GPT_SoVITS/AR/modules/transformer_onnx.py

# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/transformer.py
import copy
import numbers
from functools import partial
from typing import Any
from typing import Callable
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import torch
from AR.modules.activation_onnx import MultiheadAttention
from AR.modules.scaling import BalancedDoubleSwish
from torch import nn
from torch import Tensor
from torch.nn import functional as F

_shape_t = Union[int, List[int], torch.Size]


class LayerNorm(nn.Module):
    __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
    normalized_shape: Tuple[int, ...]
    eps: float
    elementwise_affine: bool

    def __init__(
        self,
        normalized_shape: _shape_t,
        eps: float = 1e-5,
        elementwise_affine: bool = True,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            # mypy error: incompatible types in assignment
            normalized_shape = (normalized_shape,)  # type: ignore[assignment]
        self.normalized_shape = tuple(normalized_shape)  # type: ignore[arg-type]
        self.eps = eps
        self.elementwise_affine = elementwise_affine
        if self.elementwise_affine:
            self.weight = nn.Parameter(
                torch.empty(self.normalized_shape, **factory_kwargs)
            )
            self.bias = nn.Parameter(
                torch.empty(self.normalized_shape, **factory_kwargs)
            )
        else:
            self.register_parameter("weight", None)
            self.register_parameter("bias", None)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        if self.elementwise_affine:
            nn.init.ones_(self.weight)
            nn.init.zeros_(self.bias)

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            return (
                F.layer_norm(
                    input,
                    self.normalized_shape,
                    self.weight,
                    self.bias,
                    self.eps,
                ),
                embedding,
            )

        assert embedding is None
        return F.layer_norm(
            input, self.normalized_shape, self.weight, self.bias, self.eps
        )

    def extra_repr(self) -> str:
        return (
            "{normalized_shape}, eps={eps}, "
            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
        )


class IdentityNorm(nn.Module):
    def __init__(
        self,
        d_model: int,
        eps: float = 1e-5,
        device=None,
        dtype=None,
    ) -> None:
        super(IdentityNorm, self).__init__()

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            return input

        assert embedding is None
        return input


class TransformerEncoder(nn.Module):
    r"""TransformerEncoder is a stack of N encoder layers. Users can build the
    BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.

    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: ``True`` (enabled).

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

    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoder, self).__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(
        self,
        src: Tensor,
        mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        return_layer_states: bool = False,
        cache=None,
    ) -> Tensor:
        output = src
        for mod in self.layers:
            output = mod(
                output,
                src_mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                cache=cache,
            )

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

        return output


class TransformerEncoderLayer(nn.Module):
    __constants__ = ["batch_first", "norm_first"]
    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
        batch_first: bool = False,
        norm_first: bool = False,
        device=None,
        dtype=None,
        linear1_self_attention_cls: nn.Module = nn.Linear,
        linear2_self_attention_cls: nn.Module = nn.Linear,
        linear1_feedforward_cls: nn.Module = nn.Linear,
        linear2_feedforward_cls: nn.Module = nn.Linear,
        layer_norm_cls: nn.Module = LayerNorm,
        layer_norm_eps: float = 1e-5,
        adaptive_layer_norm=False,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = MultiheadAttention(
            d_model,  # 512 16
            nhead,
            dropout=dropout,
            batch_first=batch_first,
            linear1_cls=linear1_self_attention_cls,
            linear2_cls=linear2_self_attention_cls,
            **factory_kwargs,
        )
        self.linear1 = linear1_feedforward_cls(
            d_model, dim_feedforward, **factory_kwargs
        )
        self.dropout = nn.Dropout(dropout)
        self.linear2 = linear2_feedforward_cls(
            dim_feedforward, d_model, **factory_kwargs
        )
        self.norm_first = norm_first
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        if isinstance(activation, str):
            activation = _get_activation_fn(activation)
        elif isinstance(activation, partial):
            activation = activation(d_model)
        elif activation == BalancedDoubleSwish:
            activation = BalancedDoubleSwish(d_model)
        self.activation = activation

        norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
        if layer_norm_cls == IdentityNorm:
            norm2 = BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
        else:
            norm2 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)

        if adaptive_layer_norm:
            self.norm1 = AdaptiveLayerNorm(d_model, norm1)
            self.norm2 = AdaptiveLayerNorm(d_model, norm2)
        else:
            self.norm1 = norm1
            self.norm2 = norm2

    def __setstate__(self, state):
        super(TransformerEncoderLayer, self).__setstate__(state)
        if not hasattr(self, "activation"):
            self.activation = F.relu

    def forward(
        self,
        src: Tensor,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        cache=None,
    ) -> Tensor:
        x = src
        stage_embedding = None
        x = self.norm1(
            x + self._sa_block(x, src_mask, src_key_padding_mask, cache=cache),
            stage_embedding,
        )
        x = self.norm2(x + self._ff_block(x), stage_embedding)

        return x

    def _sa_block(
        self,
        x: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        cache=None,
    ) -> Tensor:
        x = self.self_attn(
            x,
            x,
            x,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            need_weights=False,
            cache=cache,
        )
        return self.dropout1(x)

    def _ff_block(self, x: Tensor) -> Tensor:
        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
        return self.dropout2(x)


class AdaptiveLayerNorm(nn.Module):
    r"""Adaptive Layer Normalization"""

    def __init__(self, d_model, norm) -> None:
        super(AdaptiveLayerNorm, self).__init__()
        self.project_layer = nn.Linear(d_model, 2 * d_model)
        self.norm = norm
        self.d_model = d_model
        self.eps = self.norm.eps

    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            weight, bias = torch.split(
                self.project_layer(embedding),
                split_size_or_sections=self.d_model,
                dim=-1,
            )
            return (weight * self.norm(input) + bias, embedding)

        weight, bias = torch.split(
            self.project_layer(embedding),
            split_size_or_sections=self.d_model,
            dim=-1,
        )
        return weight * self.norm(input) + bias


def _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])