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
import torch.utils.data
import torch.utils.data.distributed
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_add_mul_rsub_sigmoid_tanh_0(in_ptr0, in_ptr1, in_ptr2,
    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
    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 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), 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((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_0[grid(256)](buf0, buf1,
            primals_3, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_3, buf0, buf1


def my_xavier_init(m, gain=1):
    for p in m.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform_(p, gain)
        else:
            nn.init.constant_(p, 0)


class HighwayLayerNew(torch.nn.Module):

    def __init__(self, dim):
        super(HighwayLayerNew, self).__init__()
        self.gate_proj = nn.Linear(dim, dim, bias=True)
        self.nlin_proj = nn.Linear(dim, dim, bias=True)
        my_xavier_init(self.nlin_proj)
        my_xavier_init(self.gate_proj)
        nn.init.constant_(self.gate_proj.bias, -1)

    def forward(self, input_0):
        primals_1 = self.gate_proj.weight
        primals_2 = self.gate_proj.bias
        primals_4 = self.nlin_proj.weight
        primals_5 = self.nlin_proj.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.015012