train_avwm_stage3.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
# --------------------------------------------------------

from inference_avwm import model_forward_wrapper_av
import torch
# the first flag below was False when we tested this script but True makes A100 training a lot faster:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

import matplotlib
matplotlib.use('Agg')
from collections import OrderedDict
from copy import deepcopy
from time import time
import argparse
import logging
import os
import matplotlib.pyplot as plt 
import yaml


import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from diffusers.models import AutoencoderKL

from distributed import init_distributed
from models import AVCDiT_models
from diffusion import create_diffusion
from datasets import TrainingDataset
from misc import transform
from soundstream import SoundStream
import torchaudio
from eval_audio import build_mel_transform, mel_cosine_stereo, drms_avg_db_stereo, save_ref_hat_spectrogram_panel

#################################################################################
#                             Training Helper Functions                         #
#################################################################################


def load_checkpoint_if_available(model, ema, opt, scaler, config, device, logger, args):
    start_epoch = 0
    train_steps = 0
    latest_path = os.path.join(config['results_dir'], config['run_name'], "checkpoints", "latest.pth.tar")
    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
        print("Loading model from ", latest_path)
        checkpoint = torch.load(latest_path, map_location=f"cuda:{device}", weights_only=False)

        ema_ckp = {k.replace('_orig_mod.', ''): v for k, v in checkpoint["ema"].items()}
        model.load_state_dict(ema_ckp, strict=False)
        print("Model weights loaded.")
        ema.load_state_dict(ema_ckp, strict=False)
        print("EMA weights loaded.")

        if args.restart_from_checkpoint:
            logger.info("Restarting training: epoch and step counters set to 0.")
        else:
            if "opt" in checkpoint:
                opt_ckp = {k.replace('_orig_mod.', ''): v for k, v in checkpoint["opt"].items()}
                opt.load_state_dict(opt_ckp)
                print("Optimizer state loaded.")
            if "scaler" in checkpoint and scaler is not None:
                scaler.load_state_dict(checkpoint["scaler"])
                print("GradScaler state loaded.")
            if "epoch" in checkpoint:
                start_epoch = checkpoint["epoch"] + 1
            if "train_steps" in checkpoint:
                train_steps = checkpoint["train_steps"]
            logger.info(f"Resuming from epoch {start_epoch}, step {train_steps}")

    return start_epoch, train_steps


@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
    """
    Step the EMA model towards the current model.
    """
    ema_params = OrderedDict(ema_model.named_parameters())
    model_params = OrderedDict(model.named_parameters())

    for name, param in model_params.items():
        name = name.replace('_orig_mod.', '')
        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)


def requires_grad(model, flag=True):
    """
    Set requires_grad flag for all parameters in a model.
    """
    for p in model.parameters():
        p.requires_grad = flag


def cleanup():
    """
    End DDP training.
    """
    dist.destroy_process_group()


def create_logger(logging_dir):
    """
    Create a logger that writes to a log file and stdout.
    """
    if dist.get_rank() == 0:  # real logger
        logging.basicConfig(
            level=logging.INFO,
            format='[\033[34m%(asctime)s\033[0m] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
        )
        logger = logging.getLogger(__name__)
    else:  # dummy logger (does nothing)
        logger = logging.getLogger(__name__)
        logger.addHandler(logging.NullHandler())
    return logger

#################################################################################
#                                  Training Loop                                #
#################################################################################

def main(args):
    """
    Trains a new AVCDiT model.
    """
    assert torch.cuda.is_available(), "Training currently requires at least one GPU."

    # Setup DDP:
    _, rank, device, _ = init_distributed()
    # rank = dist.get_rank()
    seed = args.global_seed * dist.get_world_size() + rank
    torch.manual_seed(seed)
    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
    with open("config/eval_config.yaml", "r") as f:
        default_config = yaml.safe_load(f)
    config = default_config
    
    with open(args.config, "r") as f:
        user_config = yaml.safe_load(f)
    config.update(user_config)
    
    # Setup an experiment folder:
    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
    if rank == 0:
        os.makedirs(checkpoint_dir, exist_ok=True)
        logger = create_logger(experiment_dir)
        logger.info(f"Experiment directory created at {experiment_dir}")
    else:
        logger = create_logger(None)

    # Create model:
    tokenizer_v = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)

    tokenizer_a = SoundStream(C=32, D=16, n_q=8, codebook_size=1024).to(device)
    tokenizer_a_path=config["tokenizer_a_path"]
    tokenizer_a_checkpoint = torch.load(tokenizer_a_path, map_location=f"cuda:{device}")
    tokenizer_a.load_state_dict(tokenizer_a_checkpoint["model_state"])
    tokenizer_a.eval()

    latent_size = config['image_size'] // 8

    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
    num_cond = config['context_size']
    model = AVCDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
    
