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_gelu_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp9 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + x0, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12,), (1,))
    assert_size_stride(primals_6, (4, 12), (12, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 12), (1, 4), 
            0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 12), (48, 12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_gelu_mul_0[grid(192)](buf0, buf1, buf2, 192,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf2, (16, 12),
            (12, 1), 0), reinterpret_tensor(primals_6, (12, 4), (1, 12), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
    return reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf0, buf1, reinterpret_tensor(buf2, (16, 12), (12, 1), 0
        ), primals_6


class GeGLUNew(torch.nn.Module):

    def __init__(self, config, layer_id, time_shift=False):
        super().__init__()
        self.layer_id = layer_id
        if time_shift:
            self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        hidden_sz = 3 * config.n_ffn
        self.key = nn.Linear(config.n_embd, hidden_sz)
        self.value = nn.Linear(config.n_embd, hidden_sz)
        self.weight = nn.Linear(hidden_sz, config.n_embd)

    def forward(self, input_0):
        primals_2 = self.key.weight
        primals_3 = self.key.bias
        primals_4 = self.value.weight
        primals_5 = self.value.bias
        primals_6 = self.weight.weight
        primals_7 = self.weight.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.013872