scripts/exp/train.py

import os
import subprocess
import time
import warnings
from pathlib import Path
from typing import Optional

import argbind
import audiotools as at
import torch
import torch.nn as nn
from audiotools import AudioSignal
from audiotools.data import transforms
from einops import rearrange
from rich import pretty
from rich.traceback import install
from tensorboardX import SummaryWriter

import vampnet
from vampnet.modules.transformer import VampNet
from vampnet.util import codebook_unflatten, codebook_flatten
from vampnet import mask as pmask
# from dac.model.dac import DAC
from lac.model.lac import LAC as DAC


# Enable cudnn autotuner to speed up training
# (can be altered by the funcs.seed function)
torch.backends.cudnn.benchmark = bool(int(os.getenv("CUDNN_BENCHMARK", 1)))
# Uncomment to trade memory for speed.

# Install to make things look nice
warnings.filterwarnings("ignore", category=UserWarning)
pretty.install()
install()

# optim
Accelerator = argbind.bind(at.ml.Accelerator, without_prefix=True)
CrossEntropyLoss = argbind.bind(nn.CrossEntropyLoss)
AdamW = argbind.bind(torch.optim.AdamW)
NoamScheduler = argbind.bind(vampnet.scheduler.NoamScheduler)

# transforms
filter_fn = lambda fn: hasattr(fn, "transform") and fn.__qualname__ not in [
    "BaseTransform",
    "Compose",
    "Choose",
]
tfm = argbind.bind_module(transforms, "train", "val", filter_fn=filter_fn)

# model
VampNet = argbind.bind(VampNet)


# data
AudioLoader = argbind.bind(at.datasets.AudioLoader)
AudioDataset = argbind.bind(at.datasets.AudioDataset, "train", "val")

IGNORE_INDEX = -100


@argbind.bind("train", "val", without_prefix=True)
def build_transform():
    transform = transforms.Compose(
        tfm.VolumeNorm(("const", -24)),
        # tfm.PitchShift(),
        tfm.RescaleAudio(),
    )
    return transform


@torch.no_grad()
def apply_transform(transform_fn, batch):
    sig: AudioSignal = batch["signal"]
    kwargs = batch["transform_args"]

    sig: AudioSignal = transform_fn(sig.clone(), **kwargs)
    return sig


def build_datasets(args, sample_rate: int):
    with argbind.scope(args, "train"):
        train_data = AudioDataset(
            AudioLoader(), sample_rate, transform=build_transform()
        )
    with argbind.scope(args, "val"):
        val_data = AudioDataset(AudioLoader(), sample_rate, transform=build_transform())
    with argbind.scope(args, "test"):
        test_data = AudioDataset(
            AudioLoader(), sample_rate, transform=build_transform()
        )
    return train_data, val_data, test_data


def rand_float(shape, low, high, rng):
    return rng.draw(shape)[:, 0] * (high - low) + low


def flip_coin(shape, p, rng):
    return rng.draw(shape)[:, 0] < p


@argbind.bind(without_prefix=True)
def load(
    args,
    accel: at.ml.Accelerator,
    save_path: str,
    resume: bool = False,
    tag: str = "latest",
    load_weights: bool = False,
    fine_tune_checkpoint: Optional[str] = None,
):
    codec = DAC.load(args["codec_ckpt"], map_location="cpu")
    codec.eval()

    model, v_extra = None, {}

    if resume:
        kwargs = {
            "folder": f"{save_path}/{tag}",
            "map_location": "cpu",
            "package": not load_weights,
        }
        if (Path(kwargs["folder"]) / "vampnet").exists():
            model, v_extra = VampNet.load_from_folder(**kwargs)
        else:
            raise ValueError(
                f"Could not find a VampNet checkpoint in {kwargs['folder']}"
            )


    if args["fine_tune"]:
        assert fine_tune_checkpoint is not None, "Must provide a fine-tune checkpoint"
        model = VampNet.load(location=Path(fine_tune_checkpoint), map_location="cpu")


    model = VampNet() if model is None else model

    model = accel.prepare_model(model)