    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
    requires_grad(ema, False)
    
    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
    lr = float(config.get('lr', 1e-4))
    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)


    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
    if bfloat_enable:
        scaler = torch.amp.GradScaler()

    start_epoch, train_steps = load_checkpoint_if_available(
        model, ema, opt, scaler if bfloat_enable else None, config, device, logger, args
    )
        
    # ~40% speedup but might leads to worse performance depending on pytorch version
    if args.torch_compile:
        model = torch.compile(model)
    model = DDP(model, device_ids=[device])
    diffusion = create_diffusion(timestep_respacing="", dual=True)  # default: 1000 steps, linear noise schedule
    # ,predict_xstart=True
    logger.info(f"AVCDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")

    train_dataset = []
    test_dataset = []

    for dataset_name in config["datasets"]:
        data_config = config["datasets"][dataset_name]

        for data_split_type in ["train", "test"]:
            if data_split_type in data_config:
                    goals_per_obs = int(data_config["goals_per_obs"])
                    if data_split_type == 'test':
                        goals_per_obs = 4 # standardize testing
                    
                    if "distance" in data_config:
                        min_dist_cat=data_config["distance"]["min_dist_cat"]
                        max_dist_cat=data_config["distance"]["max_dist_cat"]
                    else:
                        min_dist_cat=config["distance"]["min_dist_cat"]
                        max_dist_cat=config["distance"]["max_dist_cat"]

                    if "len_traj_pred" in data_config:
                        len_traj_pred=data_config["len_traj_pred"]
                    else:
                        len_traj_pred=config["len_traj_pred"]

                    dataset = TrainingDataset(
                        data_folder=data_config["data_folder"],
                        data_split_folder=data_config[data_split_type],
                        dataset_name=dataset_name,
                        image_size=config["image_size"],
                        min_dist_cat=min_dist_cat,
                        max_dist_cat=max_dist_cat,
                        len_traj_pred=len_traj_pred,
                        context_size=config["context_size"],
                        normalize=config["normalize"],
                        goals_per_obs=goals_per_obs,
                        transform=transform,
                        predefined_index=None,
                        traj_stride=1,
                        sample_rate=config["sample_rate"],
                        # target_len=7840 #TODO
                        input_sr=config["input_sr"],
                        evaluate=(data_split_type=="test")
                    )
                    if data_split_type == "train":
                        train_dataset.append(dataset)
                    else:
                        test_dataset.append(dataset)
                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")

    # combine all the datasets from different robots
    print(f"Combining {len(train_dataset)} datasets.")
    train_dataset = ConcatDataset(train_dataset)
    test_dataset = ConcatDataset(test_dataset)

    sampler = DistributedSampler(
        train_dataset,
        num_replicas=dist.get_world_size(),
        rank=rank,
        shuffle=True,
        seed=args.global_seed
    )
    loader = DataLoader(
        train_dataset,
        batch_size=config['batch_size'],
        shuffle=False,
        sampler=sampler,
        num_workers=config['num_workers'],
        pin_memory=True,
        drop_last=True,
        persistent_workers=True
    )
    logger.info(f"Dataset contains {len(train_dataset):,} images")

    # Prepare models for training:
    model.train()  # important! This enables embedding dropout for classifier-free guidance
    ema.eval()  # EMA model should always be in eval mode

    # Variables for monitoring/logging purposes:
    log_steps = 0
    running_loss = 0
    start_time = time()

    logger.info(f"Training for {args.epochs} epochs...")
    for epoch in range(start_epoch, args.epochs):
        sampler.set_epoch(epoch)
        steps_per_epoch = len(loader)
        if rank == 0:
            logger.info(f"Epoch {epoch} contains {steps_per_epoch} steps.")
        logger.info(f"Beginning epoch {epoch}...")

        for x_v, x_a, y, diff, rel_t in loader:
            x_v = x_v.to(device, non_blocking=True)
            x_a = x_a.to(device, non_blocking=True)
            y = y.to(device, non_blocking=True)
            diff = diff.to(device, non_blocking=True)
            rel_t = rel_t.to(device, non_blocking=True)
            
