| import itertools |
| import pdb |
| from dataclasses import dataclass |
| import hydra.utils |
| import lightning as L |
| import numpy as np |
| import torch |
| import torch.nn.functional as F |
| import transformers |
|
|
| import dataloader |
| import metrics |
| import models |
| import utils |
|
|
| import time |
| @dataclass |
| class Loss: |
| loss: torch.FloatTensor |
| nlls: torch.FloatTensor |
| prior_loss: torch.FloatTensor |
| num_tokens: torch.FloatTensor |
|
|
|
|
| class LogLinear(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.eps = 1e-3 |
|
|
| def forward(self, t): |
| t = (1 - self.eps) * t |
| alpha_t = 1 - t |
| dalpha_t = -(1 - self.eps) |
| return dalpha_t, alpha_t |
| |
| def sample_categorical(categorical_probs): |
| gumbel_norm = 1e-10 - (torch.rand_like(categorical_probs) + 1e-10).log() |
| return (categorical_probs / gumbel_norm).argmax(dim=-1) |
|
|
|
|
| def _unsqueeze(x, reference): |
| return x.view(*x.shape, *((1,) * (len(reference.shape) - len(x.shape)))) |
|
|
|
|
| class TrainerBase(L.LightningModule): |
| def __init__( |
| self, config, tokenizer: transformers.PreTrainedTokenizer, vocab_size=None |
| ): |
| super().__init__() |
| self.save_hyperparameters() |
| self.config = config |
| if hasattr(self.config.algo, "ignore_bos"): |
| self.ignore_bos = config.algo.ignore_bos |
| else: |
| self.ignore_bos = False |
| if hasattr(self.config.algo, "loss_type"): |
| self.loss_type = config.algo.loss_type |
| self.tokenizer = tokenizer |
| if vocab_size is None: |
| if hasattr(self.config.data, 'vocab_file'): |
| self.vocab_size = len(self.tokenizer.added_tokens_decoder) |
| self.token_offset = True |
| else: |
| self.token_offset = False |
| self.vocab_size = len(self.tokenizer) |
| else: |
| self.vocab_size = vocab_size |
|
|
| self.sampler = self.config.sampling.predictor |
| self.antithetic_sampling = self.config.training.antithetic_sampling |
| self.parameterization = self.config.algo.parameterization |
| if self.config.algo.backbone == "dit": |
| self.backbone = models.dit.DIT(self.config, vocab_size=self.vocab_size) |
|
|
| elif self.config.algo.backbone == "cont_dit": |
| backbone = models.dit_cont.ContDIT(self.config.model, vocab_size=self.vocab_size, mixed_coeff=self.config.algo.mixed_coeff) |
| self.backbone = backbone |
| elif self.config.algo.backbone == "dimamba": |
| self.backbone = models.dimamba.DiMamba( |
| self.config, |
| vocab_size=self.vocab_size, |
| pad_token_id=self.tokenizer.pad_token_id, |
| ) |
| elif self.config.algo.backbone == "hf_dit": |
| self.backbone = transformers.AutoModelForMaskedLM.from_pretrained( |
| config.eval.checkpoint_path, trust_remote_code=True |
| ) |
|
|
|
|
| self.T = self.config.algo.T |
| self.num_tokens = self.config.model.length |
| self.softplus = torch.nn.Softplus() |
| self.p_nucleus = self.config.sampling.p_nucleus |
| |
| self.noise = LogLinear() |
|
|
| self.metrics = metrics.Metrics( |
| gen_ppl_eval_model_name_or_path=self.config.eval.gen_ppl_eval_model_name_or_path, |
| eval_ppl_batch_size=self.config.eval.perplexity_batch_size, |
| ) |
|
|
| if self.config.training.ema > 0: |
| self.ema = models.ema.ExponentialMovingAverage( |
| self._get_parameters(), decay=self.config.training.ema |
| ) |
| else: |
| self.ema = None |
|
|
| self.lr = self.config.optim.lr |
| self.sampling_eps = self.config.training.sampling_eps |
| self.time_conditioning = self.config.algo.time_conditioning |
| self.neg_infinity = -1000000.0 |
| self.fast_forward_epochs = None |
