Source code for modelzoo.common.pytorch.layers.TransformerEncoderLayer

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"""
Adapted from https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/transformer.py
"""

from typing import Callable, Optional, Type, Union

import torch.nn as nn
from torch import Tensor
from torch.nn import Dropout, LayerNorm

from modelzoo.common.pytorch.layers.AttentionHelper import get_attention_module
from modelzoo.common.pytorch.layers.FeedForwardNetwork import FeedForwardNetwork


class TransformerEncoderLayer(nn.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 multihead attention 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 the intermediate layer, can be a string
            ("relu" or "gelu") or a unary callable. Default: gelu
        layer_norm_eps: the eps value in layer normalization components (default=1e-5).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
        norm_layer: the normalization class that will be used before/after FF layers (default=nn.LayerNorm)
        norm_first: if ``True``, layer norm is done prior to attention and feedforward
            operations, respectively. Otherwise it's done after. Default: ``False`` (after).
        attention_dropout_rate: Attention dropout rate. If None, defaults to dropout.
        use_projection_bias_in_attention: Add bias to Q,K,V projections
            in the Attention layer. Defaults to False.
        attention_type: Should be in ["scaled_dot_product", "dot_product"]
        attention_softmax_fp32: Use FP32 softmax in attention block.
        attention_inner_dim (int):  Number of output units in attention query/key/value projection. Defaults to d_model
        add_cross_attention: If ``True``, adds cross-attention layer between encoder/decoder,
            otherwise, only self-attention is used in the decoder (GPT-style models should set to ``False``)
        use_ffn_bias_in_attention: Add bias in the concluding FFN
            in the Attention layer. Defaults to False.
        use_ffn_bias: Add bias in all dense layers of the decoder's ffn sublayer
        attention_initializer: Attention layer initializer. Defaults to "xavier_uniform".
        attention_q_initializer: Query projection kernel initializer. If not
            specified, the query will be initialized via ``attention_initializer``
        ffn_initializer: FFN layer initializer. Defaults to "xavier_uniform".
        ffn_output_layer_initializer: If not None, initialize the last FFN layer
            with this initializer. Defaults to None.
        use_ff_layer1_dropout: If ``True``, dropout will be enabled after the first feed forward layer. Default: True
        use_ff_layer2_dropout = If ``True``, dropout will be enabled after the second feed forward layer. Default: True
        ffn_dropout_rate: Controls dropout rate of FF's first layer. If None, defaults to dropout.

    Example:
        When ``batch_first`` is ``True``:
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True)
        >>> src = torch.rand(32, 10, 512)
        >>> out = encoder_layer(src)
    """

    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = "gelu",
        layer_norm_eps: float = 1e-5,
        batch_first: bool = True,
        norm_layer: Type[nn.Module] = LayerNorm,
        norm_first: bool = False,
        attention_module: Union[str, nn.Module] = "aiayn_attention",
        extra_attention_params={},
        device=None,
        attention_dropout_rate: Optional[float] = None,
        attention_type="scaled_dot_product",
        attention_softmax_fp32: Optional[bool] = True,
        attention_inner_dim=None,
        use_projection_bias_in_attention=False,
        use_ffn_bias_in_attention=False,
        use_ffn_bias=False,
        attention_initializer="xavier_uniform",
        attention_q_initializer=None,
        ffn_initializer="xavier_uniform",
        ffn_output_layer_initializer=None,
        use_ff_layer1_dropout: bool = True,
        use_ff_layer2_dropout: bool = True,
        ffn_dropout_rate: Optional[float] = None,
    ) -> None:
        super(TransformerEncoderLayer, self).__init__()

        assert batch_first, "Currently, only batch_first=True is supported"
        if attention_dropout_rate is None:
            attention_dropout_rate = dropout

        AttentionModule = get_attention_module(
            attention_module, extra_attention_params
        )

        self.self_attn = AttentionModule(
            d_model,
            nhead,
            inner_dim=attention_inner_dim,
            dropout=attention_dropout_rate,
            batch_first=batch_first,
            attention_type=attention_type,
            softmax_dtype_fp32=attention_softmax_fp32,
            use_projection_bias=use_projection_bias_in_attention,
            use_ffn_bias=use_ffn_bias_in_attention,
            attention_initializer=attention_initializer,
            attention_q_initializer=attention_q_initializer,
            device=device,
            **extra_attention_params,
        )

        if ffn_dropout_rate is None:
            ffn_dropout_rate = dropout

        self.ffn = FeedForwardNetwork(
            input_unit=d_model,
            layers_units=[dim_feedforward, d_model],
            layers_activation=[activation, None],
            layers_dropout_rates=[
                ffn_dropout_rate if use_ff_layer1_dropout else None,
                dropout if use_ff_layer2_dropout else None,
            ],
            use_bias=use_ffn_bias,
            kernel_initializer=ffn_initializer,
            output_layer_initializer=ffn_output_layer_initializer,
            bias_initializer="zeros",
            device=device,
        )

        self.norm_first = norm_first
        self.norm1 = norm_layer(d_model, eps=layer_norm_eps, device=device,)
        self.norm2 = norm_layer(d_model, eps=layer_norm_eps, device=device,)
        self.dropout1 = Dropout(dropout)

        self.__reset_parameters()

    def reset_parameters(self):
        self.__reset_parameters()

    def __reset_parameters(self):
        self.self_attn.reset_parameters()
        self.ffn.reset_parameters()
        if hasattr(self.norm1, 'bias') and hasattr(self.norm1.bias, "data"):
            self.norm1.bias.data.zero_()
        if hasattr(self.norm1, 'weight') and hasattr(self.norm1.weight, "data"):
            self.norm1.weight.data.fill_(1.0)
        if hasattr(self.norm2, 'bias') and hasattr(self.norm2.bias, "data"):
            self.norm2.bias.data.zero_()
        if hasattr(self.norm2, 'weight') and hasattr(self.norm1.weight, "data"):
            self.norm2.weight.data.fill_(1.0)

    def forward(
        self,
        src: Tensor,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        self_attn_position_bias: Optional[Tensor] = None,
        **extra_args,
    ) -> 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).
            self_attn_position_bias: the tensor containing position bias to apply in self-attention,
                can be obtained from relative or alibi position embeddings.

        Shape:
            see the docs in Transformer class.
        """

        # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf

        x = src
        if self.norm_first:
            x = x + self._sa_block(
                self.norm1(x),
                src_mask,
                src_key_padding_mask,
                self_attn_position_bias=self_attn_position_bias,
                **extra_args,
            )
            x = x + self.ffn(self.norm2(x))
        else:
            x = self.norm1(
                x
                + self._sa_block(
                    x,
                    src_mask,
                    src_key_padding_mask,
                    self_attn_position_bias=self_attn_position_bias,
                    **extra_args,
                )
            )
            x = self.norm2(x + self.ffn(x))

        return x

    # self-attention block
    def _sa_block(
        self,
        x: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        self_attn_position_bias: Optional[Tensor],
        **extra_args,
    ) -> Tensor:
        x = self.self_attn(
            x,
            x,
            x,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            position_bias=self_attn_position_bias,
            need_weights=False,
            **extra_args,
        )
        return self.dropout1(x)