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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing import Tuple
from torch import nn
from typing import Optional
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_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex % 16
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x5, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_5(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_6(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + 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, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_1, 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, 1), torch.float32)
        extern_kernels.addmm(primals_7, primals_1, reinterpret_tensor(
            primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf4
        triton_poi_fused__softmax_1[grid(16)](buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 1, 1), (1, 1, 1), 
            0), reinterpret_tensor(buf2, (16, 1, 1), (1, 1, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf6, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf7)
        del primals_9
        buf8 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_2[grid(16)](buf7, primals_1,
            buf8, buf9, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_3[grid(64)](buf7, primals_1,
            buf8, buf9, primals_10, primals_11, buf10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_11
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf11)
        buf12 = reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0)
        del buf11
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(64)](buf12,
            primals_13, buf18, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
        buf13 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 4), (16, 4, 1), 0)
        del buf13
        triton_poi_fused_add_5[grid(64)](buf14, primals_15, buf10, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_15
        buf15 = buf9
        del buf9
        buf16 = buf8
        del buf8
        triton_poi_fused_native_layer_norm_6[grid(16)](buf14, buf15, buf16,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_7[grid(64)](buf14, buf15, buf16,
            primals_16, primals_17, buf17, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf15
        del buf16
        del primals_17
    return (buf17, buf5, primals_1, primals_10, primals_16, buf5,
        reinterpret_tensor(buf6, (4, 4), (4, 1), 0), buf7,
        reinterpret_tensor(buf10, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf12, (16, 4), (4, 1), 0), buf14, primals_14, buf18, primals_12,
        primals_8, reinterpret_tensor(buf2, (16, 1, 1), (1, 1, 4), 0),
        reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 4), 0),
        reinterpret_tensor(buf1, (16, 1, 1), (1, 4, 1), 0))


class ScaledDotProductAttention(nn.Module):
    """
    Scaled Dot-Product Attention

    Parameters
    ----------
    scale : float
        Scale factor (sqrt(d_k))

    dropout : float
        Dropout
    """

    def __init__(self, scale: 'float', dropout: 'float'=0.5) ->None:
        super(ScaledDotProductAttention, self).__init__()
        self.scale = scale
        self.dropout = nn.Dropout(dropout)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, Q: 'torch.Tensor', K: 'torch.Tensor', V:
        'torch.Tensor', mask: 'Optional[torch.Tensor]'=None):
        """
        Parameters
        ----------
        Q : torch.Tensor (batch_size, n_heads, word_pad_len, d_k)
            Query

        K : torch.Tensor
            Key

        V : torch.Tensor
            Value

        mask : torch.Tensor (batch_size, 1, 1, word_pad_len)
            Padding mask metrix, None if it is not needed

        Returns
        -------
        context : torch.Tensor (batch_size, n_heads, word_pad_len, d_k)
            Context vector

        att : torch.Tensor (batch_size, n_heads, word_pad_len, word_pad_len)
            Attention weights
        """
        att = torch.matmul(Q / self.scale, K.transpose(2, 3))
        if mask is not None:
            att = att.masked_fill(mask == 0, -1000000000.0)
        att = self.dropout(self.softmax(att))
        context = torch.matmul(att, V)
        return context, att


class MultiHeadAttention(nn.Module):
    """
    Multi-Head Self-Attention

    Parameters
    ----------
    d_model : int
        Size of word embeddings

    n_heads : int
        Number of attention heads

    dropout : float
        Dropout
    """

    def __init__(self, d_model: 'int', n_heads: 'int', dropout: 'float'=0.5
        ) ->None:
        super(MultiHeadAttention, self).__init__()
        assert d_model % n_heads == 0
        self.d_k = d_model // n_heads
        self.n_heads = n_heads
        self.W_Q = nn.Linear(d_model, n_heads * self.d_k)
        self.W_K = nn.Linear(d_model, n_heads * self.d_k)
        self.W_V = nn.Linear(d_model, n_heads * self.d_k)
        scale = self.d_k ** 0.5
        self.attention = ScaledDotProductAttention(scale=scale)
        self.layer_norm = nn.LayerNorm(d_model)
        self.fc = nn.Linear(n_heads * self.d_k, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x: 'torch.Tensor', mask: 'torch.Tensor') ->Tuple[
        torch.Tensor, torch.Tensor]:
        """
        Parameters
        ----------
        x : torch.Tensor (batch_size, word_pad_len, d_model)
            Input data

        mask : torch.Tensor (batch_size, 1, word_pad_len)
            Padding mask metrix, None if it is not needed

