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
import math
from torch import 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_view_0(in_ptr0, 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)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_1(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), 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 * x0), 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 * x0), 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 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_2(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
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, 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 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(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_4(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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_5(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_6(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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 16), (16, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (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, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (16, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (16, 4), (4, 1), 0),
            primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (16, 4), (4, 1), 0),
            primals_3, out=buf2)
        del primals_3
        buf3 = torch.ops.aten._scaled_dot_product_efficient_attention.default(
            reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), None,
            True, scale=0.5)
        buf4 = buf3[0]
        buf5 = buf3[1]
        buf6 = buf3[2]
        buf7 = buf3[3]
        del buf3
        buf8 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_view_0[grid(256)](buf4, buf8, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf8, primals_4, out=buf9)
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_1[grid(16)](primals_5, buf9,
            buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_2[grid(64)](primals_5, buf9,
            buf10, buf11, primals_6, primals_7, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
        buf13 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 4), (16, 4, 1), 0)
        del buf13
        buf20 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(64)](buf14,
            primals_9, buf20, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf15, (4, 4, 4), (16, 4, 1), 0)
        del buf15
        triton_poi_fused_add_4[grid(64)](buf16, buf12, primals_11, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_11
        buf17 = buf11
        del buf11
        buf18 = buf10
        del buf10
        triton_poi_fused_native_layer_norm_5[grid(16)](buf16, buf17, buf18,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(64)](buf16, buf17, buf18,
            primals_12, primals_13, buf19, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf17
        del buf18
        del primals_13
    return buf19, primals_5, primals_6, primals_12, reinterpret_tensor(buf0,
        (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf1, (4, 4, 4,
        4), (64, 4, 16, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64,
        4, 16, 1), 0), buf4, buf5, buf6, buf7, buf9, reinterpret_tensor(buf12,
        (16, 4), (4, 1), 0), reinterpret_tensor(buf14, (16, 4), (4, 1), 0
        ), buf16, primals_10, buf20, primals_8, reinterpret_tensor(buf8, (
        16, 16), (1, 16), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0)


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

    Attributes
    ----------
    softmax : nn.Functional
        softmax function applied at the last dimension
    """

    def __init__(self, dropout=0.1):
        super(ScaledDotProductAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, Q, K, V, mask=None):
        """
        Parameters
        ----------
        Q    : 4d tensor (batch_size, h, seq_len, d_K)
        K    : 4d tensor (batch_size, h, seq_len, d_K)
        V    : 4d tensor (batch_size, h, seq_len, d_V)
        mask : 2d tensor (seq_len, seq_len)
            2d binary tensor, where 1 means connection should be blocked and 
            0 means values can be fed forward to softmax
        
        Returns
        -------
        4d tensor (batch_size, h, seq_len, d_V)
        """
        scaled = torch.matmul(Q, K.transpose_(2, 3))
        scaled = scaled / math.sqrt(K.shape[3])
        if mask is not None:
            scaled.masked_fill_(mask, float('-inf'))
        scaled = self.softmax(scaled)
        scaled = self.dropout(scaled)
        attn = torch.matmul(scaled, V)
        return attn


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

    Attributes
    ----------
    h       : int
        number of parallel heads
    d_K     : int
        dimension of features in both query and key
    d_V     : int
        dimension of features in value
    d_model : int
        dimension of token embedding
    spd_attn: ScaledDotProductattention layer
        sub module to apply scaled dot product
    W_Q     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project query to Q
    W_K     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project key to K
    W_V     : 2d tensor (d_model, d_V * h)
        learned parameters used to linearly project val to V
    W_O     : 2d tensor (d_V * h, d_model)
        learned parameters used to linearly project scaled attention
        to the output tensor with the same dimension as input
    """

    def __init__(self, h, d_model, d_K, d_V, dropout=0.1):
        super(MultiHeadAttention, self).__init__()
        self.h = h
        self.d_K = d_K
        self.d_V = d_V
        self.d_model = d_model
        self.sdp_attn = ScaledDotProductAttention(dropout=dropout)
        self.W_Q = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_K = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_V = nn.Parameter(torch.Tensor(d_model, d_V * h))
        self.W_O = nn.Parameter(torch.Tensor(d_V * h, d_model))
        self.dropout = nn.Dropout(dropout)
        self.reset_parameters()

