funasr_detach/models/emotion2vec/modules.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import torch
import numpy as np
import torch.nn as nn
from enum import Enum, auto
import torch.nn.functional as F
from dataclasses import dataclass
from funasr_detach.models.emotion2vec.fairseq_modules import (
    LayerNorm,
    SamePad,
    TransposeLast,
)


class Modality(Enum):
    AUDIO = auto()


@dataclass
class D2vDecoderConfig:
    decoder_dim: int = 384
    decoder_groups: int = 16
    decoder_kernel: int = 5
    decoder_layers: int = 5
    input_dropout: float = 0.1

    add_positions_masked: bool = False
    add_positions_all: bool = False

    decoder_residual: bool = True
    projection_layers: int = 1
    projection_ratio: float = 2.0


class FixedPositionalEncoder(nn.Module):
    def __init__(self, pos_embed):
        super().__init__()
        self.positions = pos_embed

    def forward(self, x, padding_mask):
        return self.positions


class TextFeatPositionalEncoder(nn.Module):
    """
    Original encoder expects (B, T) long input. This module wraps it to take
    local_encoder output which are (B, T, D) float tensors
    """

    def __init__(self, pos_encoder):
        super().__init__()
        self.pos_encoder = pos_encoder

    def forward(self, x, padding_mask):
        # assume padded token embeddings are 0s
        # TODO: consider using padding_mask as input
        return self.pos_encoder(x[..., 0])


class BlockEncoder(nn.Module):
    def __init__(self, blocks, norm_layer, layer_norm_first, layerdrop, dropout):
        super().__init__()
        self.blocks = blocks
        self.norm = norm_layer
        self.layer_norm_first = layer_norm_first
        self.layerdrop = layerdrop
        self.dropout = nn.Dropout(dropout, inplace=True)

    def forward(self, x, padding_mask, alibi_bias, alibi_scale):
        if self.norm is not None and not self.layer_norm_first:
            x = self.norm(x)

        x = self.dropout(x)

        for i, blk in enumerate(self.blocks):
            if (
                not self.training
                or self.layerdrop == 0
                or (np.random.random() > self.layerdrop)
            ):
                ab = alibi_bias
                if ab is not None and alibi_scale is not None:
                    scale = (
                        alibi_scale[i]
                        if alibi_scale.size(0) > 1
                        else alibi_scale.squeeze(0)
                    )
                    ab = ab * scale.type_as(ab)
                x, _ = blk(x, padding_mask, ab)

        if self.norm is not None and self.layer_norm_first:
            x = self.norm(x)

        return x


class DecoderBase(nn.Module):
    decoder_cfg: D2vDecoderConfig

    def __init__(self, cfg: D2vDecoderConfig):
        super().__init__()

        self.decoder_cfg = cfg

    def reset_parameters(self):
        for mod in self.proj.modules():
            if isinstance(mod, nn.Linear):
                mod.reset_parameters()

    def add_residual(self, x, residual, i, mask_info):
        if (
            residual is None
            or not self.decoder_cfg.decoder_residual
            or residual.size(1) != x.size(1)
        ):
            return x

        ret = x + residual

        return ret


class Decoder1d(DecoderBase):
    def __init__(self, cfg: D2vDecoderConfig, input_dim):
        super().__init__(cfg)

        def make_block(in_dim):
            block = [
                nn.Conv1d(
                    in_dim,
                    cfg.decoder_dim,
                    kernel_size=cfg.decoder_kernel,
                    padding=cfg.decoder_kernel // 2,
                    groups=cfg.decoder_groups,
                ),
                SamePad(cfg.decoder_kernel),
                TransposeLast(),
                LayerNorm(cfg.decoder_dim, elementwise_affine=False),
                TransposeLast(),
                nn.GELU(),
            ]

            return nn.Sequential(*block)

        self.blocks = nn.Sequential(
            *[
                make_block(input_dim if i == 0 else cfg.decoder_dim)
                for i in range(cfg.decoder_layers)
            ]
        )

        projs = []
        curr_dim = cfg.decoder_dim
        for i in range(cfg.projection_layers - 1):
            next_dim = int(curr_dim * cfg.projection_ratio) if i == 0 else curr_dim
            projs.append(nn.Linear(curr_dim, next_dim))
            projs.append(nn.GELU())
            curr_dim = next_dim
        projs.append(nn.Linear(curr_dim, input_dim))
        if len(projs) == 1:
            self.proj = projs[0]
        else:
            self.proj = nn.Sequential(*projs)

    def forward(self, x, mask_info):

        x = x.transpose(1, 2)

        residual = x

        for i, layer in enumerate(self.blocks):
            x = layer(x)
            x = self.add_residual(x, residual, i, mask_info)
            residual = x

        x = x.transpose(1, 2)
        x = self.proj(x)
        return x


class AltBlock(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        mlp_drop=0.0,
        post_mlp_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        layer_norm_first=True,
        ffn_targets=False,
        cosine_attention=False,
    ):
        super().__init__()

        self.layer_norm_first = layer_norm_first
        self.ffn_targets = ffn_targets

        from funasr_detach.models.emotion2vec.timm_modules import DropPath, Mlp

        self.norm1 = norm_layer(dim)
        self.attn = AltAttention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            cosine_attention=cosine_attention,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=mlp_drop,
        )
        self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False)

    def forward(self, x, padding_mask=None, alibi_bias=None):
        if self.layer_norm_first:
            x = x + self.drop_path(self.attn(self.norm1(x), padding_mask, alibi_bias))
            r = x = self.mlp(self.norm2(x))
            t = x
            x = r + self.drop_path(self.post_mlp_dropout(x))
            if not self.ffn_targets:
                t = x
        else:
            x = x + self.drop_path(self.attn(x, padding_mask, alibi_bias))
            r = x = self.norm1(x)
            x = self.mlp(x)
            t = x
            x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x)))
            if not self.ffn_targets:
                t = x

        return x, t


class AltAttention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        cosine_attention=False,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.cosine_attention = cosine_attention

        if cosine_attention:
            self.logit_scale = nn.Parameter(
                torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True
            )

    def forward(self, x, padding_mask=None, alibi_bias=None):
        B, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)  # qkv x B x H x L x D
        )
        q, k, v = (
            qkv[0],
            qkv[1],
            qkv[2],
        )  # make torchscript happy (cannot use tensor as tuple)

        dtype = q.dtype

        if self.cosine_attention:
            # cosine attention
            attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
            logit_scale = torch.clamp(
                self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01))
            ).exp()
            attn = attn * logit_scale
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)

        if alibi_bias is not None:
            attn = attn.type_as(alibi_bias)
            attn[:, : alibi_bias.size(1)] += alibi_bias

        if padding_mask is not None and padding_mask.any():
            attn = attn.masked_fill(
                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
                float("-inf"),
            )

        attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2)  #
        x = x.reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x