        Returns
        -------
        out : torch.Tensor (batch_size, word_pad_len, d_model)
            Output of multi-head self-attention network

        att: torch.Tensor (batch_size, n_heads, word_pad_len, word_pad_len)
            Attention weights
        """
        batch_size = x.size(0)
        Q = self.W_Q(x)
        K = self.W_K(x)
        V = self.W_V(x)
        Q = Q.view(batch_size, -1, self.n_heads, self.d_k)
        K = K.view(batch_size, -1, self.n_heads, self.d_k)
        V = V.view(batch_size, -1, self.n_heads, self.d_k)
        Q, K, V = Q.transpose(1, 2), K.transpose(1, 2), V.transpose(1, 2)
        if mask is not None:
            mask = mask.unsqueeze(1)
        context, att = self.attention(Q, K, V, mask=mask)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1,
            self.d_k * self.n_heads)
        out = self.dropout(self.fc(context))
        out = out + x
        out = self.layer_norm(out)
        return out, att


class PositionWiseFeedForward(nn.Module):
    """
    Position-Wise Feed-Forward Network

    Parameters
    ----------
    d_model : int
        Size of word embeddings

    hidden_size : int
        Size of position-wise feed forward network

    dropout : float
        Dropout
    """

    def __init__(self, d_model: 'int', hidden_size: 'int', dropout: 'float'=0.5
        ) ->None:
        super(PositionWiseFeedForward, self).__init__()
        self.W_1 = nn.Linear(d_model, hidden_size)
        self.W_2 = nn.Linear(hidden_size, d_model)
        self.layer_norm = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.relu = nn.ReLU()

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """
        Parameters
        ----------
        x : torch.Tensor (batch_size, word_pad_len, d_model)
            Output of multi-head self-attention network

        Returns
        -------
        out : torch.Tensor (batch_size, word_pad_len, d_model)
            Output of position-wise feed-forward network
        """
        out = self.W_2(self.relu(self.W_1(x)))
        out = self.dropout(out)
        out += x
        out = self.layer_norm(out)
        return out


class EncoderLayerNew(nn.Module):
    """
    An encoder layer.

    Parameters
    ----------
    d_model : int
        Size of word embeddings

    n_heads : int
        Number of attention heads

    hidden_size : int
        Size of position-wise feed forward network

    dropout : float
        Dropout
    """

    def __init__(self, d_model: 'int', n_heads: 'int', hidden_size: 'int',
        dropout: 'float'=0.5) ->None:
        super(EncoderLayerNew, self).__init__()
        self.attention = MultiHeadAttention(d_model, n_heads, dropout)
        self.feed_forward = PositionWiseFeedForward(d_model, hidden_size,
            dropout)

    def forward(self, input_0):
        primals_1 = self.attention.W_Q.weight
        primals_3 = self.attention.W_Q.bias
        primals_2 = self.attention.W_K.weight
        primals_5 = self.attention.W_K.bias
        primals_4 = self.attention.W_V.weight
        primals_7 = self.attention.W_V.bias
        primals_9 = self.attention.layer_norm.weight
        primals_10 = self.attention.layer_norm.bias
        primals_6 = self.attention.fc.weight
        primals_11 = self.attention.fc.bias
        primals_8 = self.feed_forward.W_1.weight
        primals_13 = self.feed_forward.W_1.bias
        primals_12 = self.feed_forward.W_2.weight
        primals_15 = self.feed_forward.W_2.bias
        primals_16 = self.feed_forward.layer_norm.weight
        primals_17 = self.feed_forward.layer_norm.bias
        primals_14 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0], output[1]


Measured latency_ms: 0.052109