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_add_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 12
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 12), (12, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 12), (12, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (12,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 12), (192, 48, 12, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(768)](buf2, buf1, primals_5, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_5
    return buf2, reinterpret_tensor(primals_4, (4, 64), (1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


class _DynamicGatesNew(nn.Module):
    """Internal class to wrap the dynamic gate parameters into a dedicated PyTorch Module"""

    def __init__(self, cfg: 'Config', input_size: 'int'):
        super(_DynamicGatesNew, self).__init__()
        self.cfg = cfg
        self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 3 * cfg
            .hidden_size))
        self.weight_hh = nn.Parameter(torch.FloatTensor(cfg.hidden_size, 3 *
            cfg.hidden_size))
        self.bias = nn.Parameter(torch.FloatTensor(3 * cfg.hidden_size))
        self._reset_parameters()

    def _reset_parameters(self):
        """Special initialization of certain model weights."""
        nn.init.orthogonal_(self.weight_ih.data)
        weight_hh_data = torch.eye(self.cfg.hidden_size)
        weight_hh_data = weight_hh_data.repeat(1, 3)
        self.weight_hh.data = weight_hh_data
        nn.init.constant_(self.bias.data, val=0)
        if self.cfg.initial_forget_bias is not None:
            self.bias.data[:self.cfg.hidden_size
                ] = self.cfg.initial_forget_bias

    def forward(self, input_0, input_1):
        primals_1 = self.weight_ih
        primals_3 = self.weight_hh
        primals_5 = self.bias
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.022257