model.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from mup import MuReadout, MuSharedReadout

'''
The only things we modified from the original pytorch Transformer example are 
1) replace the readout layer with MuReadout or MuSharedReadout, 
2) use fan_in style initialization, 
3) change attention scaling to 1/d instead of 1/sqrt(d), and
4) zero initialization of query weights
'''

def init_method_normal(sigma):
    """Init method based on N(0, sigma)."""
    def init_(tensor):
        return nn.init.normal_(tensor, mean=0.0, std=sigma)
    return init_


# Temporarily leave PositionalEncoding module here. Will be moved somewhere else.
class PositionalEncoding(nn.Module):
    r"""Inject some information about the relative or absolute position of the tokens
        in the sequence. The positional encodings have the same dimension as
        the embeddings, so that the two can be summed. Here, we use sine and cosine
        functions of different frequencies.
    .. math::
        \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
        \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
        \text{where pos is the word position and i is the embed idx)
    Args:
        d_model: the embed dim (required).
        dropout: the dropout value (default=0.1).
        max_len: the max. length of the incoming sequence (default=5000).
    Examples:
        >>> pos_encoder = PositionalEncoding(d_model)
    """

    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        r"""Inputs of forward function
        Args:
            x: the sequence fed to the positional encoder model (required).
        Shape:
            x: [sequence length, batch size, embed dim]
            output: [sequence length, batch size, embed dim]
        Examples:
            >>> output = pos_encoder(x)
        """

        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

class TransformerModel(nn.Module):
    """Container module with an encoder, a recurrent or transformer module, and a decoder."""

    def __init__(self, args, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5, bias=True,
                encoder_var=1, decoder_var=1, tied=False, standparam=False):
        '''Note: `bias` only affects the bias of decoder'''
        super(TransformerModel, self).__init__()
        try:
            from torch.nn import TransformerEncoder
        except:
            raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or lower.')
        self.model_type = 'Transformer'
        self.encoder_var = encoder_var
        self.decoder_var = decoder_var
        self.ninp = ninp
        self.nhid = nhid
        self.bias = bias
        self.tied = tied

        self.src_mask = None
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout, attn_mult=args.attn_mult, 
                                                    bias=bias, encoder_var=encoder_var, nlayers=nlayers,
                                                    standparam=standparam)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.standparam = standparam
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        if standparam:
            self.decoder = nn.Linear(ninp, ntoken, bias=bias)
            if tied:
                with torch.no_grad():
                    self.encoder.weight = self.decoder.weight
        else:
            if tied:
                self.decoder = MuSharedReadout(self.encoder.weight, bias=bias, output_mult=args.output_mult)
            else:
                self.decoder = MuReadout(ninp, ntoken, bias=bias, output_mult=args.output_mult)
        self.init_weights()

    def _generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def init_weights(self):
        if not self.tied:
            if self.bias:
                self.decoder.bias.data.zero_()
            self.decoder.weight.data.zero_()

    def forward(self, src, has_mask=True):
        if has_mask:
            device = src.device
            if self.src_mask is None or self.src_mask.size(0) != len(src):
                mask = self._generate_square_subsequent_mask(len(src)).to(device)
                self.src_mask = mask
        else:
            self.src_mask = None

        src = self.encoder(src)
        src = self.pos_encoder(src)     
        output = self.transformer_encoder(src, self.src_mask)
        output = self.decoder(output)
        return F.log_softmax(output, dim=-1)

from torch.nn import Module, Linear, Dropout, LayerNorm, Parameter
from torch.nn.init import constant_

def _get_activation_fn(activation):
    if activation == "relu":
        return F.relu
    elif activation == "gelu":
        return F.gelu

    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))

class TransformerEncoderLayer(Module):
    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
    This standard encoder 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).
        activation: the activation function of intermediate layer, relu or gelu (default=relu).
    Examples::
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
        >>> src = torch.rand(10, 32, 512)
        >>> out = encoder_layer(src)
    """

