| """ |
| Make model classes |
| Load from checkpoints |
| Test on dummy outputs to see if everything matches |
| """ |
| import os |
| import numpy as np |
| import torch as t |
| import jukebox.utils.dist_adapter as dist |
| from jukebox.hparams import Hyperparams, setup_hparams, REMOTE_PREFIX |
| from jukebox.utils.remote_utils import download |
| from jukebox.utils.torch_utils import freeze_model |
| from jukebox.utils.dist_utils import print_all |
| from jukebox.vqvae.vqvae import calculate_strides |
| import fire |
|
|
| MODELS = { |
| '5b': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_5b"), |
| '5b_lyrics': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_5b_lyrics"), |
| '1b_lyrics': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_1b_lyrics"), |
| |
| } |
|
|
| def load_checkpoint(path): |
| restore = path |
| if restore.startswith(REMOTE_PREFIX): |
| remote_path = restore |
| local_path = os.path.join(os.path.expanduser("~/.cache"), remote_path[len(REMOTE_PREFIX):]) |
| if dist.get_rank() % 8 == 0: |
| print("Downloading from azure") |
| if not os.path.exists(os.path.dirname(local_path)): |
| os.makedirs(os.path.dirname(local_path)) |
| if not os.path.exists(local_path): |
| download(remote_path, local_path) |
| restore = local_path |
| dist.barrier() |
| checkpoint = t.load(restore, map_location=t.device('cpu')) |
| print("Restored from {}".format(restore)) |
| return checkpoint |
|
|
| def save_checkpoint(logger, name, model, opt, metrics, hps): |
| with t.no_grad(): |
| save_hps = {**hps} |
| save_hps = {k: v for k,v in save_hps.items() if k not in ['metadata_v2','metadata_v3', 'alignments', 'lyric_processor', 'midi_processor']} |
| t.save({'hps': save_hps, |
| 'model': model.state_dict(), |
| 'opt': opt.state_dict() if opt is not None else None, |
| 'step': logger.iters, |
| **metrics}, f'{logger.logdir}/checkpoint_{name}.pth.tar') |
| return |
|
|
| def restore_model(hps, model, checkpoint_path): |
| model.step = 0 |
| if checkpoint_path != '': |
| checkpoint = load_checkpoint(checkpoint_path) |
| |
| |
| |
| |
| checkpoint['model'] = {k[7:] if k[:7] == 'module.' else k: v for k, v in checkpoint['model'].items()} |
| model.load_state_dict(checkpoint['model']) |
| if 'step' in checkpoint: model.step = checkpoint['step'] |
|
|
| def restore_opt(opt, shd, checkpoint_path): |
| if not checkpoint_path: |
| return |
| checkpoint = load_checkpoint(checkpoint_path) |
| if "opt" in checkpoint: |
| opt.load_state_dict(checkpoint['opt']) |
| if "step" in checkpoint: |
| shd.step(checkpoint['step']) |
|
|
| def make_vqvae(hps, device='cuda'): |
| from jukebox.vqvae.vqvae import VQVAE |
| block_kwargs = dict(width=hps.width, depth=hps.depth, m_conv=hps.m_conv, |
| dilation_growth_rate=hps.dilation_growth_rate, |
| dilation_cycle=hps.dilation_cycle, |
| reverse_decoder_dilation=hps.vqvae_reverse_decoder_dilation) |
|
|
| if not hps.sample_length: |
| assert hps.sample_length_in_seconds != 0 |
| downsamples = calculate_strides(hps.strides_t, hps.downs_t) |
| top_raw_to_tokens = np.prod(downsamples) |
| hps.sample_length = (hps.sample_length_in_seconds * hps.sr // top_raw_to_tokens) * top_raw_to_tokens |
| print(f"Setting sample length to {hps.sample_length} (i.e. {hps.sample_length/hps.sr} seconds) to be multiple of {top_raw_to_tokens}") |
|
|
| vqvae = VQVAE(input_shape=(hps.sample_length,1), levels=hps.levels, downs_t=hps.downs_t, strides_t=hps.strides_t, |
