Update pytorch.bin; Add model and code

config.json

@@ -0,0 +1,58 @@
+{
+  "T": 0,
+  "architectures": [
+    "E2D2"
+  ],
+  "attn_backend": "sdpa",
+  "auto_map": {
+    "AutoConfig": "diffusion.E2D2Config",
+    "AutoModel": "diffusion.E2D2",
+    "AutoModelForMaskedLM": "diffusion.E2D2"
+  },
+  "backbone_config": {
+    "_target_": "backbone_encoder_decoder.LLMasEncoderDecoderShareKV",
+    "attn_backend": "sdpa",
+    "freeze_encoder": false,
+    "keep_top_decoder_layers": true,
+    "keep_top_encoder_layers": false,
+    "max_length": 768,
+    "num_decoder_layers": 14,
+    "num_encoder_layers": 28,
+    "pretrained_model_name_or_path": "Qwen/Qwen3-1.7B-Base",
+    "reinit_decoder": false,
+    "reinit_encoder": false,
+    "tie_encoder_decoder_weights": true,
+    "use_encoder_causal_mask": false,
+    "use_gradient_checkpointing": false
+  },
+  "backbone_is_decoder_only": false,
+  "block_size": 4,
+  "bos_token_id": 151643,
+  "diffusion_type": "absorbing",
+  "eos_token_id": 151643,
+  "eval_block_size": 4,
+  "keep_clean_bos": true,
+  "length": 768,
+  "mask_token_id": 151660,
+  "model_type": "e2d2",
+  "noise_config": {
+    "_target_": "noise_schedule_noise_schedules.LinearNoise"
+  },
+  "pad_token_id": 151643,
+  "pad_vocab_size_multiple": 1,
+  "shift_logits": false,
+  "time_conditioned_backbone": false,
+  "tokenization_config": {
+    "bos_token_id": 151643,
+    "eos_token_id": 151643,
+    "mask_token_id": 151660,
+    "pad_token_id": 151643,
+    "pad_vocab_size_multiple": 1,
+    "vocab_size": 151669
+  },
+  "tokenizer_name": "Qwen/Qwen3-1.7B-Base",
+  "torch_dtype": "float32",
+  "train_on_context": false,
+  "transformers_version": "4.52.4",
+  "vocab_size": 151669
+}

diffusion.py

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+from functools import partial
+from typing import Any, Dict, Literal, Optional, Tuple, Union
+
+import torch
+from tqdm.auto import tqdm
+from transformers import (
+    GenerationConfig,
+    LogitsProcessorList,
+    PreTrainedTokenizer,
+    StoppingCriteriaList,
+)
+from transformers.cache_utils import Cache, DynamicCache
+
+try:
+    from torch.nn.attention.flex_attention import (
+        BlockMask,
+        and_masks,
+        create_block_mask,
+    )
+except ImportError:
+    BlockMask, and_masks, create_block_mask = None, None, None
+
+
+from src.denoiser.base import (
+    Denoiser,
+    DenoiserConfig,
+    DenoiserInput,
+    LossAndNllOutput,
+)
+
+
+def create_attn_mask(attn_mask):
+    # noinspection PyUnusedLocal
+    def padding(b, h, q_idx, kv_idx):
+        return attn_mask[b, q_idx] & attn_mask[b, kv_idx]
+
+    return padding
+
+
+
+class DiffusionGenerationConfig(GenerationConfig):
+    def __init__(
+        self,
+        num_steps: int = 1000,
+        min_t: float = 1e-5,
+        block_size: Optional[int] = None,
+        first_hitting: bool = False,
+        sampling_strategy: Literal["posterior", "predict_then_noise"] = "posterior",
+        confidence_based_noising: bool = False,
+        confidence_margin_based_noising: bool = False,
+        confidence_threshold: float = 1e6,
+        use_model_output_cache: bool = True,
+        align_inputs_to_blocks: bool = True,
+        **kwargs,
+    ):
+        """Generation config with additional parameters relevant for diffusion model
+            sampling.
+
+        Args:
+            num_steps (int): Number of diffusion / iterative refinement steps.
+                Defaults to 1000.
+            min_t (float): Minimum time to use.
+                Diffusion models use t=1 for noise and t=0 for signal.
+                Setting t=0 exactly can lead to certain numerical instabilities.