| self.fast_forward_batches = None |
|
|
| def _validate_configuration(self): |
| assert self.config.algo.backbone in {"dit", "hf_dit"} |
| if self.config.algo.parameterization == "ar": |
| assert not self.config.algo.time_conditioning |
| assert self.config.prior.type == "none" |
|
|
| if self.parameterization in {"score", "mean"}: |
| assert self.time_conditioning |
| if self.T > 0: |
| assert self.parameterization != "score" |
|
|
| def to(self, *args, **kwargs): |
| self = super().to(*args, **kwargs) |
| self.metrics.to(*args, **kwargs) |
| return self |
|
|
| def q_xt(self, x, alpha_t): |
| raise NotImplementedError |
|
|
| def _get_parameters(self): |
| return itertools.chain(self.backbone.parameters(), self.noise.parameters()) |
|
|
| def _eval_mode(self): |
| if self.ema: |
| self.ema.store(self._get_parameters()) |
| self.ema.copy_to(self._get_parameters()) |
| self.backbone.eval() |
| self.noise.eval() |
|
|
| def _train_mode(self): |
| if self.ema: |
| self.ema.restore(self._get_parameters()) |
| self.backbone.train() |
| self.noise.train() |
|
|
| def on_load_checkpoint(self, checkpoint): |
| if self.ema: |
| self.ema.load_state_dict(checkpoint["ema"]) |
| |
| |
| self.fast_forward_epochs = checkpoint["loops"]["fit_loop"]["epoch_progress"][ |
| "current" |
| ]["completed"] |
| self.fast_forward_batches = checkpoint["loops"]["fit_loop"][ |
| "epoch_loop.batch_progress" |
| ]["current"]["completed"] |
|
|
| if 'state_dict' in checkpoint: |
| state_dict = checkpoint['state_dict'] |
| cleaned_state_dict = {} |
| |
| for key, value in state_dict.items(): |
| if "teacher" not in key: |
| cleaned_key = key |
| |
| cleaned_key = cleaned_key.replace('_orig_mod.', '') |
| cleaned_state_dict[cleaned_key] = value |
| |
| checkpoint['state_dict'] = cleaned_state_dict |
|
|
|
|
| def on_save_checkpoint(self, checkpoint): |
| if self.ema: |
| checkpoint["ema"] = self.ema.state_dict() |
| |
| |
| |
| |
| checkpoint["loops"]["fit_loop"]["epoch_loop.batch_progress"]["total"][ |
| "completed" |
| ] = ( |
| checkpoint["loops"]["fit_loop"][ |
| "epoch_loop.automatic_optimization.optim_progress" |
| ]["optimizer"]["step"]["total"]["completed"] |
| * self.trainer.accumulate_grad_batches |
| ) |
| checkpoint["loops"]["fit_loop"]["epoch_loop.batch_progress"]["current"][ |
| "completed" |
| ] = ( |
| checkpoint["loops"]["fit_loop"][ |
| "epoch_loop.automatic_optimization.optim_progress" |
| ]["optimizer"]["step"]["current"]["completed"] |
| * self.trainer.accumulate_grad_batches |
| ) |
| |
| |
| |
| checkpoint["loops"]["fit_loop"]["epoch_loop.state_dict"][ |
| "_batches_that_stepped" |
| ] = checkpoint["loops"]["fit_loop"][ |
| "epoch_loop.automatic_optimization.optim_progress" |
| ][ |
| "optimizer" |
| ][ |
| "step" |
| ][ |
| "total" |
| ][ |
| "completed" |
| ] |
| if "sampler" not in checkpoint.keys(): |
| checkpoint["sampler"] = {} |
| if hasattr(self.trainer.train_dataloader.sampler, "state_dict"): |
| sampler_state_dict = self.trainer.train_dataloader.sampler.state_dict() |
| checkpoint["sampler"]["random_state"] = sampler_state_dict.get( |
| "random_state", None |
| ) |
| else: |
| checkpoint["sampler"]["random_state"] = None |
|
|
| def on_train_start(self): |
| if self.ema: |
| self.ema.move_shadow_params_to_device(self.device) |
| |
| |
| distributed = ( |
| self.trainer._accelerator_connector.use_distributed_sampler |
| and self.trainer._accelerator_connector.is_distributed |
| ) |
| if distributed: |
