FunASR/funasr/models/emotion2vec/fairseq_modules.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, List
import numpy as np

def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
	return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)

class SamePad(nn.Module):
	def __init__(self, kernel_size, causal=False):
		super().__init__()
		if causal:
			self.remove = kernel_size - 1
		else:
			self.remove = 1 if kernel_size % 2 == 0 else 0
	
	def forward(self, x):
		if self.remove > 0:
			x = x[:, :, : -self.remove]
		return x

class TransposeLast(nn.Module):
	def __init__(self, deconstruct_idx=None):
		super().__init__()
		self.deconstruct_idx = deconstruct_idx
	
	def forward(self, x):
		if self.deconstruct_idx is not None:
			x = x[self.deconstruct_idx]
		return x.transpose(-2, -1)


class Fp32LayerNorm(nn.LayerNorm):
	def __init__(self, *args, **kwargs):
		super().__init__(*args, **kwargs)
	
	def forward(self, input):
		output = F.layer_norm(
			input.float(),
			self.normalized_shape,
			self.weight.float() if self.weight is not None else None,
			self.bias.float() if self.bias is not None else None,
			self.eps,
		)
		return output.type_as(input)


class Fp32GroupNorm(nn.GroupNorm):
	def __init__(self, *args, **kwargs):
		super().__init__(*args, **kwargs)
	
	def forward(self, input):
		output = F.group_norm(
			input.float(),
			self.num_groups,
			self.weight.float() if self.weight is not None else None,
			self.bias.float() if self.bias is not None else None,
			self.eps,
		)
		return output.type_as(input)


class ConvFeatureExtractionModel(nn.Module):
	def __init__(
		self,
		conv_layers: List[Tuple[int, int, int]],
		dropout: float = 0.0,
		mode: str = "default",
		conv_bias: bool = False,
	):
		super().__init__()
		
		assert mode in {"default", "layer_norm"}
		
		def block(
			n_in,
			n_out,
			k,
			stride,
			is_layer_norm=False,
			is_group_norm=False,
			conv_bias=False,
		):
			def make_conv():
				conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
				nn.init.kaiming_normal_(conv.weight)
				return conv
			
			assert (
				       is_layer_norm and is_group_norm
			       ) == False, "layer norm and group norm are exclusive"
			
			if is_layer_norm:
				return nn.Sequential(
					make_conv(),
					nn.Dropout(p=dropout),
					nn.Sequential(
						TransposeLast(),
						Fp32LayerNorm(dim, elementwise_affine=True),
						TransposeLast(),
					),
					nn.GELU(),
				)
			elif is_group_norm:
				return nn.Sequential(
					make_conv(),
					nn.Dropout(p=dropout),
					Fp32GroupNorm(dim, dim, affine=True),
					nn.GELU(),
				)
			else:
				return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
		
		in_d = 1
		self.conv_layers = nn.ModuleList()
		for i, cl in enumerate(conv_layers):
			assert len(cl) == 3, "invalid conv definition: " + str(cl)
			(dim, k, stride) = cl
			
			self.conv_layers.append(
				block(
					in_d,
					dim,
					k,
					stride,
					is_layer_norm=mode == "layer_norm",
					is_group_norm=mode == "default" and i == 0,
					conv_bias=conv_bias,
				)
			)
			in_d = dim
	
	def forward(self, x):
		
		# BxT -> BxCxT
		x = x.unsqueeze(1)
		
		for conv in self.conv_layers:
			x = conv(x)
		
		return x

def compute_mask_indices(
    shape: Tuple[int, int],
    padding_mask: Optional[torch.Tensor],
    mask_prob: float,
    mask_length: int,
    mask_type: str = "static",
    mask_other: float = 0.0,
    min_masks: int = 0,
    no_overlap: bool = False,
    min_space: int = 0,
    require_same_masks: bool = True,
    mask_dropout: float = 0.0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape

    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_type: how to compute mask lengths
            static = fixed size
            uniform = sample from uniform distribution [mask_other, mask_length*2]
            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
            poisson = sample from possion distribution with lambda = mask length
        min_masks: minimum number of masked spans
        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
        require_same_masks: if true, will randomly drop out masks until same amount of masks remains in each sample
        mask_dropout: randomly dropout this percentage of masks in each example
    """

    bsz, all_sz = shape
    mask = np.full((bsz, all_sz), False)

    all_num_mask = int(
        # add a random number for probabilistic rounding
        mask_prob * all_sz / float(mask_length)
        + np.random.rand()
    )

    all_num_mask = max(min_masks, all_num_mask)

    mask_idcs = []
    for i in range(bsz):
        if padding_mask is not None:
            sz = all_sz - padding_mask[i].long().sum().item()
            num_mask = int(
                # add a random number for probabilistic rounding
                mask_prob * sz / float(mask_length)
                + np.random.rand()
            )
            num_mask = max(min_masks, num_mask)
        else:
            sz = all_sz
            num_mask = all_num_mask

        if mask_type == "static":
            lengths = np.full(num_mask, mask_length)
        elif mask_type == "uniform":
            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
        elif mask_type == "normal":
            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
            lengths = [max(1, int(round(x))) for x in lengths]
        elif mask_type == "poisson":
            lengths = np.random.poisson(mask_length, size=num_mask)
            lengths = [int(round(x)) for x in lengths]
        else:
            raise Exception("unknown mask selection " + mask_type)

        if sum(lengths) == 0:
            lengths[0] = min(mask_length, sz - 1)

        if no_overlap:
            mask_idc = []

            def arrange(s, e, length, keep_length):
                span_start = np.random.randint(s, e - length)
                mask_idc.extend(span_start + i for i in range(length))

                new_parts = []
                if span_start - s - min_space >= keep_length:
                    new_parts.append((s, span_start - min_space + 1))
                if e - span_start - length - min_space > keep_length:
                    new_parts.append((span_start + length + min_space, e))
                return new_parts

            parts = [(0, sz)]
            min_length = min(lengths)
            for length in sorted(lengths, reverse=True):
                lens = np.fromiter(
                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
                    np.int,
                )
                l_sum = np.sum(lens)
                if l_sum == 0:
                    break
                probs = lens / np.sum(lens)
                c = np.random.choice(len(parts), p=probs)
                s, e = parts.pop(c)
                parts.extend(arrange(s, e, length, min_length))
            mask_idc = np.asarray(mask_idc)
        else:
            min_len = min(lengths)
            if sz - min_len <= num_mask:
                min_len = sz - num_mask - 1

            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)

            mask_idc = np.asarray(
                [
                    mask_idc[j] + offset
                    for j in range(len(mask_idc))
                    for offset in range(lengths[j])
                ]
            )

        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))

    min_len = min([len(m) for m in mask_idcs])
    for i, mask_idc in enumerate(mask_idcs):
        if len(mask_idc) > min_len and require_same_masks:
            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
        if mask_dropout > 0:
            num_holes = np.rint(len(mask_idc) * mask_dropout).astype(int)
            mask_idc = np.random.choice(
                mask_idc, len(mask_idc) - num_holes, replace=False
            )

        mask[i, mask_idc] = True

    return mask


class GradMultiply(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, scale):
        ctx.scale = scale
        res = x.new(x)
        return res

    @staticmethod
    def backward(ctx, grad):
        return grad * ctx.scale, None
    
    
def is_xla_tensor(tensor):
    return torch.is_tensor(tensor) and tensor.device.type == "xla"


def index_put(tensor, indices, value):
    if is_xla_tensor(tensor):
        for _ in range(indices.dim(), tensor.dim()):
            indices = indices.unsqueeze(-1)
        if indices.size(-1) < tensor.size(-1):
            indices = indices.expand_as(tensor)
        tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices)
    else:
        tensor[indices] = value
    return tensor