+                Defaults to 1e-5.
+            block_size (int): Block size to use for semi-autoregressive decoding.
+                Defaults to None (in which case block_size is set to max_new_tokens).
+            first_hitting (bool): Whether to use first hitting sampler.
+                When set to true, rather than following the diffusion time and sampling
+                from posterior, which can result in no tokens changing between steps,
+                e.g., for masked diffusion, we explicitly determine the next time step
+                at which a token will be decoded / generated.
+                Note: this will negate the `num_steps` parameter, as we will decode one
+                token at a time, hence, when True, num_steps = seq_length
+                (or block_size, for semi-autoregressive).
+                See https://arxiv.org/abs/2409.02908 for details.
+                Defaults to False.
+            sampling_strategy (str): Method for transitioning between latents.
+                Options:
+                    - "posterior" - Compute and sample from the posterior
+                        q(x_s | x_t, x_theta).
+                    - "predict_then_noise" - Sample from the denoising model x_theta,
+                        then add back noise to produce x_s.
+                        Only implemented for absorbing diffusion.
+                Defaults to "posterior".
+            confidence_based_noising (bool): When using the "predict_then_noise"
+                strategy, whether to add noise to random positions or to those that have
+                the lowest probability under x_theta.
+                Cannot be used in conjunction with confidence_margin_based_noising.
+                Defaults to False.
+            confidence_margin_based_noising (bool): When using the "predict_then_noise"
+                strategy, whether to add noise to random positions or to those that have
+                the lowest probability margins under x_theta, where margin is defined as
+                the absolute difference between the top two probabilities at a given
+                position.
+                See https://arxiv.org/abs/2502.06768 for details.
+                Cannot be used in conjunction with confidence_based_noising.
+                Defaults to False.
+            confidence_threshold (float): Confidence threshold to use for sampling.
+                Any tokens that exceed threshold are decoded.
+                See https://arxiv.org/abs/2505.22618 for details.
+                Defaults to 1e6.
+            use_model_output_cache (bool): Whether to re-use model's output, if sequence
+                is unchanged, because if xt == xs, we can simply re-use the denoising
+                model's outputs and save a function evaluation.
+                Relevant if model.backbone is not time/noise-conditioned.
+                Defaults to True.
+            align_inputs_to_blocks (bool): Whether to align input tokens to block size,
+                e.g., for an input of length C and block size S, context will be C // S,
+                and generation will begin with a block whose first C % S tokens come
+                from the input.
+            kwargs: Keyword arguments passed to `GenerationConfig`.
+        """
+        super().__init__(**kwargs)
+        self.num_steps = num_steps
+        self.min_t = min_t
+        # TODO: assumes we are setting max_new_tokens, which may not be the case!
+        self.block_size = block_size if block_size is not None else self.max_new_tokens
+        self.first_hitting = first_hitting
+        if self.first_hitting:
+            # TODO: log.warn that this is being overridden
+            self.num_steps = min(num_steps, self.block_size)
+        self.sampling_strategy = sampling_strategy
+        assert not confidence_based_noising or not confidence_margin_based_noising, (
+            "Cannot use both `confidence_based_noising` and"
+            " `confidence_margin_based_noising`."
+        )
+        self.confidence_based_noising = confidence_based_noising
+        self.confidence_margin_based_noising = confidence_margin_based_noising
+        self.confidence_threshold = confidence_threshold
+        self.use_model_output_cache = use_model_output_cache
+        self.align_inputs_to_blocks = align_inputs_to_blocks
+
+
+class D3PMConfig(DenoiserConfig):
+    """Configuration class for D3PM models."""
+
+    model_type = "d3pm"
+    auto_map = {
+        "AutoConfig": "diffusion.D3PMConfig",
+        "AutoModel": "diffusion.D3PM",
+        "AutoModelForMaskedLM": "diffusion.D3PM",
+    }
+
+    def __init__(
+        self,
+        keep_clean_bos: Optional[bool] = None,  # Whether to enforce un-noised BOS token
+        T: int = 1000,
+        diffusion_type: Literal["absorbing", "uniform"] = "absorbing",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.keep_clean_bos = keep_clean_bos
+        self.diffusion_type = diffusion_type
+        self.T = T
+
+
+class D3PM(Denoiser):
+    """Denoiser class for D3PM models.
