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
import torch.cuda
import torch.distributed
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_mul_ones_tril_0(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + -1 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = 1 + x1
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp3 / tmp7
    tmp9 = tmp5 * tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tmp11 = tmp9 * tmp10
    tmp12 = tmp5 + tmp11
    tmp13 = 1.0
    tmp14 = tmp13 - tmp9
    tmp15 = tmp9 * tmp14
    tmp16 = tmp13 - tmp3
    tmp17 = tmp3 * tmp16
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr2 + x2, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, 8), (8, 1))
    assert_size_stride(primals_3, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_ones_tril_0[grid(16)](buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (0, 4, 1), 0
            ), primals_1, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(128)](primals_1, buf1, buf2, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_2, (8, 8), (1, 8), 0), out=buf3)
        del primals_2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2[grid(64)](buf3,
            primals_3, primals_1, buf1, buf4, buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf3
        del primals_3
    return buf4, buf1, primals_1, buf1, reinterpret_tensor(buf2, (16, 8), (
        8, 1), 0), buf5, buf6


class PositionwiseFeedForward(nn.Module):
    """ A two-layer Feed-Forward-Network with residual layer norm.

    Args:
        d_model (int): the size of input for the first-layer of the FFN.
        d_ff (int): the hidden layer size of the second-layer
            of the FNN.
        dropout (float): dropout probability in :math:`[0, 1)`.
    """

    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
        self.dropout_1 = nn.Dropout(dropout)
        self.relu = nn.ReLU()
        self.dropout_2 = nn.Dropout(dropout)

    def forward(self, x):
        """Layer definition.

        Args:
            x: ``(batch_size, input_len, model_dim)``

        Returns:
            (FloatTensor): Output ``(batch_size, input_len, model_dim)``.
        """
        inter = self.dropout_1(self.relu(self.w_1(self.layer_norm(x))))
        output = self.dropout_2(self.w_2(inter))
        return output + x

    def update_dropout(self, dropout):
        self.dropout_1.p = dropout
        self.dropout_2.p = dropout


class AverageAttentionNew(nn.Module):
    """
    Average Attention module from
    "Accelerating Neural Transformer via an Average Attention Network"
    :cite:`DBLP:journals/corr/abs-1805-00631`.

    Args:
       model_dim (int): the dimension of keys/values/queries,
           must be divisible by head_count
       dropout (float): dropout parameter
    """

    def __init__(self, model_dim, dropout=0.1, aan_useffn=False):
        self.model_dim = model_dim
        self.aan_useffn = aan_useffn
        super(AverageAttentionNew, self).__init__()
        if aan_useffn:
            self.average_layer = PositionwiseFeedForward(model_dim,
                model_dim, dropout)
        self.gating_layer = nn.Linear(model_dim * 2, model_dim * 2)

    def cumulative_average_mask(self, batch_size, inputs_len, device):
        """
        Builds the mask to compute the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Figure 3

        Args:
            batch_size (int): batch size
            inputs_len (int): length of the inputs

        Returns:
            (FloatTensor):

            * A Tensor of shape ``(batch_size, input_len, input_len)``
        """
        triangle = torch.tril(torch.ones(inputs_len, inputs_len, dtype=
            torch.float, device=device))
        weights = torch.ones(1, inputs_len, dtype=torch.float, device=device
            ) / torch.arange(1, inputs_len + 1, dtype=torch.float, device=
            device)
        mask = triangle * weights.transpose(0, 1)
        return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)

    def cumulative_average(self, inputs, mask_or_step, layer_cache=None,
        step=None):
        """
        Computes the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Equations (1) (5) (6)

        Args:
            inputs (FloatTensor): sequence to average
                ``(batch_size, input_len, dimension)``
            mask_or_step: if cache is set, this is assumed
                to be the current step of the
                dynamic decoding. Otherwise, it is the mask matrix
                used to compute the cumulative average.
            layer_cache: a dictionary containing the cumulative average
                of the previous step.

        Returns:
            a tensor of the same shape and type as ``inputs``.
        """
        if layer_cache is not None:
            step = mask_or_step
            average_attention = (inputs + step * layer_cache['prev_g']) / (step
                 + 1)
            layer_cache['prev_g'] = average_attention
            return average_attention
        else:
            mask = mask_or_step
            return torch.matmul(mask, inputs)

    def forward(self, input_0):
        primals_2 = self.gating_layer.weight
        primals_3 = self.gating_layer.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]


Measured latency_ms: 0.061837