| sampler_cls = dataloader.FaultTolerantDistributedSampler |
| else: |
| sampler_cls = dataloader.RandomFaultTolerantSampler |
| updated_dls = [] |
| for dl in self.trainer.fit_loop._combined_loader.flattened: |
| if hasattr(dl.sampler, "shuffle"): |
| dl_sampler = sampler_cls(dl.dataset, shuffle=dl.sampler.shuffle) |
| else: |
| dl_sampler = sampler_cls(dl.dataset) |
| if ( |
| distributed |
| and self.fast_forward_epochs is not None |
| and self.fast_forward_batches is not None |
| ): |
| dl_sampler.load_state_dict( |
| { |
| "epoch": self.fast_forward_epochs, |
| "counter": ( |
| self.fast_forward_batches * self.config.loader.batch_size |
| ), |
| } |
| ) |
| updated_dls.append( |
| torch.utils.data.DataLoader( |
| dl.dataset, |
| batch_size=self.config.loader.batch_size, |
| num_workers=self.config.loader.num_workers, |
| pin_memory=self.config.loader.pin_memory, |
| sampler=dl_sampler, |
| shuffle=False, |
| persistent_workers=True, |
| ) |
| ) |
| self.trainer.fit_loop._combined_loader.flattened = updated_dls |
|
|
| def optimizer_step(self, *args, **kwargs): |
| super().optimizer_step(*args, **kwargs) |
| if self.ema: |
| self.ema.update(self._get_parameters()) |
|
|
| def _process_sigma(self, sigma): |
| raise NotImplementedError |
|
|
| def _process_model_output(self, model_output, xt, sigma): |
| raise NotImplementedError |
|
|
| def forward(self, xt, sigma): |
| sigma = self._process_sigma(sigma) |
| with torch.cuda.amp.autocast(dtype=torch.float32): |
| model_output = self.backbone(xt, sigma) |
| return self._process_model_output(model_output=model_output, xt=xt, sigma=sigma) |
|
|
| def on_train_epoch_start(self): |
| self.metrics.reset() |
| assert self.metrics.train_nlls.nll.mean_value == 0 |
| assert self.metrics.train_nlls.nll.weight == 0 |
|
|
| def training_step(self, batch, batch_idx): |
|
|
| current_accumulation_step = batch_idx % self.trainer.accumulate_grad_batches |
| losses = self._loss( |
| batch["input_ids"], |
| batch["attention_mask"], |
| current_accumulation_step, |
| train_mode=True, |
| ) |
| self.metrics.update_train(losses.nlls, losses.prior_loss, losses.num_tokens) |
| self.log( |
| name="trainer/loss", |
| value=losses.loss.item(), |
| on_step=True, |
| on_epoch=False, |
| sync_dist=True, |
| ) |
| return losses.loss |
|
|
| def on_train_epoch_end(self): |
| for k, v in self.metrics.valid_nlls.items(): |
| self.log( |
| name=k, value=v.compute(), on_step=False, on_epoch=True, sync_dist=True |
| ) |
|
|
| def on_validation_epoch_start(self): |
| self.metrics.reset() |
| self._eval_mode() |
| assert self.metrics.valid_nlls.nll.mean_value == 0 |
| assert self.metrics.valid_nlls.nll.weight == 0 |
|
|
| def validation_step(self, batch, batch_idx): |
|
|
| del batch_idx |
| losses = self._loss(batch["input_ids"], batch["attention_mask"]) |
| self.metrics.update_valid(losses.nlls, losses.prior_loss, losses.num_tokens) |
| return losses.loss |
|
|
| def on_validation_epoch_end(self): |
| for k, v in self.metrics.valid_nlls.items(): |
| self.log( |
| name=k, value=v.compute(), on_step=False, on_epoch=True, sync_dist=True |
| ) |
| if ( |
| self.config.eval.compute_perplexity_on_sanity |
| or not self.trainer.sanity_checking |
| ) and self.config.eval.generate_samples: |
| samples, text_samples = None, None |
| for _ in range(self.config.sampling.num_sample_batches): |
| samples = self.generate_samples( |
| num_samples=self.config.loader.eval_batch_size |
| ) |
|
|
| self.metrics.record_entropy(samples) |
| |
