import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm


class MultiheadAttention(nn.Module):
    """Allows the model to jointly attend to information
    from different representation subspaces.
    See reference: Attention Is All You Need

    .. math::
        	ext{MultiHead}(Q, K, V) = 	ext{Concat}(head_1,\\dots,head_h)W^O
        	ext{where} head_i = 	ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)

    Args:
        embed_dim: total dimension of the model
        num_heads: parallel attention layers, or heads

    """

    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
        self._reset_parameters()
        self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))

    def _reset_parameters(self):
        xavier_uniform_(self.out_proj.weight)

    def forward(self, query, key, value, key_padding_mask=None,
        incremental_state=None, attn_mask=None):
        """
        Inputs of forward function
            query: [target length, batch size, embed dim]
            key: [sequence length, batch size, embed dim]
            value: [sequence length, batch size, embed dim]
            key_padding_mask: if True, mask padding based on batch size
            incremental_state: if provided, previous time steps are cashed
            need_weights: output attn_output_weights
            static_kv: key and value are static

        Outputs of forward function
            attn_output: [target length, batch size, embed dim]
            attn_output_weights: [batch size, target length, sequence length]
        """
        q_shape = query.shape
        src_shape = key.shape
        q = self._in_proj_q(query)
        k = self._in_proj_k(key)
        v = self._in_proj_v(value)
        q *= self.scaling
        q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
            head_dim))
        q = q.permute(1, 2, 0, 3)
        k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        k = k.permute(1, 2, 0, 3)
        v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        v = v.permute(1, 2, 0, 3)
        if key_padding_mask is not None:
            assert key_padding_mask.shape[0] == q_shape[1]
            assert key_padding_mask.shape[1] == src_shape[0]
        attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
        if attn_mask is not None:
            attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
            attn_output_weights += attn_mask
        if key_padding_mask is not None:
            attn_output_weights = torch.reshape(attn_output_weights, [
                q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
            key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
            key = key.type(torch.float32)
            y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
                torch.float32)
            y = torch.where(key == 0.0, key, y)
            attn_output_weights += y
        attn_output_weights = F.softmax(attn_output_weights.type(torch.
            float32), dim=-1, dtype=torch.float32 if attn_output_weights.
            dtype == torch.float16 else attn_output_weights.dtype)
        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
            training=self.training)
        attn_output = torch.matmul(attn_output_weights, v)
        attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
            q_shape[0], q_shape[1], self.embed_dim])
        attn_output = self.out_proj(attn_output)
        return attn_output

    def _in_proj_q(self, query):
        query = query.permute(1, 2, 0)
        query = torch.unsqueeze(query, dim=2)
        res = self.conv1(query)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_k(self, key):
        key = key.permute(1, 2, 0)
        key = torch.unsqueeze(key, dim=2)
        res = self.conv2(key)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_v(self, value):
        value = value.permute(1, 2, 0)
        value = torch.unsqueeze(value, dim=2)
        res = self.conv3(value)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res


class TransformerDecoderLayer(nn.Module):
    """TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
    This standard decoder layer is based on the paper "Attention Is All You Need".
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
    in a different way during application.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).

    """

    def __init__(self, d_model, nhead, dim_feedforward=2048,
        attention_dropout_rate=0.0, residual_dropout_rate=0.1):
        super(TransformerDecoderLayer, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=
            attention_dropout_rate)
        self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=
            attention_dropout_rate)
        self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
            dim_feedforward, kernel_size=(1, 1))
        self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
            d_model, kernel_size=(1, 1))
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.norm3 = LayerNorm(d_model)
        self.dropout1 = Dropout(residual_dropout_rate)
        self.dropout2 = Dropout(residual_dropout_rate)
        self.dropout3 = Dropout(residual_dropout_rate)

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
        tgt_key_padding_mask=None, memory_key_padding_mask=None):
        """Pass the inputs (and mask) through the decoder layer.

        Args:
            tgt: the sequence to the decoder layer (required).
            memory: the sequnce from the last layer of the encoder (required).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the memory sequence (optional).
            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
            memory_key_padding_mask: the mask for the memory keys per batch (optional).

