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 math
from torch import nn
import torch.utils.checkpoint
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_native_layer_norm_pow_tanh_0(in_ptr0, out_ptr0,
    out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp36 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tmp15 = tmp14 * tmp1
    tmp16 = tmp14 * tmp14
    tmp17 = tmp16 * tmp14
    tmp18 = tmp17 * tmp5
    tmp19 = tmp14 + tmp18
    tmp20 = tmp19 * tmp8
    tmp21 = libdevice.tanh(tmp20)
    tmp22 = tmp21 + tmp11
    tmp23 = tmp15 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 * tmp1
    tmp27 = tmp25 * tmp25
    tmp28 = tmp27 * tmp25
    tmp29 = tmp28 * tmp5
    tmp30 = tmp25 + tmp29
    tmp31 = tmp30 * tmp8
    tmp32 = libdevice.tanh(tmp31)
    tmp33 = tmp32 + tmp11
    tmp34 = tmp26 * tmp33
    tmp35 = tmp24 + tmp34
    tmp37 = tmp36 * tmp1
    tmp38 = tmp36 * tmp36
    tmp39 = tmp38 * tmp36
    tmp40 = tmp39 * tmp5
    tmp41 = tmp36 + tmp40
    tmp42 = tmp41 * tmp8
    tmp43 = libdevice.tanh(tmp42)
    tmp44 = tmp43 + tmp11
    tmp45 = tmp37 * tmp44
    tmp46 = tmp35 + tmp45
    tmp47 = 4.0
    tmp48 = tmp46 / tmp47
    tmp49 = tmp13 - tmp48
    tmp50 = tmp49 * tmp49
    tmp51 = tmp23 - tmp48
    tmp52 = tmp51 * tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp34 - tmp48
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp45 - tmp48
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp59 / tmp47
    tl.store(out_ptr0 + x0, tmp48, xmask)
    tl.store(out_ptr1 + x0, tmp60, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_pow_tanh_1(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp14 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tmp15 = tmp13 - tmp14
    tmp17 = tmp16 + tmp11
    tmp18 = libdevice.rsqrt(tmp17)
    tmp19 = tmp15 * tmp18
    tmp21 = tmp19 * tmp20
    tmp23 = tmp21 + tmp22
    tl.store(out_ptr0 + x2, tmp23, 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, 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,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (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((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_native_layer_norm_pow_tanh_0[grid(64)](buf0,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_pow_tanh_1[grid(256)](buf0,
            buf1, buf2, primals_4, primals_5, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf1
        del buf2
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), primals_6


def gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
        torch.pow(x, 3))))


class IBertLMHeadNew(nn.Module):
    """I-BERT Head for masked language modeling."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size,
            bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias

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


Measured latency_ms: 0.021982