    # assert accel.unwrap(model).n_codebooks == codec.quantizer.n_codebooks
    assert (
        accel.unwrap(model).vocab_size == codec.quantizer.quantizers[0].codebook_size
    )

    optimizer = AdamW(model.parameters(), use_zero=accel.use_ddp)
    scheduler = NoamScheduler(optimizer, d_model=accel.unwrap(model).embedding_dim)
    scheduler.step()

    trainer_state = {"state_dict": None, "start_idx": 0}

    if "optimizer.pth" in v_extra:
        optimizer.load_state_dict(v_extra["optimizer.pth"])
    if "scheduler.pth" in v_extra:
        scheduler.load_state_dict(v_extra["scheduler.pth"])
    if "trainer.pth" in v_extra:
        trainer_state = v_extra["trainer.pth"]

    return {
        "model": model,
        "codec": codec,
        "optimizer": optimizer,
        "scheduler": scheduler,
        "trainer_state": trainer_state,
    }



def num_params_hook(o, p):
    return o + f" {p/1e6:<.3f}M params."


def add_num_params_repr_hook(model):
    import numpy as np
    from functools import partial

    for n, m in model.named_modules():
        o = m.extra_repr()
        p = sum([np.prod(p.size()) for p in m.parameters()])

        setattr(m, "extra_repr", partial(num_params_hook, o=o, p=p))


def accuracy(
    preds: torch.Tensor,
    target: torch.Tensor,
    top_k: int = 1,
    ignore_index: Optional[int] = None,
) -> torch.Tensor:
    # Flatten the predictions and targets to be of shape (batch_size * sequence_length, n_class)
    preds = rearrange(preds, "b p s -> (b s) p")
    target = rearrange(target, "b s -> (b s)")

    # return torchmetrics.functional.accuracy(preds, target, task='multiclass', top_k=topk, num_classes=preds.shape[-1], ignore_index=ignore_index)
    if ignore_index is not None:
        # Create a mask for the ignored index
        mask = target != ignore_index
        # Apply the mask to the target and predictions
        preds = preds[mask]
        target = target[mask]

    # Get the top-k predicted classes and their indices
    _, pred_indices = torch.topk(preds, k=top_k, dim=-1)

    # Determine if the true target is in the top-k predicted classes
    correct = torch.sum(torch.eq(pred_indices, target.unsqueeze(1)), dim=1)

    # Calculate the accuracy
    accuracy = torch.mean(correct.float())

    return accuracy


@argbind.bind(without_prefix=True)
def train(
    args,
    accel: at.ml.Accelerator,
    codec_ckpt: str = None,
    seed: int = 0,
    save_path: str = "ckpt",
    max_epochs: int = int(100e3),
    epoch_length: int = 1000,
    save_audio_epochs: int = 2,
    save_epochs: list = [10, 50, 100, 200, 300, 400,],
    batch_size: int = 48,
    grad_acc_steps: int = 1,
    val_idx: list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    num_workers: int = 10,
    detect_anomaly: bool = False,
    grad_clip_val: float = 5.0,
    fine_tune: bool = False, 
    quiet: bool = False,
):
    assert codec_ckpt is not None, "codec_ckpt is required"

    seed = seed + accel.local_rank
    at.util.seed(seed)
    writer = None

    if accel.local_rank == 0:
        writer = SummaryWriter(log_dir=f"{save_path}/logs/")
        argbind.dump_args(args, f"{save_path}/args.yml")

    # load the codec model
    loaded = load(args, accel, save_path)
    model = loaded["model"]
    codec = loaded["codec"]
    optimizer = loaded["optimizer"]
    scheduler = loaded["scheduler"]
    trainer_state = loaded["trainer_state"]

    sample_rate = codec.sample_rate

    # a better rng for sampling from our schedule
    rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=seed)  

    # log a model summary w/ num params
    if accel.local_rank == 0:
        add_num_params_repr_hook(accel.unwrap(model))
        with open(f"{save_path}/model.txt", "w") as f:
            f.write(repr(accel.unwrap(model)))