        """
        tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,
            key_padding_mask=tgt_key_padding_mask)
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)
        tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=
            memory_mask, key_padding_mask=memory_key_padding_mask)
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)
        tgt = tgt.permute(1, 2, 0)
        tgt = torch.unsqueeze(tgt, 2)
        tgt2 = self.conv2(F.relu(self.conv1(tgt)))
        tgt2 = torch.squeeze(tgt2, 2)
        tgt2 = tgt2.permute(2, 0, 1)
        tgt = torch.squeeze(tgt, 2)
        tgt = tgt.permute(2, 0, 1)
        tgt = tgt + self.dropout3(tgt2)
        tgt = self.norm3(tgt)
        return tgt


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'d_model': 4, 'nhead': 4}]

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
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
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_convolution_0(in_ptr0, 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 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)
    tl.store(out_ptr1 + x3, tmp0, xmask)
    tl.store(out_ptr2 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, 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
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_3(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x5 = xindex
    y4 = yindex
    x3 = xindex // 4
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x5 + 16 * y4), xmask & ymask)
    tmp1 = tl.load(in_ptr0 + (4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (y0 + 4 * x5 + 64 * y1), tmp8, xmask & ymask)


@triton.jit
def triton_poi_fused_bmm_5(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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 64 * (x0 // 4) + x0 % 4), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    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')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_unsqueeze_9(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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_10(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 + 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_11(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_12(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)


@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_14(in_out_ptr0, in_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
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_15(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_16(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
    x3 = xindex
    x4 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    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 + (x0 + 4 * x2 + 16 * x1), tmp13, xmask)


@triton.jit
def triton_poi_fused_transpose_17(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


