molscribe/transformer/decoder.py

"""
Implementation of "Attention is All You Need" and of
subsequent transformer based architectures
"""

import torch
import torch.nn as nn

from onmt.decoders.decoder import DecoderBase
from onmt.modules import MultiHeadedAttention, AverageAttention
from onmt.modules.position_ffn import PositionwiseFeedForward
from onmt.modules.position_ffn import ActivationFunction
from onmt.utils.misc import sequence_mask


class TransformerDecoderLayerBase(nn.Module):
    def __init__(
        self,
        d_model,
        heads,
        d_ff,
        dropout,
        attention_dropout,
        self_attn_type="scaled-dot",
        max_relative_positions=0,
        aan_useffn=False,
        full_context_alignment=False,
        alignment_heads=0,
        pos_ffn_activation_fn=ActivationFunction.relu,
    ):
        """
        Args:
            d_model (int): the dimension of keys/values/queries in
                :class:`MultiHeadedAttention`, also the input size of
                the first-layer of the :class:`PositionwiseFeedForward`.
            heads (int): the number of heads for MultiHeadedAttention.
            d_ff (int): the second-layer of the
                :class:`PositionwiseFeedForward`.
            dropout (float): dropout in residual, self-attn(dot) and
                feed-forward
            attention_dropout (float): dropout in context_attn  (and
                self-attn(avg))
            self_attn_type (string): type of self-attention scaled-dot,
                average
            max_relative_positions (int):
                Max distance between inputs in relative positions
                representations
            aan_useffn (bool): Turn on the FFN layer in the AAN decoder
            full_context_alignment (bool):
                whether enable an extra full context decoder forward for
                alignment
            alignment_heads (int):
                N. of cross attention heads to use for alignment guiding
            pos_ffn_activation_fn (ActivationFunction):
                activation function choice for PositionwiseFeedForward layer

        """
        super(TransformerDecoderLayerBase, self).__init__()

        if self_attn_type == "scaled-dot":
            self.self_attn = MultiHeadedAttention(
                heads,
                d_model,
                dropout=attention_dropout,
                max_relative_positions=max_relative_positions,
            )
        elif self_attn_type == "average":
            self.self_attn = AverageAttention(
                d_model, dropout=attention_dropout, aan_useffn=aan_useffn
            )

        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout,
                                                    pos_ffn_activation_fn
                                                    )
        self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)
        self.drop = nn.Dropout(dropout)
        self.full_context_alignment = full_context_alignment
        self.alignment_heads = alignment_heads

    def forward(self, *args, **kwargs):
        """Extend `_forward` for (possibly) multiple decoder pass:
        Always a default (future masked) decoder forward pass,
        Possibly a second future aware decoder pass for joint learn
        full context alignement, :cite:`garg2019jointly`.

        Args:
            * All arguments of _forward.
            with_align (bool): whether return alignment attention.

        Returns:
            (FloatTensor, FloatTensor, FloatTensor or None):

            * output ``(batch_size, T, model_dim)``
            * top_attn ``(batch_size, T, src_len)``
            * attn_align ``(batch_size, T, src_len)`` or None
        """
        with_align = kwargs.pop("with_align", False)
        output, attns = self._forward(*args, **kwargs)
        top_attn = attns[:, 0, :, :].contiguous()
        attn_align = None
        if with_align:
            if self.full_context_alignment:
                # return _, (B, Q_len, K_len)
                _, attns = self._forward(*args, **kwargs, future=True)

            if self.alignment_heads > 0:
                attns = attns[:, : self.alignment_heads, :, :].contiguous()
            # layer average attention across heads, get ``(B, Q, K)``
            # Case 1: no full_context, no align heads -> layer avg baseline
            # Case 2: no full_context, 1 align heads -> guided align
            # Case 3: full_context, 1 align heads -> full cte guided align
            attn_align = attns.mean(dim=1)
        return output, top_attn, attn_align

    def update_dropout(self, dropout, attention_dropout):
        self.self_attn.update_dropout(attention_dropout)
        self.feed_forward.update_dropout(dropout)
        self.drop.p = dropout

    def _forward(self, *args, **kwargs):
        raise NotImplementedError

    def _compute_dec_mask(self, tgt_pad_mask, future):
        tgt_len = tgt_pad_mask.size(-1)
        if not future:  # apply future_mask, result mask in (B, T, T)
            future_mask = torch.ones(
                [tgt_len, tgt_len],
                device=tgt_pad_mask.device,
                dtype=torch.uint8,
            )
            future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)
            # BoolTensor was introduced in pytorch 1.2
            try:
                future_mask = future_mask.bool()
            except AttributeError:
                pass
            dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)
        else:  # only mask padding, result mask in (B, 1, T)
            dec_mask = tgt_pad_mask
        return dec_mask