    # load the datasets
    train_data, val_data, _ = build_datasets(args, sample_rate)
    train_dataloader = accel.prepare_dataloader(
        train_data,
        start_idx=trainer_state["start_idx"],
        num_workers=num_workers,
        batch_size=batch_size,
        collate_fn=train_data.collate,
    )
    val_dataloader = accel.prepare_dataloader(
        val_data,
        start_idx=0,
        num_workers=num_workers,
        batch_size=batch_size,
        collate_fn=val_data.collate,
    )

    criterion = CrossEntropyLoss()

    if fine_tune:
        import loralib as lora
        lora.mark_only_lora_as_trainable(model)


    class Trainer(at.ml.BaseTrainer):
        _last_grad_norm = 0.0

        def _metrics(self, vn, z_hat, r, target, flat_mask, output):
            for r_range in [(0, 0.5), (0.5, 1.0)]:
                unmasked_target = target.masked_fill(flat_mask.bool(), IGNORE_INDEX)
                masked_target = target.masked_fill(~flat_mask.bool(), IGNORE_INDEX)

                assert target.shape[0] == r.shape[0]
                # grab the indices of the r values that are in the range
                r_idx = (r >= r_range[0]) & (r < r_range[1])

                # grab the target and z_hat values that are in the range
                r_unmasked_target = unmasked_target[r_idx]
                r_masked_target = masked_target[r_idx]
                r_z_hat = z_hat[r_idx]

                for topk in (1, 25):
                    s, e = r_range
                    tag = f"accuracy-{s}-{e}/top{topk}"

                    output[f"{tag}/unmasked"] = accuracy(
                        preds=r_z_hat,
                        target=r_unmasked_target,
                        ignore_index=IGNORE_INDEX,
                        top_k=topk,
                    )
                    output[f"{tag}/masked"] = accuracy(
                        preds=r_z_hat,
                        target=r_masked_target,
                        ignore_index=IGNORE_INDEX,
                        top_k=topk,
                    )

        def train_loop(self, engine, batch):
            model.train()
            batch = at.util.prepare_batch(batch, accel.device)
            signal = apply_transform(train_data.transform, batch)

            output = {}
            vn = accel.unwrap(model)
            with accel.autocast():
                with torch.inference_mode():
                    codec.to(accel.device)
                    z = codec.encode(signal.samples, signal.sample_rate)["codes"]
                    z = z[:, : vn.n_codebooks, :]

                n_batch = z.shape[0]
                r = rng.draw(n_batch)[:, 0].to(accel.device)

                mask = pmask.random(z, r)
                mask = pmask.codebook_unmask(mask, vn.n_conditioning_codebooks)
                z_mask, mask = pmask.apply_mask(z, mask, vn.mask_token)
                
                z_mask_latent = vn.embedding.from_codes(z_mask, codec)

                dtype = torch.bfloat16 if accel.amp else None
                with accel.autocast(dtype=dtype):
                    z_hat = model(z_mask_latent, r)

                target = codebook_flatten(
                    z[:, vn.n_conditioning_codebooks :, :],
                )

                flat_mask = codebook_flatten(
                    mask[:, vn.n_conditioning_codebooks :, :],
                )

                # replace target with ignore index for masked tokens
                t_masked = target.masked_fill(~flat_mask.bool(), IGNORE_INDEX)
                output["loss"] = criterion(z_hat, t_masked)

                self._metrics(
                    vn=vn,
                    r=r,
                    z_hat=z_hat,
                    target=target,
                    flat_mask=flat_mask,
                    output=output,
                )