+
+    This class implements the Denoiser interface for D3PM models.
+    """
+
+    config_class = D3PMConfig
+
+    def __init__(self, config: D3PMConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        self.T = config.T
+        self.diffusion_type = config.diffusion_type
+        self._create_static_mask()
+
+    def _create_static_mask(self) -> None:
+        static_mask = torch.ones(
+            self.config.length, self.config.length, dtype=torch.bool
+        )
+        self.register_buffer(
+            "static_attention_mask",
+            static_mask,
+        )
+
+    def _sample_q_xt(
+        self,
+        x0: torch.LongTensor,
+        alpha_t: torch.FloatTensor,
+        context_mask: torch.FloatTensor,
+    ) -> torch.LongTensor:
+        """Sample from the pre-defined forward / noising process.
+
+        Parameters:
+            x0 (Tensor): Signal / data sample;
+                can potentially include context tokens.
+            alpha_t (Tensor): Amount of signal to retain.
+            context_mask (Tensor): Indicator of context tokens (to remain
+                unchanged).
+        """
+        move_indices = torch.rand(*x0.shape, device=x0.device) < (1.0 - alpha_t)
+        if self.diffusion_type == "absorbing":
+            xt = torch.where(
+                (move_indices * (1 - context_mask)).bool(), self.mask_token_id, x0
+            )
+            if self.config.keep_clean_bos:
+                xt[..., 0] = x0[..., 0]
+            return xt  # type: ignore
+        if self.diffusion_type == "uniform":
+            xt = torch.randint(0, self.vocab_size, x0.shape, device=x0.device)
+            xt = torch.where(context_mask.bool(), x0, xt)
+            if self.config.keep_clean_bos:
+                xt[..., 0] = x0[..., 0]
+            return xt  # type: ignore
+        raise NotImplementedError(
+            f"Diffusion type '{self.diffusion_type}' not implemented."
+        )
+
+    def _prepare_inputs(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        t: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Cache] = None,
+    ):
+        # Prepare inputs for D3PM model
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if context_mask is None:
+            context_mask = torch.zeros_like(attention_mask)
+
+        if torch.is_floating_point(attention_mask):
+            attention_mask = attention_mask.to(torch.int)
+            context_mask = context_mask.to(torch.int)
+
+        if t is None:
+            t = torch.rand(input_ids.shape[0], device=input_ids.device)
+        alpha_t, alpha_t_prime = self.noise_schedule(t)
+        while alpha_t.ndim < 2:
+            alpha_t = alpha_t[..., None]
+            alpha_t_prime = alpha_t_prime[..., None]
+        xt = self._sample_q_xt(
+            x0=input_ids,
+            alpha_t=alpha_t,
+            context_mask=context_mask,
+        )
+        if (context_mask is not None
+                and context_mask.sum() == 0
+                and (attention_mask == 1).all()):
+            processed_attention_mask = None
+        else:
+            processed_attention_mask = (
+                self.static_attention_mask[None, ...]
+                & attention_mask[:, None, :]
+                & attention_mask[..., None]
+            )[:, None, ...]  # Make attention mask 4D
+            processed_attention_mask = self._preprocess_attention_mask(
+                processed_attention_mask, dtype=torch.float
+            )
+        if self.training and self.config.train_on_context:
+            tokens_mask = attention_mask
+        else:
+            tokens_mask = attention_mask * (1 - context_mask)
+        return DenoiserInput(
+            xt=xt,
+            x0=input_ids,
+            attention_mask=processed_attention_mask,
+            context_mask=context_mask,
+            tokens_mask=tokens_mask,
+            t=t,
+            alpha_t=alpha_t,
+            alpha_t_prime=alpha_t_prime,
+        )
+
+    def _prepare_inputs_inference(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context: Optional[torch.LongTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        cache: Optional[Dict[str, Any]] = None,
+        **backbone_kwargs: Any,
+    ) -> Tuple[DenoiserInput, Dict[str, Any]]:
+        assert input_ids is not None or context is not None, (
+            "Must provide either input_ids or context."