| text_samples = self.tokenizer.batch_decode(samples) |
| if self.config.eval.compute_generative_perplexity: |
| self.metrics.record_generative_perplexity( |
| text_samples, self.num_tokens, self.device |
| ) |
| if text_samples is not None: |
| if self.trainer.global_rank == 0 and hasattr( |
| self.trainer.logger, "log_table" |
| ): |
| |
| text_samples = text_samples[: self.config.sampling.num_sample_log] |
| self.trainer.logger.log_table( |
| key=f"samples@global_step{self.global_step}", |
| columns=["Generated Samples"], |
| data=[[s] for s in text_samples], |
| ) |
| if self.config.eval.compute_generative_perplexity: |
| self.log( |
| "val/gen_ppl", |
| self.metrics.gen_ppl.compute(), |
| on_epoch=True, |
| on_step=False, |
| sync_dist=True, |
| ) |
| self.log( |
| "val/sample_entropy", |
| self.metrics.sample_entropy.compute(), |
| on_epoch=True, |
| on_step=False, |
| sync_dist=True, |
| ) |
| self._train_mode() |
|
|
| def configure_optimizers(self): |
| optimizer = torch.optim.AdamW( |
| self._get_parameters(), |
| lr=self.config.optim.lr, |
| betas=(self.config.optim.beta1, self.config.optim.beta2), |
| eps=self.config.optim.eps, |
| weight_decay=self.config.optim.weight_decay, |
| ) |
|
|
| scheduler = hydra.utils.instantiate( |
| self.config.lr_scheduler, optimizer=optimizer |
| ) |
| scheduler_dict = { |
| "scheduler": scheduler, |
| "interval": "step", |
| "monitor": "val/loss", |
| "name": "trainer/lr", |
| } |
| return [optimizer], [scheduler_dict] |
|
|
| def generate_samples(self, num_samples, num_steps, eps): |
| raise NotImplementedError |
|
|
| def restore_model_and_sample(self, num_steps, eps=1e-5): |
| """Generate samples from the model.""" |
| |
| self._eval_mode() |
| start = time.time() |
| samples = self.generate_samples( |
| num_samples=self.config.loader.eval_batch_size, num_steps=num_steps, eps=eps |
| ) |
| end = time.time() |
| self._train_mode() |
| return samples |
|
|
| def _process_model_input(self, x0, valid_tokens): |
| raise NotImplementedError |
|
|
| def nll( |
| self, |
| input_tokens, |
| output_tokens, |
| current_accumulation_step=None, |
| train_mode=False, |
| ): |
| raise NotImplementedError |
|
|
| def _loss(self, x0, valid_tokens, current_accumulation_step=None, train_mode=False): |
| (input_tokens, output_tokens, valid_tokens) = self._process_model_input( |
| x0, valid_tokens |
| ) |
| loss = self.nll( |
| input_tokens, output_tokens, current_accumulation_step, train_mode |
| ) |
| assert loss.ndim == 2 |
| if self.ignore_bos: |
| loss[:, 1:] = loss[:, 1:] |
| valid_tokens[:, 1:] = valid_tokens[:, 1:] |
|
|
| nlls = (loss * valid_tokens).sum() |
| num_tokens = valid_tokens.sum() |
| token_nll = nlls / num_tokens |
|
|
| return Loss(loss=token_nll, nlls=nlls, prior_loss=0.0, num_tokens=num_tokens) |
|
|
|
|
| class Diffusion(TrainerBase): |
| def _validate_configuration(self): |
| super()._validate_configuration() |
| assert self.config.sampling.noise_removal in {"none", "ancestral", "greedy"} |
| assert self.loss_type in {"elbo", "low_var"} |
| if self.config.sampling.noise_removal == "greedy": |
| assert self.sampler != "analytic" |
| assert self.parameterization in {"mean", "subs"} |
|
|
| def _process_model_input(self, x0, valid_tokens): |
| return x0, None, valid_tokens |
|
|
| def _process_sigma(self, sigma): |
| assert sigma.ndim == 2 |
| sigma = sigma.mean(-1).squeeze() |
| if sigma.ndim == 0: |
| sigma = sigma.unsqueeze(0) |
| if not self.time_conditioning: |
| sigma = torch.zeros_like(sigma) |