            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
                with torch.no_grad():
                    # Map input images to latent space + normalize latents:
                    B, T = x_v.shape[:2]
                    #=== vision observation encoding
                    x_v = x_v.flatten(0,1)
                    x_v = tokenizer_v.encode(x_v).latent_dist.sample().mul_(0.18215)
                    x_v = x_v.unflatten(0, (B, T))
                    #=== audio observation encoding
                    x_a = x_a.flatten(0,1)
                    x_a = tokenizer_a.encoder(x_a)
                    x_a = x_a.unflatten(0, (B, T))
                
                num_goals = T - num_cond
                #=== split into target and condition
                x_v_start = x_v[:, num_cond:].flatten(0, 1)
                x_v_cond = x_v[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x_v.shape[2], x_v.shape[3], x_v.shape[4]).flatten(0, 1)
                x_a_start = x_a[:, num_cond:].flatten(0, 1)
                x_a_cond = x_a[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x_a.shape[2], x_a.shape[3]).flatten(0, 1)
                #===
                y = y.flatten(0, 1)
                rel_t = rel_t.flatten(0, 1)



                diff = diff.flatten(0, 1)                       # [N, 1]
                diff_tok = diff.unsqueeze(1).expand(-1, 16, -1)  # [N, 64, 1]
                x_a_start = torch.cat([x_a_start, diff_tok], dim=2)  # [N, 64, 181]
                
                t = torch.randint(0, diffusion.num_timesteps, (x_v_start.shape[0],), device=device)
                model_kwargs = dict(y=y, x_v_cond=x_v_cond, x_a_cond=x_a_cond, rel_t=rel_t)
                loss_dict = diffusion.training_losses(model, x_v_start, x_a_start, t, model_kwargs)
                loss = loss_dict["loss"].mean()

            if not bfloat_enable:
                opt.zero_grad()
                loss.backward()
                opt.step()
            else:
                scaler.scale(loss).backward()
                if config.get('grad_clip_val', 0) > 0:
                    scaler.unscale_(opt)
                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
                scaler.step(opt)
                scaler.update()
            
            update_ema(ema, model.module)

            # Log loss values:
            running_loss += loss.detach().item()
            log_steps += 1
            train_steps += 1
            if train_steps % args.log_every == 0:
                # Measure training speed:
                torch.cuda.synchronize()
                end_time = time()
                steps_per_sec = log_steps / (end_time - start_time)
                samples_per_sec = dist.get_world_size()*x_v_cond.shape[0]*steps_per_sec
                # Reduce loss history over all processes:
                avg_loss = torch.tensor(running_loss / log_steps, device=device)
                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
                avg_loss = avg_loss.item() / dist.get_world_size()
                total_steps = len(loader) * args.epochs
                progress_pct = train_steps / total_steps * 100

                remaining_steps = total_steps - train_steps
                eta_seconds = remaining_steps / steps_per_sec if steps_per_sec > 0 else 0
                eta_hours = eta_seconds / 3600

                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
                logger.info(f"Progress: {progress_pct:.2f}% | ETA: {eta_hours:.1f}h")
                # Reset monitoring variables:
                running_loss = 0
                log_steps = 0
                start_time = time()

            # Save DiT checkpoint:
            if train_steps % args.ckpt_every == 0 and train_steps > 0:
                if rank == 0:
                    checkpoint = {
                        "model": model.module.state_dict(),
                        "ema": ema.state_dict(),
                        "opt": opt.state_dict(),
                        "args": args,
                        "epoch": epoch,
                        "train_steps": train_steps
                    }
                    if bfloat_enable:
                        checkpoint.update({"scaler": scaler.state_dict()})
                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
                    torch.save(checkpoint, checkpoint_path)
                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
                        torch.save(checkpoint, checkpoint_path)
                    logger.info(f"Saved checkpoint to {checkpoint_path}")
            
            if train_steps % args.eval_every == 0 and train_steps > 0:
                eval_start_time = time()
                # validation / test set evaluation
                save_dir = os.path.join(experiment_dir, str(train_steps))
                sim_score_val = evaluate(ema, tokenizer_v, tokenizer_a, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond, config["sample_rate"], config["input_sr"], logger)
                dist.barrier()
                eval_end_time = time()
                eval_time = eval_end_time - eval_start_time
                # logger.info(f"(step={train_steps:07d}) Val Perceptual Loss: {sim_score_val:.4f}, Train Perceptual Loss: {sim_score_train:.4f}, Eval Time: {eval_time:.2f}")
                logger.info(f"(step={train_steps:07d}) Val Perceptual Loss: {sim_score_val:.4f}, Eval Time: {eval_time:.2f}")

    model.eval()  # important! This disables randomized embedding dropout
    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...

    logger.info("Done!")
    cleanup()

def denormalize_dis(ndata: float, min_v=-20.0, max_v=20.0, scale=0.15):
    n01 = (float(ndata) + 1.0) / 2.0
    raw = n01 * (max_v - min_v) + min_v
    return raw * scale