| emb_width=hps.emb_width, l_bins=hps.l_bins, |
| mu=hps.l_mu, commit=hps.commit, |
| spectral=hps.spectral, multispectral=hps.multispectral, |
| multipliers=hps.hvqvae_multipliers, use_bottleneck=hps.use_bottleneck, |
| **block_kwargs) |
|
|
| vqvae = vqvae.to(device) |
| restore_model(hps, vqvae, hps.restore_vqvae) |
| if hps.train and not hps.prior: |
| print_all(f"Loading vqvae in train mode") |
| if hps.restore_vqvae != '': |
| print_all("Reseting bottleneck emas") |
| for level, bottleneck in enumerate(vqvae.bottleneck.level_blocks): |
| num_samples = hps.sample_length |
| downsamples = calculate_strides(hps.strides_t, hps.downs_t) |
| raw_to_tokens = np.prod(downsamples[:level + 1]) |
| num_tokens = (num_samples // raw_to_tokens) * dist.get_world_size() |
| bottleneck.restore_k(num_tokens=num_tokens, threshold=hps.revival_threshold) |
| else: |
| print_all(f"Loading vqvae in eval mode") |
| vqvae.eval() |
| freeze_model(vqvae) |
| return vqvae |
|
|
| def make_prior(hps, vqvae, device='cuda'): |
| from jukebox.prior.prior import SimplePrior |
|
|
| prior_kwargs = dict(input_shape=(hps.n_ctx,), bins=vqvae.l_bins, |
| width=hps.prior_width, depth=hps.prior_depth, heads=hps.heads, |
| attn_order=hps.attn_order, blocks=hps.blocks, spread=hps.spread, |
| attn_dropout=hps.attn_dropout, resid_dropout=hps.resid_dropout, emb_dropout=hps.emb_dropout, |
| zero_out=hps.zero_out, res_scale=hps.res_scale, pos_init=hps.pos_init, |
| init_scale=hps.init_scale, |
| m_attn=hps.m_attn, m_mlp=hps.m_mlp, |
| checkpoint_res=hps.c_res if hps.train else 0, checkpoint_attn=hps.c_attn if hps.train else 0, checkpoint_mlp=hps.c_mlp if hps.train else 0) |
|
|
| x_cond_kwargs = dict(out_width=hps.prior_width, init_scale=hps.init_scale, |
| width=hps.cond_width, depth=hps.cond_depth, m_conv=hps.cond_m_conv, |
| dilation_growth_rate=hps.cond_dilation_growth_rate, dilation_cycle=hps.cond_dilation_cycle, |
| zero_out=hps.cond_zero_out, res_scale=hps.cond_res_scale, |
| checkpoint_res=hps.cond_c_res) |
|
|
| y_cond_kwargs = dict(out_width=hps.prior_width, init_scale=hps.init_scale, |
| y_bins=hps.y_bins, t_bins=hps.t_bins, sr= hps.sr, min_duration=hps.min_duration, |
| max_duration=hps.max_duration, max_bow_genre_size=hps.max_bow_genre_size) |
|
|
| if hps.use_tokens and not hps.single_enc_dec: |
| prime_kwargs = dict(use_tokens=hps.use_tokens, prime_loss_fraction=hps.prime_loss_fraction, |
| n_tokens=hps.n_tokens, bins=hps.n_vocab, |
| width=hps.prime_width, depth=hps.prime_depth, heads=hps.prime_heads, |
| attn_order=hps.prime_attn_order, blocks=hps.prime_blocks, spread=hps.prime_spread, |
| attn_dropout=hps.prime_attn_dropout, resid_dropout=hps.prime_resid_dropout, |
| emb_dropout=hps.prime_emb_dropout, |
| zero_out=hps.prime_zero_out, res_scale=hps.prime_res_scale, |
| pos_init=hps.prime_pos_init, init_scale=hps.prime_init_scale, |
| m_attn=hps.prime_m_attn, m_mlp=hps.prime_m_mlp, |
| checkpoint_res=hps.prime_c_res if hps.train else 0, checkpoint_attn=hps.prime_c_attn if hps.train else 0, |
| checkpoint_mlp=hps.prime_c_mlp if hps.train else 0) |
| else: |
| prime_kwargs = dict(use_tokens=hps.use_tokens, prime_loss_fraction=hps.prime_loss_fraction, |
| n_tokens=hps.n_tokens, bins=hps.n_vocab) |
|
|
| |
| rescale = lambda z_shape: (z_shape[0]*hps.n_ctx//vqvae.z_shapes[hps.level][0],) |