            
            accel.backward(output["loss"] / grad_acc_steps)

            output["other/learning_rate"] = optimizer.param_groups[0]["lr"]
            output["other/batch_size"] = z.shape[0]

            if (
                (engine.state.iteration % grad_acc_steps == 0)
                or (engine.state.iteration % epoch_length == 0)
                or (engine.state.iteration % epoch_length == 1)
            ):  # (or we reached the end of the epoch)
                accel.scaler.unscale_(optimizer)
                output["other/grad_norm"] = torch.nn.utils.clip_grad_norm_(
                    model.parameters(), grad_clip_val
                )
                self._last_grad_norm = output["other/grad_norm"]

                accel.step(optimizer)
                optimizer.zero_grad()

                scheduler.step()
                accel.update()
            else:
                output["other/grad_norm"] = self._last_grad_norm

            return {k: v for k, v in sorted(output.items())}

        @torch.no_grad()
        def val_loop(self, engine, batch):
            model.eval()
            codec.eval()
            batch = at.util.prepare_batch(batch, accel.device)
            signal = apply_transform(val_data.transform, batch)

            vn = accel.unwrap(model)
            z = codec.encode(signal.samples, signal.sample_rate)["codes"]
            z = z[:, : vn.n_codebooks, :]

            n_batch = z.shape[0]
            r = rng.draw(n_batch)[:, 0].to(accel.device)

            mask = pmask.random(z, r)
            mask = pmask.codebook_unmask(mask, vn.n_conditioning_codebooks)
            z_mask, mask = pmask.apply_mask(z, mask, vn.mask_token)

            z_mask_latent = vn.embedding.from_codes(z_mask, codec)

            z_hat = model(z_mask_latent, r)

            target = codebook_flatten(
                z[:, vn.n_conditioning_codebooks :, :],
            )

            flat_mask = codebook_flatten(
                mask[:, vn.n_conditioning_codebooks :, :]
            )

            output = {}
            # replace target with ignore index for masked tokens
            t_masked = target.masked_fill(~flat_mask.bool(), IGNORE_INDEX)
            output["loss"] = criterion(z_hat, t_masked)

            self._metrics(
                vn=vn,
                r=r,
                z_hat=z_hat,
                target=target,
                flat_mask=flat_mask,
                output=output,
            )

            return output

        def checkpoint(self, engine):
            if accel.local_rank != 0:
                print(f"ERROR:Skipping checkpoint on rank {accel.local_rank}")
                return

            metadata = {"logs": dict(engine.state.logs["epoch"])}

            if self.state.epoch % save_audio_epochs == 0:
                self.save_samples()

            tags = ["latest"]
            loss_key = "loss/val" if "loss/val" in metadata["logs"] else "loss/train"
            self.print(f"Saving to {str(Path('.').absolute())}")

            if self.state.epoch in save_epochs:
                tags.append(f"epoch={self.state.epoch}")

            if self.is_best(engine, loss_key):
                self.print(f"Best model so far")
                tags.append("best")

            if fine_tune:
                for tag in tags: 
                    # save the lora model 
                    (Path(save_path) / tag).mkdir(parents=True, exist_ok=True)
                    torch.save(
                        lora.lora_state_dict(accel.unwrap(model)), 
                        f"{save_path}/{tag}/lora.pth"
                    )

            for tag in tags:
                model_extra = {
                    "optimizer.pth": optimizer.state_dict(),
                    "scheduler.pth": scheduler.state_dict(),
                    "trainer.pth": {
                        "start_idx": self.state.iteration * batch_size,
                        "state_dict": self.state_dict(),
                    },
                    "metadata.pth": metadata,
                }

                accel.unwrap(model).metadata = metadata
                accel.unwrap(model).save_to_folder(
                    f"{save_path}/{tag}", model_extra,
                )

        def save_sampled(self, z):
            num_samples = z.shape[0]

            for i in range(num_samples):
                sampled = accel.unwrap(model).generate(
                    codec=codec,
                    time_steps=z.shape[-1],
                    start_tokens=z[i : i + 1],
                )
                sampled.cpu().write_audio_to_tb(
                    f"sampled/{i}",
                    self.writer,
                    step=self.state.epoch,
                    plot_fn=None,
                )


