model/cfm.py

"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""

from __future__ import annotations
from typing import Callable
from random import random

import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence

from torchdiffeq import odeint

from model.modules import MelSpec
from model.utils import (
    default, exists, 
    list_str_to_idx, list_str_to_tensor, 
    lens_to_mask, mask_from_frac_lengths,
) 


class CFM(nn.Module):
    def __init__(
        self,
        transformer: nn.Module,
        sigma = 0.,
        odeint_kwargs: dict = dict(
            # atol = 1e-5,
            # rtol = 1e-5,
            method = 'euler'  # 'midpoint'
        ),
        audio_drop_prob = 0.3,
        cond_drop_prob = 0.2,
        num_channels = None,
        mel_spec_module: nn.Module | None = None,
        mel_spec_kwargs: dict = dict(),
        frac_lengths_mask: tuple[float, float] = (0.7, 1.),
        vocab_char_map: dict[str: int] | None = None
    ):
        super().__init__()

        self.frac_lengths_mask = frac_lengths_mask

        # mel spec
        self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs))
        num_channels = default(num_channels, self.mel_spec.n_mel_channels)
        self.num_channels = num_channels

        # classifier-free guidance
        self.audio_drop_prob = audio_drop_prob
        self.cond_drop_prob = cond_drop_prob

        # transformer
        self.transformer = transformer
        dim = transformer.dim
        self.dim = dim

        # conditional flow related
        self.sigma = sigma

        # sampling related
        self.odeint_kwargs = odeint_kwargs

        # vocab map for tokenization
        self.vocab_char_map = vocab_char_map

    @property
    def device(self):
        return next(self.parameters()).device

    @torch.no_grad()
    def sample(
        self,
        cond: float['b n d'] | float['b nw'],
        text: int['b nt'] | list[str],
        duration: int | int['b'],
        *,
        lens: int['b'] | None = None,
        steps = 32,
        cfg_strength = 1., 
        sway_sampling_coef = None,
        seed: int | None = None,
        max_duration = 4096, 
        vocoder: Callable[[float['b d n']], float['b nw']] | None = None,
        no_ref_audio = False,
        duplicate_test = False,
        t_inter = 0.1,
        edit_mask = None,
    ):
        self.eval()

        if next(self.parameters()).dtype == torch.float16:
            cond = cond.half()

        # raw wave

        if cond.ndim == 2:
            cond = self.mel_spec(cond)
            cond = cond.permute(0, 2, 1)
            assert cond.shape[-1] == self.num_channels

        batch, cond_seq_len, device = *cond.shape[:2], cond.device
        if not exists(lens):
            lens = torch.full((batch,), cond_seq_len, device = device, dtype = torch.long)

        # text

        if isinstance(text, list):
            if exists(self.vocab_char_map):
                text = list_str_to_idx(text, self.vocab_char_map).to(device)
            else:
                text = list_str_to_tensor(text).to(device)
            assert text.shape[0] == batch

        if exists(text):
            text_lens = (text != -1).sum(dim = -1)
            lens = torch.maximum(text_lens, lens) # make sure lengths are at least those of the text characters

        # duration

        cond_mask = lens_to_mask(lens)
        if edit_mask is not None:
            cond_mask = cond_mask & edit_mask

        if isinstance(duration, int):
            duration = torch.full((batch,), duration, device = device, dtype = torch.long)

        duration = torch.maximum(lens + 1, duration) # just add one token so something is generated
        duration = duration.clamp(max = max_duration)
        max_duration = duration.amax()
        
        # duplicate test corner for inner time step oberservation
        if duplicate_test:
            test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2*cond_seq_len), value = 0.)
            
        cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value = 0.)
        cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value = False)
        cond_mask = cond_mask.unsqueeze(-1)
        step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))  # allow direct control (cut cond audio) with lens passed in

        if batch > 1:
            mask = lens_to_mask(duration)
        else:  # save memory and speed up, as single inference need no mask currently
            mask = None