+        )
+        cache = cache if cache is not None else {}
+        past_key_values = cache.pop("past_key_values", DynamicCache())
+        if context is not None:
+            if input_ids is not None:
+                if context_mask is None:
+                    context_mask = torch.cat(
+                        [torch.ones_like(context), torch.zeros_like(input_ids)], dim=-1
+                    )
+                input_ids = torch.cat([context, input_ids], dim=-1)
+            else:
+                input_ids = context
+                context_mask = torch.ones_like(input_ids)
+        if attention_mask is None:
+            cache_length = self._get_past_key_values_seq_length(past_key_values)
+            full_seq_length = cache_length + input_ids.shape[-1]
+            attention_mask = torch.ones(
+                (input_ids.shape[0], 1, input_ids.shape[1], full_seq_length),
+                device=input_ids.device,
+            )  # Make attention mask 4D
+            attention_mask = self._preprocess_attention_mask(
+                attention_mask, dtype=torch.float
+            )
+        return DenoiserInput(
+            xt=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            context_mask=context_mask,
+            backbone_kwargs=backbone_kwargs | {"use_cache": False},
+        ), cache
+
+    def _forward(
+        self,
+        backbone_output: torch.FloatTensor,
+        denoiser_inputs: DenoiserInput,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        return torch.log_softmax(backbone_output, dim=-1)  # type: ignore
+
+    def _compute_loss(
+        self,
+        model_output: torch.FloatTensor,
+        denoiser_inputs: DenoiserInput,
+        **kwargs: Any,
+    ) -> LossAndNllOutput:
+        raise NotImplementedError
+
+    def _sample_prior(self, device, batch_size, length):
+        """Samples from prior / limiting distribution."""
+        if self.diffusion_type == "absorbing":
+            return self.mask_token_id * torch.ones(
+                (batch_size, length), dtype=torch.int64, device=device
+            )
+        if self.diffusion_type == "uniform":
+            return torch.randint(
+                0,
+                self.vocab_size,
+                (batch_size, length),
+                device=device,
+                dtype=torch.int64,
+            )
+        raise NotImplementedError(
+            f"Diffusion type '{self.diffusion_type}' not implemented."
+        )
+
+    def _compute_posterior(
+        self,
+        x: Union[torch.FloatTensor, torch.LongTensor],
+        xt: torch.LongTensor,
+        alpha_t: torch.FloatTensor,
+        alpha_s: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        """Computes posterior / approximate posterior q(x_s | x_t, x),
+            where x represents clean sequence (as one-hots) or the output of the
+            denoising model.
+
+        Args:
+            x (Tensor): True (one-hot) / predicted clean signal (B, L, V).
+            xt (Tensor): Noised signal at time t (B, L).
+            alpha_t (Tensor): Noise schedule parameter at time t (B, 1, 1).
+            alpha_s (Tensor): Noise schedule parameter at time s (B, 1, 1).
+        """
+        if self.diffusion_type == "absorbing":
+            q_xs = x * (alpha_s - alpha_t)
+            q_xs[..., self.mask_token_id] = 1 - alpha_s[..., 0]
+            q_xs /= 1 - alpha_t
+            return q_xs  # type: ignore
+
+        alpha_ts = alpha_t / alpha_s
+        d_alpha = alpha_s - alpha_t
+        xt_one_hot = torch.nn.functional.one_hot(x, self.vocab_size)
+        limiting_distribution = torch.ones_like(xt_one_hot) / self.vocab_size
+        if self.diffusion_type == "uniform":
+            return (
+                alpha_t * self.vocab_size * x * xt_one_hot
+                + (alpha_ts - alpha_t) * xt_one_hot
+                + d_alpha * x
+                + (1 - alpha_ts) * (1 - alpha_s) * limiting_distribution
+            ) / (
+                alpha_t * self.vocab_size * torch.gather(x, -1, xt[..., None])
+                + (1 - alpha_t)
+            )
+        raise NotImplementedError(
+            f"Diffusion type {self.diffusion_type} not implemented."
+        )
+
+    @staticmethod
+    def _sample_generation_timesteps(
+        generation_config: DiffusionGenerationConfig,
+        max_length: Optional[int] = None,
+        device: Optional[str] = None,
+    ) -> torch.FloatTensor:
+        """Sample timesteps for diffusion generation process."""