        def save_imputation(self, z: torch.Tensor):
            n_prefix = int(z.shape[-1] * 0.25)
            n_suffix = int(z.shape[-1] *  0.25)

            vn = accel.unwrap(model)

            mask = pmask.inpaint(z, n_prefix, n_suffix)
            mask = pmask.codebook_unmask(mask, vn.n_conditioning_codebooks)
            z_mask, mask = pmask.apply_mask(z, mask, vn.mask_token)

            imputed_noisy = vn.to_signal(z_mask, codec)
            imputed_true = vn.to_signal(z, codec)

            imputed = []
            for i in range(len(z)):
                imputed.append(
                    vn.generate(
                        codec=codec,
                        time_steps=z.shape[-1],
                        start_tokens=z[i][None, ...],
                        mask=mask[i][None, ...],
                    )   
                )   
            imputed = AudioSignal.batch(imputed)

            for i in range(len(val_idx)):
                imputed_noisy[i].cpu().write_audio_to_tb(
                    f"imputed_noisy/{i}",
                    self.writer,
                    step=self.state.epoch,
                    plot_fn=None,
                )
                imputed[i].cpu().write_audio_to_tb(
                    f"imputed/{i}",
                    self.writer,
                    step=self.state.epoch,
                    plot_fn=None,
                )
                imputed_true[i].cpu().write_audio_to_tb(
                    f"imputed_true/{i}",
                    self.writer,
                    step=self.state.epoch,
                    plot_fn=None,
                )

        @torch.no_grad()
        def save_samples(self):
            model.eval()
            codec.eval()
            vn = accel.unwrap(model)

            batch = [val_data[i] for i in val_idx]
            batch = at.util.prepare_batch(val_data.collate(batch), accel.device)

            signal = apply_transform(val_data.transform, batch)

            z = codec.encode(signal.samples, signal.sample_rate)["codes"]
            z = z[:, : vn.n_codebooks, :]

            r = torch.linspace(0.1, 0.95, len(val_idx)).to(accel.device)


            mask = pmask.random(z, r)
            mask = pmask.codebook_unmask(mask, vn.n_conditioning_codebooks)
            z_mask, mask = pmask.apply_mask(z, mask, vn.mask_token)

            z_mask_latent = vn.embedding.from_codes(z_mask, codec)

            z_hat = model(z_mask_latent, r)

            z_pred = torch.softmax(z_hat, dim=1).argmax(dim=1)
            z_pred = codebook_unflatten(z_pred, n_c=vn.n_predict_codebooks)
            z_pred = torch.cat([z[:, : vn.n_conditioning_codebooks, :], z_pred], dim=1)

            generated = vn.to_signal(z_pred, codec)
            reconstructed = vn.to_signal(z, codec)
            masked = vn.to_signal(z_mask.squeeze(1), codec)

            for i in range(generated.batch_size):
                audio_dict = {
                    "original": signal[i],
                    "masked": masked[i],
                    "generated": generated[i],
                    "reconstructed": reconstructed[i],
                }
                for k, v in audio_dict.items():
                    v.cpu().write_audio_to_tb(
                        f"samples/_{i}.r={r[i]:0.2f}/{k}",
                        self.writer,
                        step=self.state.epoch,
                        plot_fn=None,
                    )

            self.save_sampled(z)
            self.save_imputation(z)

    trainer = Trainer(writer=writer, quiet=quiet)

    if trainer_state["state_dict"] is not None:
        trainer.load_state_dict(trainer_state["state_dict"])
    if hasattr(train_dataloader.sampler, "set_epoch"):
        train_dataloader.sampler.set_epoch(trainer.trainer.state.epoch)

    trainer.run(
        train_dataloader,
        val_dataloader,
        num_epochs=max_epochs,
        epoch_length=epoch_length,
        detect_anomaly=detect_anomaly,
    )


if __name__ == "__main__":
    args = argbind.parse_args()
    args["args.debug"] = int(os.getenv("LOCAL_RANK", 0)) == 0
    with argbind.scope(args):
        with Accelerator() as accel:
            train(args, accel)