        # test for no ref audio
        if no_ref_audio:
            cond = torch.zeros_like(cond)

        # neural ode

        def fn(t, x):
            # at each step, conditioning is fixed
            # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))

            # predict flow
            pred = self.transformer(x = x, cond = step_cond, text = text, time = t, mask = mask, drop_audio_cond = False, drop_text = False)
            if cfg_strength < 1e-5:
                return pred
            
            null_pred = self.transformer(x = x, cond = step_cond, text = text, time = t, mask = mask, drop_audio_cond = True, drop_text = True)
            return pred + (pred - null_pred) * cfg_strength

        # noise input
        # to make sure batch inference result is same with different batch size, and for sure single inference
        # still some difference maybe due to convolutional layers
        y0 = []
        for dur in duration:
            if exists(seed):
                torch.manual_seed(seed)
            y0.append(torch.randn(dur, self.num_channels, device = self.device, dtype=step_cond.dtype))
        y0 = pad_sequence(y0, padding_value = 0, batch_first = True)

        t_start = 0

        # duplicate test corner for inner time step oberservation
        if duplicate_test:
            t_start = t_inter
            y0 = (1 - t_start) * y0 + t_start * test_cond
            steps = int(steps * (1 - t_start))

        t = torch.linspace(t_start, 1, steps, device = self.device, dtype=step_cond.dtype)
        if sway_sampling_coef is not None:
            t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)

        trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
        
        sampled = trajectory[-1]
        out = sampled
        out = torch.where(cond_mask, cond, out)

        if exists(vocoder):
            out = out.permute(0, 2, 1)
            out = vocoder(out)

        return out, trajectory

    def forward(
        self,
        inp: float['b n d'] | float['b nw'], # mel or raw wave
        text: int['b nt'] | list[str],
        *,
        lens: int['b'] | None = None,
        noise_scheduler: str | None = None,
    ):
        # handle raw wave
        if inp.ndim == 2:
            inp = self.mel_spec(inp)
            inp = inp.permute(0, 2, 1)
            assert inp.shape[-1] == self.num_channels

        batch, seq_len, dtype, device, σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma

        # handle text as string
        if isinstance(text, list):
            if exists(self.vocab_char_map):
                text = list_str_to_idx(text, self.vocab_char_map).to(device)
            else:
                text = list_str_to_tensor(text).to(device)
            assert text.shape[0] == batch

        # lens and mask
        if not exists(lens):
            lens = torch.full((batch,), seq_len, device = device)
        
        mask = lens_to_mask(lens, length = seq_len)  # useless here, as collate_fn will pad to max length in batch

        # get a random span to mask out for training conditionally
        frac_lengths = torch.zeros((batch,), device = self.device).float().uniform_(*self.frac_lengths_mask)
        rand_span_mask = mask_from_frac_lengths(lens, frac_lengths)

        if exists(mask):
            rand_span_mask &= mask

        # mel is x1
        x1 = inp

        # x0 is gaussian noise
        x0 = torch.randn_like(x1)

        # time step
        time = torch.rand((batch,), dtype = dtype, device = self.device)
        # TODO. noise_scheduler

        # sample xt (φ_t(x) in the paper)
        t = time.unsqueeze(-1).unsqueeze(-1)
        φ = (1 - t) * x0 + t * x1
        flow = x1 - x0

        # only predict what is within the random mask span for infilling
        cond = torch.where(
            rand_span_mask[..., None],
            torch.zeros_like(x1), x1
        )

        # transformer and cfg training with a drop rate
        drop_audio_cond = random() < self.audio_drop_prob  # p_drop in voicebox paper
        if random() < self.cond_drop_prob:  # p_uncond in voicebox paper
            drop_audio_cond = True
            drop_text = True
        else:
            drop_text = False
            
        # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here
        # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences
        pred = self.transformer(x = φ, cond = cond, text = text, time = time, drop_audio_cond = drop_audio_cond, drop_text = drop_text)

        # flow matching loss
        loss = F.mse_loss(pred, flow, reduction = 'none')
        loss = loss[rand_span_mask]

        return loss.mean(), cond, pred