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,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27, primals_28
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 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), (16, 4, 1))
    assert_size_stride(primals_13, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (4, 4), (4, 1))
    assert_size_stride(primals_20, (4,), (1,))
    assert_size_stride(primals_21, (4,), (1,))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (2048, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_24, (2048,), (1,))
    assert_size_stride(primals_25, (4, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_26, (4,), (1,))
    assert_size_stride(primals_27, (4,), (1,))
    assert_size_stride(primals_28, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](primals_1, buf0, buf2,
            buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 4), (16, 1, 16, 4))
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 1, 4), (16, 1, 16, 4))
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 1, 4), (16, 1, 16, 4))
        buf6 = reinterpret_tensor(buf4, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf4
        triton_poi_fused_convolution_1[grid(16, 4)](buf3, primals_5, buf6, 
            16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_5
        buf7 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused_mul_2[grid(64)](buf1, primals_3, buf7, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_3
        buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (1, 16, 0),
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0)
        del buf8
        triton_poi_fused__softmax_4[grid(16, 16)](buf9, buf10, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf1, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf1
        triton_poi_fused_convolution_1[grid(16, 4)](buf5, primals_7, buf11,
            16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_7
        buf12 = reinterpret_tensor(buf9, (16, 4, 4), (1, 64, 16), 0)
        del buf9
        triton_poi_fused_bmm_5[grid(256)](buf10, buf12, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf13 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
        del buf5
        extern_kernels.bmm(buf12, reinterpret_tensor(buf11, (16, 4, 1), (4,
            1, 0), 0), out=buf13)
        buf14 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        triton_poi_fused_clone_6[grid(4, 16)](buf13, buf14, 4, 16, XBLOCK=
            16, YBLOCK=4, num_warps=1, num_stages=1)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
        del buf13
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_1, buf15,
            primals_9, buf16, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf18 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_native_layer_norm_8[grid(64)](primals_1, buf15,
            primals_9, buf16, buf17, primals_10, primals_11, buf18, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_11
        buf19 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        triton_poi_fused_unsqueeze_9[grid(64)](buf18, buf19, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf20 = extern_kernels.convolution(buf19, primals_13, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 4, 1, 4), (16, 1, 16, 4))
        buf21 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        triton_poi_fused_unsqueeze_9[grid(64)](primals_12, buf21, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_12
        buf22 = extern_kernels.convolution(buf21, primals_15, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 1, 4), (16, 1, 16, 4))
        buf23 = extern_kernels.convolution(buf21, primals_17, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 4, 1, 4), (16, 1, 16, 4))
        buf24 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 4)](buf22, primals_16,
            buf24, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_16
        buf25 = reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0)
        del buf22
        triton_poi_fused_mul_2[grid(64)](buf20, primals_14, buf25, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_14
        buf26 = reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0)
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 1), (1, 16, 0),
            0), reinterpret_tensor(buf24, (16, 1, 4), (4, 0, 1), 0), out=buf26)
        buf27 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf26, buf27, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf28 = reinterpret_tensor(buf26, (4, 4, 4, 4), (64, 1, 16, 4), 0)
        del buf26
        triton_poi_fused__softmax_4[grid(16, 16)](buf27, buf28, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf29 = reinterpret_tensor(buf20, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf20
        triton_poi_fused_convolution_1[grid(16, 4)](buf23, primals_18,
            buf29, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_18
        buf30 = reinterpret_tensor(buf27, (16, 4, 4), (1, 64, 16), 0)
        del buf27
        triton_poi_fused_bmm_5[grid(256)](buf28, buf30, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf31 = reinterpret_tensor(buf23, (16, 4, 1), (4, 1, 1), 0)
        del buf23
        extern_kernels.bmm(buf30, reinterpret_tensor(buf29, (16, 4, 1), (4,
            1, 0), 0), out=buf31)
        del buf30
        buf32 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_6[grid(4, 16)](buf31, buf32, 4, 16, XBLOCK=
            16, YBLOCK=4, num_warps=1, num_stages=1)
        buf33 = reinterpret_tensor(buf31, (16, 4), (4, 1), 0)
        del buf31
        extern_kernels.mm(reinterpret_tensor(buf32, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_19, (4, 4), (1, 4), 0), out=buf33)
        buf34 = buf18
        del buf18
        triton_poi_fused_add_10[grid(64)](buf34, buf33, primals_20, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_20
        buf35 = buf17
        del buf17
        buf36 = buf16
        del buf16
        triton_poi_fused_native_layer_norm_11[grid(16)](buf34, buf35, buf36,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf37 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0)
        del buf33
        triton_poi_fused_native_layer_norm_12[grid(64)](buf34, buf35, buf36,
            primals_21, primals_22, buf37, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_22
        buf38 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
        triton_poi_fused_unsqueeze_9[grid(64)](buf37, buf38, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf39 = extern_kernels.convolution(buf38, primals_23, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 2048, 1, 4), (8192, 1, 8192, 2048))
        buf40 = buf39
        del buf39
        triton_poi_fused_convolution_relu_13[grid(32768)](buf40, primals_24,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_24
        buf41 = extern_kernels.convolution(buf40, primals_25, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 4, 1, 4), (16, 1, 16, 4))
        buf42 = buf41
        del buf41
        triton_poi_fused_convolution_14[grid(64)](buf42, primals_26, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_26
        buf43 = reinterpret_tensor(buf36, (4, 4, 1), (1, 4, 16), 0)
        del buf36
        buf44 = reinterpret_tensor(buf35, (4, 4, 1), (1, 4, 16), 0)
        del buf35
        triton_poi_fused_add_native_layer_norm_15[grid(16)](buf38, buf42,
            buf43, buf44, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf45 = buf37
        del buf37
        triton_poi_fused_add_native_layer_norm_16[grid(64)](buf38, buf42,
            buf43, buf44, primals_27, primals_28, buf45, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf43
        del buf44
        del primals_28
        buf46 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_transpose_17[grid(16, 4)](buf25, buf46, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf47 = reinterpret_tensor(buf25, (16, 1, 4), (4, 4, 1), 0)
        del buf25
        triton_poi_fused_transpose_17[grid(16, 4)](buf7, buf47, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf7
    return (buf45, primals_1, primals_2, primals_4, primals_6, primals_9,
        primals_10, primals_13, primals_15, primals_17, primals_21,
        primals_23, primals_25, primals_27, buf10, reinterpret_tensor(buf14,
        (16, 4), (4, 1), 0), buf15, buf19, buf21, buf28, reinterpret_tensor
        (buf32, (16, 4), (4, 1), 0), buf34, buf38, buf40, buf42, primals_19,
        reinterpret_tensor(buf29, (16, 1, 4), (4, 4, 1), 0), buf46,
        reinterpret_tensor(buf24, (16, 4, 1), (4, 1, 4), 0), primals_8,
        reinterpret_tensor(buf11, (16, 1, 4), (4, 4, 1), 0), buf47,
        reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0))


class MultiheadAttention(nn.Module):
    """Allows the model to jointly attend to information
    from different representation subspaces.
    See reference: Attention Is All You Need

    .. math::
        	ext{MultiHead}(Q, K, V) = 	ext{Concat}(head_1,\\dots,head_h)W^O
        	ext{where} head_i = 	ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)

    Args:
        embed_dim: total dimension of the model
        num_heads: parallel attention layers, or heads

    """