| assert sigma.ndim == 1, sigma.shape |
| return sigma |
|
|
| def _sample_t(self, n, accum_step): |
| if accum_step is not None: |
| |
| batch_dim = n |
| n = self.config.loader.global_batch_size |
| _eps_t = torch.rand(n, device=self.device) |
| if self.antithetic_sampling: |
| offset = torch.arange(n, device=self.device) / n |
| _eps_t = (_eps_t / n + offset) % 1 |
| t = (1 - self.sampling_eps) * _eps_t + self.sampling_eps |
| if accum_step is not None: |
| t = t.chunk(self.trainer.num_nodes)[self.trainer.node_rank] |
| t = t.chunk(self.trainer.num_devices)[self.trainer.local_rank] |
| t = t.chunk(self.trainer.accumulate_grad_batches)[accum_step] |
| |
| t = t[:batch_dim] |
| return t |
|
|
| def _sigma_from_alphat(self, alpha_t): |
| return -torch.log(alpha_t) |
|
|
| def _reconstruction_loss(self, x0): |
| t0 = torch.zeros(1, x0.shape[0], dtype=self.dtype, device=self.device) |
| sigma_t0 = self._sigma_from_alphat(self.noise(t0)[1]) |
| model_output_t0 = self.forward(x0, sigma_t0) |
| return -torch.gather( |
| input=model_output_t0, dim=-1, index=x0[:, :, None] |
| ).squeeze(-1) |
|
|
| def nll_per_token(self, model_output, xt, x0, alpha_t, dalpha_t, low_var): |
| raise NotImplementedError |
|
|
| def nll(self, x0, output_tokens, current_accumulation_step=None, train_mode=False): |
| del output_tokens |
| t = self._sample_t(x0.shape[0], current_accumulation_step) |
| assert t.shape[0] == x0.shape[0] |
| if self.T > 0: |
| t = (t * self.T).to(torch.int) |
| t = t / self.T |
| |
| t += 1 / self.T |
|
|
| dalpha_t, alpha_t = self.noise(t) |
| alpha_t = alpha_t.unsqueeze(-1) |
| assert alpha_t.ndim == 2 |
| sigma = self._sigma_from_alphat(alpha_t) |
|
|
| xt = self.q_xt(x0, alpha_t) |
|
|
| log_x_theta = self.forward(xt, sigma=sigma) |
| utils.print_nans(log_x_theta, "model_output") |
| return self.nll_per_token( |
| log_x_theta=log_x_theta, |
| xt=xt, |
| x0=x0, |
| alpha_t=alpha_t, |
| dalpha_t=dalpha_t, |
| low_var=train_mode and self.loss_type == "low_var", |
| ) |
|
|
| def _get_score(self, **kwargs): |
| del kwargs |
| raise NotImplementedError |
|
|
| def _denoiser_update(self, x, t): |
| raise NotImplementedError |
|
|
| def _analytic_update(self, x, t, dt): |
| raise NotImplementedError |
|
|
| def _ancestral_update(self, x, t, dt, p_x0, noise_removal_step): |
| raise NotImplementedError |
|
|
| @torch.no_grad() |
| def generate_samples(self, num_samples, num_steps=None, eps=1e-5, prompt_mask=None, prompt_tokens=None): |
| """Generate samples from the model.""" |
| |
| if num_steps is None: |
| num_steps = self.config.sampling.steps |
| x = self.prior_sample(num_samples, self.num_tokens) |
| if prompt_tokens is not None and prompt_mask is not None: |
|
|
| |
| x = (prompt_mask * prompt_tokens + (1 - prompt_mask) * x).long() |
| |
|
|
| timesteps = torch.linspace(1, eps, num_steps + 1, device=self.device) |
| dt = (1 - eps) / num_steps |
| p_x0_cache = None |
| for i in range(num_steps): |
| t = timesteps[i] * torch.ones(x.shape[0], 1, device=self.device) |
| if self.sampler == "ancestral": |
| |
| |
|
|
| _, x = self._ancestral_update(x=x, t=t, dt=dt, p_x0=None, prompt_mask=prompt_mask) |
| elif self.sampler == "ancestral_cache": |
| p_x0_cache, x_next = self._ancestral_update( |
| x=x, t=t, dt=dt, p_x0=p_x0_cache |
| ) |
| if not torch.allclose(x_next, x) or self.time_conditioning: |
| |
| p_x0_cache = None |
| x = x_next |
|
|
|
|
| else: |
| x = self._analytic_update(x=x, t=t, dt=dt) |
|
|
