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    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 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp7 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tmp1 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp1
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 + tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf0, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_1[grid(64)](buf2, buf1,
            primals_1, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf3, primals_1, reinterpret_tensor(buf0, (16, 8), (8, 1), 0
        ), buf1, buf2


class SFUNew(nn.Module):
    """Semantic Fusion Unit
    The ouput vector is expected to not only retrieve correlative information from fusion vectors,
    but also retain partly unchange as the input vector
    """

    def __init__(self, input_size, fusion_size):
        super(SFUNew, self).__init__()
        self.linear_r = nn.Linear(input_size + fusion_size, input_size)
        self.linear_g = nn.Linear(input_size + fusion_size, input_size)

    def forward(self, input_0, input_1):
        primals_3 = self.linear_r.weight
        primals_4 = self.linear_r.bias
        primals_5 = self.linear_g.weight
        primals_6 = self.linear_g.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.021046