@torch.no_grad
def evaluate(model, vae, sstream, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond, sample_rate, input_sr, logger):
    sampler = DistributedSampler(
        test_dataloaders,
        num_replicas=dist.get_world_size(),
        rank=rank,
        shuffle=True,
        seed=seed
    )
    loader = DataLoader(
        test_dataloaders,
        batch_size=batch_size,
        shuffle=False,
        sampler=sampler,
        num_workers=num_workers,
        pin_memory=True,
        drop_last=True
    )
    from dreamsim import dreamsim
    eval_model, _ = dreamsim(pretrained=True)
    score = torch.tensor(0.).to(device)
    n_samples = torch.tensor(0).to(device)

    down_resampler = torchaudio.transforms.Resample(orig_freq=input_sr, new_freq=sample_rate, lowpass_filter_width=64).to(device, dtype=torch.bfloat16)
    mel_tf = build_mel_transform(
        sample_rate=sample_rate,
        n_fft=1024, win_length=1024, hop_length=256,
        n_mels=80, power=1.0,
        device=device,   # or ref.device
    )
    # Run for 1 step
    for x_v, x_a, y, diff, rel_t, x_a_orig in loader:
        x_v = x_v.to(device)
        x_a = x_a.to(device)
        x_a_orig = x_a_orig.to(device)
        y = y.to(device)
        diff = diff.to(device).flatten(0, 1)
        rel_t = rel_t.to(device).flatten(0, 1)
        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
            B, T = x_v.shape[:2]
            num_goals = T - num_cond
            samples_v, samples_a, diff_pred = model_forward_wrapper_av((model, diffusion, vae, sstream), (x_v, x_a), y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t)
            
            samples_a = down_resampler(samples_a) #

            x_start_pixels = x_v[:, num_cond:].flatten(0, 1)
            x_cond_pixels = x_v[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x_v.shape[2], x_v.shape[3], x_v.shape[4]).flatten(0, 1)
            samples_v = samples_v * 0.5 + 0.5
            x_start_pixels = x_start_pixels * 0.5 + 0.5
            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
            res = eval_model(x_start_pixels, samples_v)
            score += res.sum()
            n_samples += len(res)

            # x_start_audio = x_a[:, num_cond:].flatten(0, 1)
            # x_cond_audio = x_a[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x_a.shape[2], x_a.shape[3]).flatten(0, 1)
            x_start_audio = x_a_orig[:, num_cond:].flatten(0, 1)
            x_cond_audio = x_a_orig[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x_a_orig.shape[2], x_a_orig.shape[3]).flatten(0, 1)
        break
    
    if rank == 0:
        os.makedirs(save_dir, exist_ok=True)

        if diff is not None:
            mae = torch.mean(torch.abs(diff_pred - diff))
            logger.info(f"Distance Diff MAE = {mae.item():.6f}")

        mel_cosine_ls=[]
        for i in range(min(samples_v.shape[0], 10)):
            _, ax = plt.subplots(1,3,dpi=256)
            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
            ax[2].imshow((samples_v[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
            plt.savefig(f'{save_dir}/{i}.png')
            plt.close()


            mel_cos = mel_cosine_stereo(x_start_audio[i], samples_a[i], sample_rate=sample_rate, mel_tf=mel_tf)
            mel_cosine_ls.append(mel_cos)
            ok = save_ref_hat_spectrogram_panel(
                x_start_audio[i], samples_a[i],
                out_path=f"{save_dir}/{i}_spectrograms.png",
                n_fft=512, hop_length=160, win_length=400, pool=4,
                title="gt vs pred"
            )

            # sr = int(16000 * 7840 / 2400) #TODO
            torchaudio.save(f"{save_dir}/{i}_gen.wav", samples_a[i].cpu().to(torch.float32), sample_rate=sample_rate)
            torchaudio.save(f"{save_dir}/{i}_gt.wav", x_start_audio[i].cpu().to(torch.float32), sample_rate=sample_rate)
            torchaudio.save(f"{save_dir}/{i}_cond.wav", x_cond_audio[i, -1].cpu().to(torch.float32), sample_rate=sample_rate)
        logger.info("the first 10  mel cosine: " + ", ".join(f"{v:.6f}" for v in mel_cosine_ls))


    dist.all_reduce(score)
    dist.all_reduce(n_samples)
    sim_score = score/n_samples
    return sim_score


def get_args_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", type=str, required=True)
    parser.add_argument("--epochs", type=int, default=300)
    parser.add_argument("--global-seed", type=int, default=0)
    parser.add_argument("--log-every", type=int, default=100)
    parser.add_argument("--ckpt-every", type=int, default=2000)
    parser.add_argument("--eval-every", type=int, default=5000)
    parser.add_argument("--bfloat16", type=int, default=1)
    parser.add_argument("--torch-compile", type=int, default=1)
    parser.add_argument("--restart-from-checkpoint", type=int, default=0,
                    help="If 1, only load model weights and reset epoch/step to zero (cold start)")
    return parser

if __name__ == "__main__":
    args = get_args_parser().parse_args()
    main(args)