    def reset_parameters(self):
        slope = math.sqrt(5)
        nn.init.kaiming_uniform_(self.W_Q, a=slope)
        nn.init.kaiming_uniform_(self.W_K, a=slope)
        nn.init.kaiming_uniform_(self.W_V, a=slope)
        nn.init.kaiming_uniform_(self.W_O, a=slope)

    def forward(self, query, key, val, mask=None):
        """
        Parameters
        ----------
        query : 3d tensor (batch_size, seq_len, d_model)
            embedded query sequence
        key   : 3d tensor (batch_size, seq_len, d_model)
            embedded key sequence
        val   : 3d tensor (batch_size, seq_len, d_model)
            embedded value sequence
        mask  : 2d tensor (seq_len, seq_len)
            2d binary tensor, where 1 means pass, 0 means block

        Returns
        -------
        3d tensor (batch_size, seq_len, d_model)
        """
        h, d_K, d_V, _d_model = self.h, self.d_K, self.d_V, self.d_model
        W_Q, W_K, W_V, W_O = self.W_Q, self.W_K, self.W_V, self.W_O
        bs_q, l_q = query.shape[0], query.shape[1]
        bs_k, l_k = key.shape[0], key.shape[1]
        bs_v, l_v = val.shape[0], val.shape[1]
        Q = torch.matmul(query, W_Q)
        K = torch.matmul(key, W_K)
        V = torch.matmul(val, W_V)
        Q = Q.view(bs_q, l_q, h, d_K)
        K = K.view(bs_k, l_k, h, d_K)
        V = V.view(bs_v, l_v, h, d_V)
        Q.transpose_(1, 2)
        K.transpose_(1, 2)
        V.transpose_(1, 2)
        head = self.sdp_attn(Q, K, V, mask)
        head.transpose_(1, 2)
        head = head.reshape(bs_q, l_q, h * d_V)
        res = torch.matmul(head, W_O)
        return self.dropout(res)


class FeedForwardNetwork(nn.Module):
    """
    Position-wise Feed-Forward Network
    Apply the following operation
    f(x) = max(0, xW1 + b1) * W2 + b2

    Attributes
    ----------
    l1 : nn.Linear (in_features=d_model, out_features=d_ff)
    l2 : nn.Linear (in_features=d_ff, out_features=d_model)
    """

    def __init__(self, d_model, d_ff, dropout=0.1):
        """
        Parameters
        ----------
        d_model : int
            embedding length
        d_ff    : int
            size of intermediate activation
        """
        super(FeedForwardNetwork, self).__init__()
        self.l1 = nn.Linear(d_model, d_ff)
        self.l2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.l2(torch.relu(self.l1(x)))
        return self.dropout(x)


class EncoderUnitNew(nn.Module):
    """
    Encoder Unit - build block of encoder

    Attributes
    ----------
    attn  : MultiHeadAttention
    norm1 : LayerNorm
    ffn   : FeedForwardNetwork
    norm2 : LayerNorm
    """

    def __init__(self, h, d_model, d_K, d_V, d_ff, dropout=0.1):
        """
        Parameters
        ----------
        h       : int
            number of attention heads
        d_model : int
            size of embedding
        d_K     : int
            number of features in query and key
        d_V     : int
            number of features in value
        d_ff    : int
            dimension of the feed-forward network
        """
        super(EncoderUnitNew, self).__init__()
        self.attn = MultiHeadAttention(h, d_model, d_K, d_V)
        self.norm1 = nn.LayerNorm(d_model)
        self.ffn = FeedForwardNetwork(d_model, d_ff)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, input_0):
        primals_1 = self.attn.W_Q
        primals_2 = self.attn.W_K
        primals_3 = self.attn.W_V
        primals_4 = self.attn.W_O
        primals_6 = self.norm1.weight
        primals_7 = self.norm1.bias
        primals_8 = self.ffn.l1.weight
        primals_9 = self.ffn.l1.bias
        primals_10 = self.ffn.l2.weight
        primals_11 = self.ffn.l2.bias
        primals_12 = self.norm2.weight
        primals_13 = self.norm2.bias
        primals_5 = 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])
        return output[0]


Measured latency_ms: 0.015048