    @staticmethod
    def _to_onmt3_cache(layer_cache, attn_type):
        """Convert old-style layer_cache dict to OpenNMT-py 3.x (bool, dict) tuple."""
        if layer_cache is None:
            return (False, {"keys": torch.tensor([]), "values": torch.tensor([])})
        # Cache enabled — convert from old key names to new
        if attn_type == "self":
            keys = layer_cache.get("self_keys")
            values = layer_cache.get("self_values")
        else:
            keys = layer_cache.get("memory_keys")
            values = layer_cache.get("memory_values")
        return (
            True,
            {
                "keys": keys if keys is not None else torch.tensor([]),
                "values": values if values is not None else torch.tensor([]),
            },
        )

    @staticmethod
    def _from_onmt3_cache(onmt_cache, layer_cache, attn_type):
        """Write back OpenNMT-py 3.x cache to old-style layer_cache dict."""
        if layer_cache is None:
            return
        cache_dict = onmt_cache[1] if isinstance(onmt_cache, tuple) else onmt_cache
        if attn_type == "self":
            layer_cache["self_keys"] = cache_dict.get("keys")
            layer_cache["self_values"] = cache_dict.get("values")
        else:
            layer_cache["memory_keys"] = cache_dict.get("keys")
            layer_cache["memory_values"] = cache_dict.get("values")

    @staticmethod
    def _expand_mask(mask):
        """Expand 3D mask (B, 1, L) to 4D (B, 1, 1, L) for OpenNMT-py 3.x."""
        if mask is not None and mask.dim() == 3:
            return mask.unsqueeze(1)
        return mask

    def _forward_self_attn(self, inputs_norm, dec_mask, layer_cache, step):
        if isinstance(self.self_attn, MultiHeadedAttention):
            # OpenNMT-py 3.x: layer_cache and attn_type are instance attributes
            self.self_attn.layer_cache = self._to_onmt3_cache(layer_cache, "self")
            self.self_attn.attn_type = "self"
            result = self.self_attn(
                inputs_norm,
                inputs_norm,
                inputs_norm,
                mask=self._expand_mask(dec_mask),
            )
            self._from_onmt3_cache(self.self_attn.layer_cache, layer_cache, "self")
            return result
        elif isinstance(self.self_attn, AverageAttention):
            return self.self_attn(
                inputs_norm, mask=dec_mask, layer_cache=layer_cache, step=step
            )
        else:
            raise ValueError(
                f"self attention {type(self.self_attn)} not supported"
            )


class TransformerDecoderLayer(TransformerDecoderLayerBase):
    """Transformer Decoder layer block in Pre-Norm style.
    Pre-Norm style is an improvement w.r.t. Original paper's Post-Norm style,
    providing better converge speed and performance. This is also the actual
    implementation in tensor2tensor and also avalable in fairseq.
    See https://tunz.kr/post/4 and :cite:`DeeperTransformer`.

    .. mermaid::

        graph LR
        %% "*SubLayer" can be self-attn, src-attn or feed forward block
            A(input) --> B[Norm]
            B --> C["*SubLayer"]
            C --> D[Drop]
            D --> E((+))
            A --> E
            E --> F(out)

    """

    def __init__(
        self,
        d_model,
        heads,
        d_ff,
        dropout,
        attention_dropout,
        self_attn_type="scaled-dot",
        max_relative_positions=0,
        aan_useffn=False,
        full_context_alignment=False,
        alignment_heads=0,
        pos_ffn_activation_fn=ActivationFunction.relu,
    ):
        """
        Args:
            See TransformerDecoderLayerBase
        """
        super(TransformerDecoderLayer, self).__init__(
            d_model,
            heads,
            d_ff,
            dropout,
            attention_dropout,
            self_attn_type,
            max_relative_positions,
            aan_useffn,
            full_context_alignment,
            alignment_heads,
            pos_ffn_activation_fn=pos_ffn_activation_fn,
        )
        self.context_attn = MultiHeadedAttention(
            heads, d_model, dropout=attention_dropout
        )
        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)

    def update_dropout(self, dropout, attention_dropout):
        super(TransformerDecoderLayer, self).update_dropout(
            dropout, attention_dropout
        )
        self.context_attn.update_dropout(attention_dropout)

    def _forward(
        self,
        inputs,
        memory_bank,
        src_pad_mask,
        tgt_pad_mask,
        layer_cache=None,
        step=None,
        future=False,
    ):
        """A naive forward pass for transformer decoder.