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
                    encoder_var=1, attn_mult=1, bias=True, nlayers=1, standparam=False):
        super(TransformerEncoderLayer, self).__init__()
        self.attn_mult = attn_mult
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout,attn_mult=attn_mult,
                                            bias=bias, add_bias_kv=bias, encoder_var=encoder_var,
                                            standparam=standparam)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias)
        self.dropout = Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias)

        self.norm1 = LayerNorm(d_model, elementwise_affine=True)
        self.norm2 = LayerNorm(d_model, elementwise_affine=True)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)

        self.activation = _get_activation_fn(activation)
        self.init_method = init_method_normal((encoder_var / d_model)**0.5)
        
        self.nlayers = nlayers
        self.d_model = d_model
        self.reset_parameters()


    def __setstate__(self, state):
        if 'activation' not in state:
            state['activation'] = F.relu
        super(TransformerEncoderLayer, self).__setstate__(state)
    
    def reset_parameters(self):
        self.init_method(self.linear1.weight)
        self.init_method(self.linear2.weight)
        if self.linear1.bias is not None:
            constant_(self.linear1.bias, 0.)
        if self.linear2.bias is not None:
            constant_(self.linear2.bias, 0.)

    def forward(self, src, src_mask=None, src_key_padding_mask=None):
        # type: (Tensor, Optional[Tensor], Optional[Tensor]) -> Tensor
        r"""Pass the input through the encoder layer.
        Args:
            src: the sequence to the encoder layer (required).
            src_mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
        Shape:
            see the docs in Transformer class.
        """
        src1 = self.self_attn(src, src, src, attn_mask=src_mask,
                              key_padding_mask=src_key_padding_mask)[0]

        src = src + self.dropout1(src1)        
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        
        return src

class MultiheadAttention(Module):
    r"""Allows the model to jointly attend to information
    from different representation subspaces.
    See reference: Attention Is All You Need
    .. math::
        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
    Args:
        embed_dim: total dimension of the model.
        num_heads: parallel attention heads.
        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
        bias: add bias as module parameter. Default: True.
        add_bias_kv: add bias to the key and value sequences at dim=0.
        add_zero_attn: add a new batch of zeros to the key and
                       value sequences at dim=1.
        kdim: total number of features in key. Default: None.
        vdim: total number of features in key. Default: None.
        Note: if kdim and vdim are None, they will be set to embed_dim such that
        query, key, and value have the same number of features.
    Examples::
        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
    """
    __annotations__ = {
        'bias_k': torch._jit_internal.Optional[torch.Tensor],
        'bias_v': torch._jit_internal.Optional[torch.Tensor],
    }
    __constants__ = ['q_proj_weight', 'k_proj_weight', 'v_proj_weight', 'in_proj_weight']

    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False,
                 add_zero_attn=False, kdim=None, vdim=None, attn_mult=1, encoder_var=1, standparam=False):
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.attn_mult = attn_mult
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
        self.standparam = standparam

        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"

        if self._qkv_same_embed_dim is False:
            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
            self.register_parameter('in_proj_weight', None)
        else:
            self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))
            self.register_parameter('q_proj_weight', None)
            self.register_parameter('k_proj_weight', None)
            self.register_parameter('v_proj_weight', None)

        if bias:
            self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
        else:
            self.register_parameter('in_proj_bias', None)
        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)

        if add_bias_kv:
            self.bias_k = Parameter(torch.empty(1, 1, embed_dim))
            self.bias_v = Parameter(torch.empty(1, 1, embed_dim))
        else:
            self.bias_k = self.bias_v = None

        self.add_zero_attn = add_zero_attn
        
        self.init_method = init_method_normal((encoder_var / embed_dim)**0.5)

        self._reset_parameters()

    def _reset_parameters(self):
        if self._qkv_same_embed_dim:
            self.init_method(self.in_proj_weight)
            # zero initializing query head
            constant_(self.in_proj_weight[:self.embed_dim], 0.)
        else:
            # zero initializing query head
            constant_(self.q_proj_weight, 0.)
            self.init_method(self.k_proj_weight)
            self.init_method(self.v_proj_weight)

        self.init_method(self.out_proj.weight)
        if self.in_proj_bias is not None:
            constant_(self.in_proj_bias, 0.)
            constant_(self.out_proj.bias, 0.)
        if self.bias_k is not None:
            constant_(self.bias_k, 0.)
        if self.bias_v is not None:
            constant_(self.bias_v, 0.)