    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
        self._reset_parameters()
        self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))

    def _reset_parameters(self):
        xavier_uniform_(self.out_proj.weight)

    def forward(self, query, key, value, key_padding_mask=None,
        incremental_state=None, attn_mask=None):
        """
        Inputs of forward function
            query: [target length, batch size, embed dim]
            key: [sequence length, batch size, embed dim]
            value: [sequence length, batch size, embed dim]
            key_padding_mask: if True, mask padding based on batch size
            incremental_state: if provided, previous time steps are cashed
            need_weights: output attn_output_weights
            static_kv: key and value are static

        Outputs of forward function
            attn_output: [target length, batch size, embed dim]
            attn_output_weights: [batch size, target length, sequence length]
        """
        q_shape = query.shape
        src_shape = key.shape
        q = self._in_proj_q(query)
        k = self._in_proj_k(key)
        v = self._in_proj_v(value)
        q *= self.scaling
        q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
            head_dim))
        q = q.permute(1, 2, 0, 3)
        k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        k = k.permute(1, 2, 0, 3)
        v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        v = v.permute(1, 2, 0, 3)
        if key_padding_mask is not None:
            assert key_padding_mask.shape[0] == q_shape[1]
            assert key_padding_mask.shape[1] == src_shape[0]
        attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
        if attn_mask is not None:
            attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
            attn_output_weights += attn_mask
        if key_padding_mask is not None:
            attn_output_weights = torch.reshape(attn_output_weights, [
                q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
            key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
            key = key.type(torch.float32)
            y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
                torch.float32)
            y = torch.where(key == 0.0, key, y)
            attn_output_weights += y
        attn_output_weights = F.softmax(attn_output_weights.type(torch.
            float32), dim=-1, dtype=torch.float32 if attn_output_weights.
            dtype == torch.float16 else attn_output_weights.dtype)
        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
            training=self.training)
        attn_output = torch.matmul(attn_output_weights, v)
        attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
            q_shape[0], q_shape[1], self.embed_dim])
        attn_output = self.out_proj(attn_output)
        return attn_output

    def _in_proj_q(self, query):
        query = query.permute(1, 2, 0)
        query = torch.unsqueeze(query, dim=2)
        res = self.conv1(query)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_k(self, key):
        key = key.permute(1, 2, 0)
        key = torch.unsqueeze(key, dim=2)
        res = self.conv2(key)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_v(self, value):
        value = value.permute(1, 2, 0)
        value = torch.unsqueeze(value, dim=2)
        res = self.conv3(value)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res


class TransformerDecoderLayerNew(nn.Module):
    """TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
    This standard decoder layer is based on the paper "Attention Is All You Need".
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
    in a different way during application.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).

    """

    def __init__(self, d_model, nhead, dim_feedforward=2048,
        attention_dropout_rate=0.0, residual_dropout_rate=0.1):
        super(TransformerDecoderLayerNew, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=
            attention_dropout_rate)
        self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=
            attention_dropout_rate)
        self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
            dim_feedforward, kernel_size=(1, 1))
        self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
            d_model, kernel_size=(1, 1))
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.norm3 = LayerNorm(d_model)
        self.dropout1 = Dropout(residual_dropout_rate)
        self.dropout2 = Dropout(residual_dropout_rate)
        self.dropout3 = Dropout(residual_dropout_rate)

    def forward(self, input_0, input_1):
        primals_8 = self.self_attn.out_proj.weight
        primals_3 = self.self_attn.out_proj.bias
        primals_2 = self.self_attn.conv1.weight
        primals_5 = self.self_attn.conv1.bias
        primals_4 = self.self_attn.conv2.weight
        primals_7 = self.self_attn.conv2.bias
        primals_6 = self.self_attn.conv3.weight
        primals_9 = self.self_attn.conv3.bias
        primals_19 = self.multihead_attn.out_proj.weight
        primals_10 = self.multihead_attn.out_proj.bias
        primals_13 = self.multihead_attn.conv1.weight
        primals_11 = self.multihead_attn.conv1.bias
        primals_15 = self.multihead_attn.conv2.weight
        primals_14 = self.multihead_attn.conv2.bias
        primals_17 = self.multihead_attn.conv3.weight
        primals_16 = self.multihead_attn.conv3.bias
        primals_23 = self.conv1.weight
        primals_24 = self.conv1.bias
        primals_25 = self.conv2.weight
        primals_18 = self.conv2.bias
        primals_20 = self.norm1.weight
        primals_21 = self.norm1.bias
        primals_22 = self.norm2.weight
        primals_26 = self.norm2.bias
        primals_27 = self.norm3.weight
        primals_28 = self.norm3.bias
        primals_1 = input_0
        primals_12 = input_1
        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, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28])
        return output[0]