| t0 = timesteps[-1] * torch.ones(x.shape[0], 1, device=self.device) |
| if self.config.sampling.noise_removal == "ancestral": |
| if self.sampler == "analytic": |
| x = self._denoiser_update(x=x, t=t0) |
| else: |
| _, x = self._ancestral_update( |
| x=x, t=t0, dt=None, p_x0=p_x0_cache, noise_removal_step=True, prompt_mask=prompt_mask |
| ) |
| elif self.config.sampling.noise_removal == "greedy": |
| sigma = self._sigma_from_alphat(self.noise(t0)[1]) |
| x = self.forward(xt=x, sigma=sigma).argmax(dim=-1) |
|
|
| if self.token_offset: |
| x += self.tokenizer.eos_token_id |
| return x |
|
|
| @torch.no_grad |
| def _semi_ar_sampler(self, n_samples, stride_length, num_strides, dt=0.001): |
| |
| ones = torch.ones(n_samples, dtype=self.dtype, device=self.device) |
|
|
| num_steps = int(1 / dt) |
| sampling_steps = 0 |
| intermediate_tokens = [] |
| target = None |
| for _ in range(num_strides + 1): |
| p_x0_cache = None |
| x = self.prior_sample(n_samples, self.num_tokens) |
| if target is not None: |
| x[:, :-stride_length] = target |
| for i in range(num_steps + 1): |
| p_x0_cache, x_next = self._ancestral_update( |
| x=x, t=(1 - i * dt) * ones, dt=dt, p_x0=p_x0_cache |
| ) |
| if not torch.allclose(x_next, x) or self.time_conditioning: |
| p_x0_cache = None |
| sampling_steps += 1 |
| x = x_next |
| x = self.forward(x, 0 * ones).argmax(dim=-1) |
| intermediate_tokens.append(x[:, :stride_length].cpu().numpy()) |
| target = x[:, stride_length:] |
|
|
| intermediate_tokens.append(target.cpu().numpy()) |
| intermediate_text_samples = [] |
| sequence_lengths = ( |
| ( |
| np.concatenate(intermediate_tokens, axis=1)[:, 1:] |
| == self.tokenizer.eos_token_id |
| ).cumsum(-1) |
| == 0 |
| ).sum(-1) |
| for i in range(2, len(intermediate_tokens) + 1): |
| intermediate_text_samples.append( |
| self.tokenizer.batch_decode( |
| np.concatenate(intermediate_tokens[:i], axis=1) |
| ) |
| ) |
| return (sampling_steps, intermediate_text_samples, sequence_lengths) |
|
|
| def restore_model_and_semi_ar_sample(self, stride_length, num_strides, dt=0.001): |
| """Generate samples from the model.""" |
| |
| |
| self._eval_mode() |
| (sampling_steps, samples, sequence_lengths) = self._semi_ar_sampler( |
| n_samples=self.config.loader.eval_batch_size, |
| stride_length=stride_length, |
| num_strides=num_strides, |
| dt=dt, |
| ) |
| self._train_mode() |
| return sampling_steps, samples, sequence_lengths |
|
|
|
|
| class AbsorbingState(Diffusion): |
| def __init__(self, config, tokenizer): |
| self.temp = config.algo.get("temp", 1.0) |
| |
| |
| |
| |
| |
| vocab_size = tokenizer.vocab_size |
| if hasattr(config.data, 'vocab_file'): |
| vocab_size = len(tokenizer.added_tokens_decoder) |
| self.token_offset = True |
| else: |
| self.token_offset = False |
| if not hasattr(tokenizer, "mask_token") or tokenizer.mask_token is None: |
| self.mask_index = vocab_size |
| vocab_size += 1 |
| else: |
| self.mask_index = tokenizer.mask_token_id |
|
|
| self.subs_masking = config.algo.subs_masking |
| super().__init__(config, tokenizer, vocab_size=vocab_size) |
| self.save_hyperparameters() |
|
|
| def _validate_configuration(self): |
| super()._validate_configuration() |
| if self.parameterization in {"score", "mean"}: |
| assert self.time_conditioning |
| assert not (self.parameterization == "mean" and self.T == 0) |
| if self.T > 0: |
| assert self.parameterization in {"mean", "subs"} |
| if self.subs_masking: |
| assert self.parameterization == "mean" |
|
|
| def q_xt(self, x, alpha_t): |