    def __setstate__(self, state):
        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
        if '_qkv_same_embed_dim' not in state:
            state['_qkv_same_embed_dim'] = True

        super(MultiheadAttention, self).__setstate__(state)

    def forward(self, query, key, value, key_padding_mask=None,
                need_weights=True, attn_mask=None):
        # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
        r"""
    Args:
        query, key, value: map a query and a set of key-value pairs to an output.
            See "Attention Is All You Need" for more details.
        key_padding_mask: if provided, specified padding elements in the key will
            be ignored by the attention. This is an binary mask. When the value is True,
            the corresponding value on the attention layer will be filled with -inf.
        need_weights: output attn_output_weights.
        attn_mask: 2D or 3D mask that prevents attention to certain positions. This is an additive mask
            (i.e. the values will be added to the attention layer). A 2D mask will be broadcasted for all
            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
    Shape:
        - Inputs:
        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
          the embedding dimension.
        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
          S is the source sequence length.
        - Outputs:
        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
          E is the embedding dimension.
        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
          L is the target sequence length, S is the source sequence length.
        """
        if not self._qkv_same_embed_dim:
            #### swapping pytorch's attn with ours ####
            return multi_head_attention_forward(
                query, key, value, self.embed_dim, self.num_heads,
                self.in_proj_weight, self.in_proj_bias,
                self.bias_k, self.bias_v, self.add_zero_attn,
                self.dropout, self.out_proj.weight, self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask, need_weights=need_weights,
                attn_mask=attn_mask, use_separate_proj_weight=True,
                q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
                v_proj_weight=self.v_proj_weight, attn_mult=self.attn_mult,
                standparam=self.standparam)
        else:
            #### swapping pytorch's attn with ours ####
            return multi_head_attention_forward(
                query, key, value, self.embed_dim, self.num_heads,
                self.in_proj_weight, self.in_proj_bias,
                self.bias_k, self.bias_v, self.add_zero_attn,
                self.dropout, self.out_proj.weight, self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask, need_weights=need_weights,
                attn_mask=attn_mask, attn_mult=self.attn_mult,
                standparam=self.standparam)

from _overrides import has_torch_function, handle_torch_function

def multi_head_attention_forward(query,                           # type: Tensor
                                 key,                             # type: Tensor
                                 value,                           # type: Tensor
                                 embed_dim_to_check,              # type: int
                                 num_heads,                       # type: int
                                 in_proj_weight,                  # type: Tensor
                                 in_proj_bias,                    # type: Tensor
                                 bias_k,                          # type: Optional[Tensor]
                                 bias_v,                          # type: Optional[Tensor]
                                 add_zero_attn,                   # type: bool
                                 dropout_p,                       # type: float
                                 out_proj_weight,                 # type: Tensor
                                 out_proj_bias,                   # type: Tensor
                                 training=True,                   # type: bool
                                 key_padding_mask=None,           # type: Optional[Tensor]
                                 need_weights=True,               # type: bool
                                 attn_mask=None,                  # type: Optional[Tensor]
                                 use_separate_proj_weight=False,  # type: bool
                                 q_proj_weight=None,              # type: Optional[Tensor]
                                 k_proj_weight=None,              # type: Optional[Tensor]
                                 v_proj_weight=None,              # type: Optional[Tensor]
                                 static_k=None,                   # type: Optional[Tensor]
                                 static_v=None,                   # type: Optional[Tensor]
                                 attn_mult=1,
                                 standparam=False
                                 ):
    # type: (...) -> Tuple[Tensor, Optional[Tensor]]
    r"""
    Args:
        query, key, value: map a query and a set of key-value pairs to an output.
            See "Attention Is All You Need" for more details.
        embed_dim_to_check: total dimension of the model.
        num_heads: parallel attention heads.
        in_proj_weight, in_proj_bias: input projection weight and bias.
        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
        add_zero_attn: add a new batch of zeros to the key and
                       value sequences at dim=1.
        dropout_p: probability of an element to be zeroed.
        out_proj_weight, out_proj_bias: the output projection weight and bias.
        training: apply dropout if is ``True``.
        key_padding_mask: if provided, specified padding elements in the key will
            be ignored by the attention. This is an binary mask. When the value is True,
            the corresponding value on the attention layer will be filled with -inf.
        need_weights: output attn_output_weights.
        attn_mask: 2D or 3D mask that prevents attention to certain positions. This is an additive mask
            (i.e. the values will be added to the attention layer). A 2D mask will be broadcasted for all
            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
        use_separate_proj_weight: the function accept the proj. weights for query, key,
            and value in different forms. If false, in_proj_weight will be used, which is
            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
        static_k, static_v: static key and value used for attention operators.
    Shape:
        Inputs:
        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
          the embedding dimension.
        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
          S is the source sequence length.
        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
        Outputs:
        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
          E is the embedding dimension.
        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
          L is the target sequence length, S is the source sequence length.
    """
    tgt_len, bsz, embed_dim = query.size()
    assert embed_dim == embed_dim_to_check
    assert key.size() == value.size()