        # T: could be 1 in the case of stepwise decoding or tgt_len

        Args:
            inputs (FloatTensor): ``(batch_size, T, model_dim)``
            memory_bank (FloatTensor): ``(batch_size, src_len, model_dim)``
            src_pad_mask (bool): ``(batch_size, 1, src_len)``
            tgt_pad_mask (bool): ``(batch_size, 1, T)``
            layer_cache (dict or None): cached layer info when stepwise decode
            step (int or None): stepwise decoding counter
            future (bool): If set True, do not apply future_mask.

        Returns:
            (FloatTensor, FloatTensor):

            * output ``(batch_size, T, model_dim)``
            * attns ``(batch_size, head, T, src_len)``

        """
        dec_mask = None

        if inputs.size(1) > 1:
            # masking is necessary when sequence length is greater than one
            dec_mask = self._compute_dec_mask(tgt_pad_mask, future)

        inputs_norm = self.layer_norm_1(inputs)

        query, _ = self._forward_self_attn(
            inputs_norm, dec_mask, layer_cache, step
        )

        query = self.drop(query) + inputs

        query_norm = self.layer_norm_2(query)
        # OpenNMT-py 3.x: layer_cache and attn_type are instance attributes
        self.context_attn.layer_cache = self._to_onmt3_cache(layer_cache, "context")
        self.context_attn.attn_type = "context"
        mid, attns = self.context_attn(
            memory_bank,
            memory_bank,
            query_norm,
            mask=self._expand_mask(src_pad_mask),
        )
        self._from_onmt3_cache(self.context_attn.layer_cache, layer_cache, "context")
        output = self.feed_forward(self.drop(mid) + query)

        return output, attns


class TransformerDecoderBase(DecoderBase):
    def __init__(self, d_model, copy_attn, alignment_layer):
        super(TransformerDecoderBase, self).__init__()

        # Decoder State
        self.state = {}

        # previously, there was a GlobalAttention module here for copy
        # attention. But it was never actually used -- the "copy" attention
        # just reuses the context attention.
        self._copy = copy_attn
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)

        self.alignment_layer = alignment_layer

    @classmethod
    def from_opt(cls, opt, embeddings):
        """Alternate constructor."""
        return cls(
            opt.dec_layers,
            opt.dec_rnn_size,
            opt.heads,
            opt.transformer_ff,
            opt.copy_attn,
            opt.self_attn_type,
            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,
            opt.attention_dropout[0] if type(opt.attention_dropout) is list else opt.attention_dropout,
            embeddings,
            opt.max_relative_positions,
            opt.aan_useffn,
            opt.full_context_alignment,
            opt.alignment_layer,
            alignment_heads=opt.alignment_heads,
            pos_ffn_activation_fn=opt.pos_ffn_activation_fn,
        )

    def init_state(self, src, memory_bank, enc_hidden):
        """Initialize decoder state."""
        self.state["src"] = src
        self.state["cache"] = None

    def map_state(self, fn):
        def _recursive_map(struct, batch_dim=0):
            for k, v in struct.items():
                if v is not None:
                    if isinstance(v, dict):
                        _recursive_map(v)
                    else:
                        struct[k] = fn(v, batch_dim)

        if self.state["src"] is not None:
            self.state["src"] = fn(self.state["src"], 1)
        if self.state["cache"] is not None:
            _recursive_map(self.state["cache"])

    def detach_state(self):
        raise NotImplementedError

    def forward(self, *args, **kwargs):
        raise NotImplementedError

    def update_dropout(self, dropout, attention_dropout):
        self.embeddings.update_dropout(dropout)
        for layer in self.transformer_layers:
            layer.update_dropout(dropout, attention_dropout)


class TransformerDecoder(TransformerDecoderBase):
    """The Transformer decoder from "Attention is All You Need".
    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`