| z_shapes = [rescale(z_shape) for z_shape in vqvae.z_shapes] |
|
|
| prior = SimplePrior(z_shapes=z_shapes, |
| l_bins=vqvae.l_bins, |
| encoder=vqvae.encode, |
| decoder=vqvae.decode, |
| level=hps.level, |
| downs_t=vqvae.downs_t, |
| strides_t=vqvae.strides_t, |
| labels=hps.labels, |
| prior_kwargs=prior_kwargs, |
| x_cond_kwargs=x_cond_kwargs, |
| y_cond_kwargs=y_cond_kwargs, |
| prime_kwargs=prime_kwargs, |
| copy_input=hps.copy_input, |
| labels_v3=hps.labels_v3, |
| merged_decoder=hps.merged_decoder, |
| single_enc_dec=hps.single_enc_dec) |
|
|
| prior.alignment_head = hps.get('alignment_head', None) |
| prior.alignment_layer = hps.get('alignment_layer', None) |
|
|
| if hps.fp16_params: |
| print_all("Converting to fp16 params") |
| from jukebox.transformer.ops import _convert_conv_weights_to_fp16 |
| prior.apply(_convert_conv_weights_to_fp16) |
| prior = prior.to(device) |
| restore_model(hps, prior, hps.restore_prior) |
| if hps.train: |
| print_all(f"Loading prior in train mode") |
| pass |
| else: |
| print_all(f"Loading prior in eval mode") |
| prior.eval() |
| freeze_model(prior) |
| return prior |
|
|
| def make_model(model, device, hps, levels=None): |
| vqvae, *priors = MODELS[model] |
| vqvae = make_vqvae(setup_hparams(vqvae, dict(sample_length=hps.get('sample_length', 0), sample_length_in_seconds=hps.get('sample_length_in_seconds', 0))), device) |
| hps.sample_length = vqvae.sample_length |
| if levels is None: |
| levels = range(len(priors)) |
| priors = [make_prior(setup_hparams(priors[level], dict()), vqvae, 'cpu') for level in levels] |
| return vqvae, priors |
|
|
| def save_outputs(model, device, hps): |
| |
| if hps.labels_v3: |
| n_ctx = 6144 |
| n_tokens = 384 |
| prime_bins = 79 |
| else: |
| n_ctx = 8192 |
| n_tokens = 512 |
| prime_bins = 80 |
|
|
| rng = t.random.manual_seed(0) |
| x = 2 * t.rand((1, n_ctx * 8 * 4 * 4, 1), generator=rng, dtype=t.float).cuda() - 1.0 |
| lyric_tokens = t.randint(0, prime_bins, (1, n_tokens), generator=rng, dtype=t.long).view(-1).numpy() |
| artist_id = 10 |
| genre_ids = [1] |
| total_length = 2 * 2646000 |
| offset = 2646000 |
|
|
| vqvae, priors = make_model(model, device, hps) |
|
|
| |
| vq_prior = priors[-1] |
| zs = vq_prior.encode(x, start_level=0) |
| x_ds = [vq_prior.decode(zs[level:], start_level=level) for level in range(0, len(zs))] |
|
|
| |
| data = dict(zs=zs, x_ds=x_ds) |
| for level in range(len(priors)): |
| print(f"Doing level {level}") |
| if hps.labels_v3 and level != hps.levels - 1: |
| print(f"Skipping level {level}") |
| continue |
| prior = priors[level] |
| prior.cuda() |
| x_in = x[:, :n_ctx * 8 * (4 ** level)] |
| y_in = t.from_numpy(prior.labeller.get_y_from_ids(artist_id, genre_ids, lyric_tokens, total_length, offset)).view(1, -1).cuda().long() |
| x_out, _, metrics = prior(x_in, y_in, fp16=hps.fp16, get_preds=True, decode=True) |
| preds = metrics['preds'] |
| data[level] = dict(x=x_in, y=y_in, x_out=x_out, preds=preds) |
| prior.cpu() |
| t.save(data, 'data.pth.tar') |
| dist.barrier() |
| print("Saved data") |
| exit() |
|
|
|
|
| def run(model, port=29500, **kwargs): |
| from jukebox.utils.dist_utils import setup_dist_from_mpi |
| rank, local_rank, device = setup_dist_from_mpi(port=port) |
| hps = Hyperparams(**kwargs) |
|
|
| with t.no_grad(): |
| save_outputs(model, device, hps) |
|
|
| if __name__ == '__main__': |
| fire.Fire(run) |
|
|