    head_dim = embed_dim // num_heads
    assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"
    ###### only thing changed from pytorch source ######
    if standparam:
        scaling = float(head_dim) ** -0.5 * math.sqrt(attn_mult)
    else:
        scaling = float(head_dim) ** -1 * math.sqrt(attn_mult)

    if not use_separate_proj_weight:
        if torch.equal(query, key) and torch.equal(key, value):
            # self-attention
            q, k, v = F.linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)

        elif torch.equal(key, value):
            # encoder-decoder attention
            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = 0
            _end = embed_dim
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            q = F.linear(query, _w, _b)

            if key is None:
                assert value is None
                k = None
                v = None
            else:

                # This is inline in_proj function with in_proj_weight and in_proj_bias
                _b = in_proj_bias
                _start = embed_dim
                _end = None
                _w = in_proj_weight[_start:, :]
                if _b is not None:
                    _b = _b[_start:]
                k, v = F.linear(key, _w, _b).chunk(2, dim=-1)

        else:
            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = 0
            _end = embed_dim
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            q = F.linear(query, _w, _b)

            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = embed_dim
            _end = embed_dim * 2
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            k = F.linear(key, _w, _b)

            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = embed_dim * 2
            _end = None
            _w = in_proj_weight[_start:, :]
            if _b is not None:
                _b = _b[_start:]
            v = F.linear(value, _w, _b)
    else:
        q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)
        len1, len2 = q_proj_weight_non_opt.size()
        assert len1 == embed_dim and len2 == query.size(-1)

        k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)
        len1, len2 = k_proj_weight_non_opt.size()
        assert len1 == embed_dim and len2 == key.size(-1)

        v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)
        len1, len2 = v_proj_weight_non_opt.size()
        assert len1 == embed_dim and len2 == value.size(-1)

        if in_proj_bias is not None:
            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])
            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)])
            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):])
        else:
            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias)
            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias)
            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias)

    q = q * scaling

    if attn_mask is not None:
        if attn_mask.dim() == 2:
            attn_mask = attn_mask.unsqueeze(0)
            if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
                raise RuntimeError('The size of the 2D attn_mask is not correct.')
        elif attn_mask.dim() == 3:
            if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]:
                raise RuntimeError('The size of the 3D attn_mask is not correct.')
        else:
            raise RuntimeError("attn_mask's dimension {} is not supported".format(attn_mask.dim()))
        # attn_mask's dim is 3 now.

    if bias_k is not None and bias_v is not None:
        if static_k is None and static_v is None:
            k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = F.pad(attn_mask, (0, 1))
            if key_padding_mask is not None:
                key_padding_mask = F.pad(key_padding_mask, (0, 1))
        else:
            assert static_k is None, "bias cannot be added to static key."
            assert static_v is None, "bias cannot be added to static value."
    else:
        assert bias_k is None
        assert bias_v is None

    q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
    if k is not None:
        k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
    if v is not None:
        v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)

    if static_k is not None:
        assert static_k.size(0) == bsz * num_heads
        assert static_k.size(2) == head_dim
        k = static_k

    if static_v is not None:
        assert static_v.size(0) == bsz * num_heads
        assert static_v.size(2) == head_dim
        v = static_v

    src_len = k.size(1)

    if key_padding_mask is not None:
        assert key_padding_mask.size(0) == bsz
        assert key_padding_mask.size(1) == src_len

    if add_zero_attn:
        src_len += 1
        k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)
        v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)
        if attn_mask is not None:
            attn_mask = F.pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = F.pad(key_padding_mask, (0, 1))

    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
    assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]

    if attn_mask is not None:
        attn_output_weights += attn_mask

    if key_padding_mask is not None:
        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
        attn_output_weights = attn_output_weights.masked_fill(
            key_padding_mask.unsqueeze(1).unsqueeze(2),
            float('-inf'),
        )
        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)

    attn_output_weights = F.softmax(
        attn_output_weights, dim=-1)
    attn_output_weights = F.dropout(attn_output_weights, p=dropout_p, training=training)

    attn_output = torch.bmm(attn_output_weights, v)
    assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
    attn_output = F.linear(attn_output, out_proj_weight, out_proj_bias)

    if need_weights:
        # average attention weights over heads
        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
        return attn_output, attn_output_weights.sum(dim=1) / num_heads
    else:
        return attn_output, None