    .. mermaid::

       graph BT
          A[input]
          B[multi-head self-attn]
          BB[multi-head src-attn]
          C[feed forward]
          O[output]
          A --> B
          B --> BB
          BB --> C
          C --> O


    Args:
        num_layers (int): number of decoder layers.
        d_model (int): size of the model
        heads (int): number of heads
        d_ff (int): size of the inner FF layer
        copy_attn (bool): if using a separate copy attention
        self_attn_type (str): type of self-attention scaled-dot, average
        dropout (float): dropout in residual, self-attn(dot) and feed-forward
        attention_dropout (float): dropout in context_attn (and self-attn(avg))
        embeddings (onmt.modules.Embeddings):
            embeddings to use, should have positional encodings
        max_relative_positions (int):
            Max distance between inputs in relative positions representations
        aan_useffn (bool): Turn on the FFN layer in the AAN decoder
        full_context_alignment (bool):
            whether enable an extra full context decoder forward for alignment
        alignment_layer (int): N� Layer to supervise with for alignment guiding
        alignment_heads (int):
            N. of cross attention heads to use for alignment guiding
    """

    def __init__(
        self,
        num_layers,
        d_model,
        heads,
        d_ff,
        copy_attn,
        self_attn_type,
        dropout,
        attention_dropout,
        max_relative_positions,
        aan_useffn,
        full_context_alignment,
        alignment_layer,
        alignment_heads,
        pos_ffn_activation_fn=ActivationFunction.relu,
    ):
        super(TransformerDecoder, self).__init__(
            d_model, copy_attn, alignment_layer
        )

        self.transformer_layers = nn.ModuleList(
            [
                TransformerDecoderLayer(
                    d_model,
                    heads,
                    d_ff,
                    dropout,
                    attention_dropout,
                    self_attn_type=self_attn_type,
                    max_relative_positions=max_relative_positions,
                    aan_useffn=aan_useffn,
                    full_context_alignment=full_context_alignment,
                    alignment_heads=alignment_heads,
                    pos_ffn_activation_fn=pos_ffn_activation_fn,
                )
                for i in range(num_layers)
            ]
        )

    def detach_state(self):
        self.state["src"] = self.state["src"].detach()

    def forward(self, tgt_emb, memory_bank, src_pad_mask=None, tgt_pad_mask=None, step=None, **kwargs):
        """Decode, possibly stepwise."""
        if step == 0:
            self._init_cache(memory_bank)

        batch_size, src_len, src_dim = memory_bank.size()
        device = memory_bank.device
        if src_pad_mask is None:
            src_pad_mask = torch.zeros((batch_size, 1, src_len), dtype=torch.bool, device=device)
        output = tgt_emb
        batch_size, tgt_len, tgt_dim = tgt_emb.size()
        if tgt_pad_mask is None:
            tgt_pad_mask = torch.zeros((batch_size, 1, tgt_len), dtype=torch.bool, device=device)

        future = kwargs.pop("future", False)
        with_align = kwargs.pop("with_align", False)
        attn_aligns = []
        hiddens = []

        for i, layer in enumerate(self.transformer_layers):
            layer_cache = (
                self.state["cache"]["layer_{}".format(i)]
                if step is not None
                else None
            )
            output, attn, attn_align = layer(
                output,
                memory_bank,
                src_pad_mask,
                tgt_pad_mask,
                layer_cache=layer_cache,
                step=step,
                with_align=with_align,
                future=future
            )
            hiddens.append(output)
            if attn_align is not None:
                attn_aligns.append(attn_align)

        output = self.layer_norm(output)  # (B, L, D)

        attns = {"std": attn}
        if self._copy:
            attns["copy"] = attn
        if with_align:
            attns["align"] = attn_aligns[self.alignment_layer]  # `(B, Q, K)`
            # attns["align"] = torch.stack(attn_aligns, 0).mean(0)  # All avg

        # TODO change the way attns is returned dict => list or tuple (onnx)
        return output, attns, hiddens

    def _init_cache(self, memory_bank):
        self.state["cache"] = {}
        for i, layer in enumerate(self.transformer_layers):
            layer_cache = {"memory_keys": None, "memory_values": None, "self_keys": None, "self_values": None}
            self.state["cache"]["layer_{}".format(i)] = layer_cache