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

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

from typing import Optional

import torch.nn as nn
from torch import Tensor

from modelzoo.common.pytorch.layers.utils import _get_clones


class TransformerEncoder(nn.Module):
    r"""TransformerEncoder is a stack of N encoder layers

    Args:
        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
        num_layers: the number of sub-encoder-layers in the encoder (required).
        norm: the layer normalization component (optional).
        enable_nested_tensor: if True, input will automatically convert to nested tensor
            (and convert back on output). This will improve the overall performance of
            TransformerEncoder when padding rate is high. Default: ``False`` (disabled).

    Examples::
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
        >>> src = torch.rand(10, 32, 512)
        >>> out = transformer_encoder(src)
    """

    def __init__(
        self, encoder_layer, num_layers, norm=None, enable_nested_tensor=False
    ):
        super(TransformerEncoder, self).__init__()

        assert not enable_nested_tensor, "Nested tensors are not supported."
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm
        # Re-initialize all layers to get new set of weights for each layer
        self.__reset_parameters()

    def reset_parameters(self):
        self.__reset_parameters()

    def __reset_parameters(self):
        for layer in self.layers:
            layer.reset_parameters()
        if self.norm:
            if hasattr(self.norm, 'bias') and hasattr(self.norm.bias, "data"):
                self.norm.bias.data.zero_()
            if hasattr(self.norm, 'weight') and hasattr(
                self.norm.weight, "data"
            ):
                self.norm.weight.data.fill_(1.0)

    def forward(
        self,
        src: Tensor,
        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 layers in turn.

        Args:
            src: the sequence to the encoder (required).
            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.
        """
        output = src

        for mod in self.layers:
            output = mod(
                output,
                src_mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                self_attn_position_bias=self_attn_position_bias,
                **extra_args,
            )

        if self.norm is not None:
            output = self.norm(output)

        return output