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
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 = 32
    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_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + x2, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (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,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (1, 8), (8, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_6, reinterpret_tensor(
            primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](buf0, buf1, buf2, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf2, reinterpret_tensor(primals_7,
            (8, 1), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_1[grid(16)](buf4, buf0, buf1,
            buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf5, primals_3, primals_6, buf0, buf1, buf2, buf4, primals_7


class MergeLayerNew(nn.Module):

    def __init__(self, h_size, device='cpu'):
        super(MergeLayerNew, self).__init__()
        self.weight_inbound = nn.Linear(h_size, h_size, bias=True)
        self.weight_outbound = nn.Linear(h_size, h_size, bias=True)
        self.lambda_layer = nn.Linear(h_size * 2, 1, bias=True)
        self.init_params()
        self

    def init_params(self):
        nn.init.xavier_normal_(self.weight_inbound.weight, gain=1.414)
        nn.init.xavier_normal_(self.weight_outbound.weight, gain=1.414)
        nn.init.zeros_(self.weight_inbound.bias)
        nn.init.zeros_(self.weight_outbound.bias)

    def forward(self, input_0, input_1):
        primals_1 = self.weight_inbound.weight
        primals_2 = self.weight_inbound.bias
        primals_3 = self.weight_outbound.weight
        primals_5 = self.weight_outbound.bias
        primals_7 = self.lambda_layer.weight
        primals_8 = self.lambda_layer.bias
        primals_4 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.022911