| """Computes the noisy sample xt. |
| |
| Args: |
| x: int torch.Tensor with shape (batch_size, |
| diffusion_model_input_length), input. |
| alpha_t: float torch.Tensor with shape (batch_size, 1). |
| """ |
| move_indices = torch.rand(*x.shape, device=x.device) < 1 - alpha_t |
| xt = torch.where(move_indices, self.mask_index, x) |
| if self.ignore_bos: |
| xt[:, 0] = x[:, 0] |
| return xt |
|
|
| def prior_sample(self, *batch_dims): |
| |
| return self.mask_index * torch.ones( |
| *batch_dims, dtype=torch.int64, device=self.device |
| ) |
|
|
| def _ancestral_update(self, x, t, dt, p_x0=None, noise_removal_step=False, **kwargs): |
| _, alpha_t = self.noise(t) |
| if noise_removal_step: |
| alpha_s = torch.ones_like(alpha_t) |
| else: |
| _, alpha_s = self.noise(t - dt) |
| assert alpha_t.ndim == 2 |
| p_x0 = (self.forward(x, self._sigma_from_alphat(alpha_t)).double()) |
| p_x0 = p_x0.exp() |
| q_xs = p_x0 * (alpha_s - alpha_t)[:, :, None] |
| q_xs[:, :, self.mask_index] = 1 - alpha_s |
| _x = sample_categorical(q_xs) |
| copy_flag = (x != self.mask_index).to(x.dtype) |
| return p_x0, copy_flag * x + (1 - copy_flag) * _x |
|
|
| def _staggered_score(self, score, dsigma): |
| score = score.clone() |
| extra_const = (1 - dsigma.exp()) * score.sum(dim=-1) |
| score *= dsigma.exp()[:, None] |
| score[..., self.mask_index] += extra_const |
| return score |
|
|
| def _analytic_update(self, x, t, dt): |
| sigma_t = self._sigma_from_alphat(self.noise(t)[1]) |
| sigma_s = self._sigma_from_alphat(self.noise(t - dt)[1]) |
| dsigma = sigma_t - sigma_s |
| score = self._get_score(x, sigma_t) |
| if self.config.sampling.use_float64: |
| score = score.to(torch.float64) |
| stag_score = self._staggered_score(score, dsigma) |
| probs = stag_score * self._transp_transition(x, dsigma) |
| return sample_categorical(probs) |
|
|
| def _denoiser_update(self, x, t): |
| sigma = self._sigma_from_alphat(self.noise(t)[1]) |
| score = self._get_score(x, sigma) |
| if self.config.sampling.use_float64: |
| score = score.to(torch.float64) |
| stag_score = self._staggered_score(score, sigma) |
| probs = stag_score * self._transp_transition(x, sigma) |
| probs[..., self.mask_index] = 0 |
| samples = sample_categorical(probs) |
| return samples |
|
|
| def _transp_transition(self, i, sigma): |
| sigma = _unsqueeze(sigma, reference=i[..., None]) |
| edge = torch.exp(-sigma) * F.one_hot(i, num_classes=self.vocab_size) |
| edge += torch.where(i == self.mask_index, 1 - torch.exp(-sigma).squeeze(-1), 0)[ |
| ..., None |
| ] |
| return edge |
|
|
|
|
| class UniformState(Diffusion): |
| def _validate_configuration(self): |
| super()._validate_configuration() |
| assert self.time_conditioning |
| assert self.parameterization == "mean" |
| if self.config.algo.name != "distillation": |
| assert self.T == 0 |
|
|
| def q_xt(self, x, alpha_t): |
| """Computes the noisy sample xt. |
| |
| Args: |
| x: int torch.Tensor with shape (batch_size, |
| diffusion_model_input_length), input. |
| move_chance: float torch.Tensor with shape |
| (batch_size, 1). |
| """ |
| move_indices = torch.rand(*x.shape, device=x.device) < 1 - alpha_t |
| uniform_tensor = torch.randint(0, self.vocab_size, x.shape, device=x.device) |
| xt = torch.where(move_indices, uniform_tensor, x) |
| if self.ignore_bos: |
| xt[:, 0] = x[:, 0] |
| return xt |
|
|
| def prior_sample(self, *batch_dims): |
| return torch.randint( |
| 0, self.vocab_size, batch_dims, dtype=torch.int64, device=self.device |
| ) |
|
|
|
|