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        if max_length is None:
+            max_length = generation_config.max_new_tokens
+
+        if (
+            generation_config.first_hitting
+            # TODO: first-hitting does not work with posterior
+            and generation_config.sampling_strategy == "posterior"
+        ):
+            timesteps = torch.FloatTensor([1.0])
+            for i in range(max_length, 0, -1):
+                u = torch.rand(1)
+                next_t = timesteps[-1] * u ** (1 / i)
+                timesteps = torch.cat((timesteps, next_t), dim=0)
+            return timesteps[1:].to(device)  # type: ignore
+        return torch.linspace(  # type: ignore
+            1.0,
+            generation_config.min_t,
+            generation_config.num_steps + 1,
+            device=device,
+        )[:-1]
+
+    def _generate_unconditional(
+        self,
+        generation_config: DiffusionGenerationConfig,
+        alpha_t: torch.FloatTensor,
+        alpha_s: torch.FloatTensor,
+        denoiser_inputs: Optional[DenoiserInput] = None,
+        model_output_cache: Optional[Dict[str, torch.FloatTensor]] = None,
+        cache: Optional[Dict[str, Any]] = None,
+        running_generation: Optional[torch.LongTensor] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        **kwargs: Any,
+    ) -> Tuple[torch.LongTensor, Dict[str, torch.FloatTensor], Dict[str, Any]]:
+        cache = cache if cache is not None else {}
+        if model_output_cache is None:  # execute function evaluation
+            backbone_output = self._backbone_forward(
+                denoiser_inputs,
+                fix_cache_length=True,  # Do not let kv cache grow on each forward call
+                **cache,
+                **kwargs,
+            )
+            backbone_output = {k: v for k, v in backbone_output.items()}
+            logits = backbone_output.pop("logits")
+            cache = cache | backbone_output
+            log_x_theta = self._forward(logits, denoiser_inputs, **kwargs)
+            if logits_processor is not None:
+                for token_idx in range(log_x_theta.shape[1]):
+                    # TODO: Looping over token positions like this does not allow for
+                    #   some processors, e.g. length penalty which could be applied all
+                    #   at once to the entire block, to be applied in parallel.
+                    log_x_theta[:, token_idx] = logits_processor(
+                        input_ids=running_generation,
+                        scores=log_x_theta[:, token_idx],  # type: ignore
+                    )
+                log_x_theta = torch.log_softmax(log_x_theta, dim=-1)  # re-normalize
+            x_theta = log_x_theta.exp()
+        else:
+            x_theta = model_output_cache["x_theta"]
+        model_output_cache = {"x_theta": x_theta}
+        prob_check_denom = denoiser_inputs.xt.numel()
+        if generation_config.sampling_strategy == "posterior":
+            q_xs = self._compute_posterior(
+                x_theta, denoiser_inputs.xt, alpha_t, alpha_s
+            )
+
+            assert abs((q_xs.sum() / prob_check_denom).item() - 1.0) < 1e-6, (
+                "Posterior probabilities not summing to 1."
+            )
+            assert q_xs.isnan().sum().item() == 0, "NaN found in the posterior."
+            xs = self._sample_categorical(q_xs, generation_config.do_sample)
+            output = torch.where(
+                (denoiser_inputs.xt != self.mask_token_id).bool(),  # type: ignore
+                denoiser_inputs.xt,
+                xs,
+            )
+        elif generation_config.sampling_strategy == "predict_and_noise":
+            assert self.config.diffusion_type == "absorbing", (
+                "predict_and_noise decoding strategy only supports absorbing diffusion."
+            )
+            # assert (
+            #     abs((x_theta.sum() / prob_check_denom).item() - 1.0) < 1e-6
+            # ), "Denoising output probabilities not summing to 1."
+            # assert x_theta.isnan().sum().item() == 0, (
+            #     "NaN found in the denoising output."
+            # )
+
+            # Predict
+            xs = self._sample_categorical(x_theta, generation_config.do_sample)
+            xs_probs = x_theta.gather(-1, xs[..., None]).squeeze(dim=-1)
+            output = xs.clone()
+
+            # Noise
+            num_noise_indices = torch.minimum(
+                ((1 - alpha_s) * generation_config.block_size).to(torch.int),
+                (denoiser_inputs.xt == self.mask_token_id).sum() - 1,  # type: ignore
+            )
+            if generation_config.confidence_based_noising:
+                conf = x_theta.gather(-1, xs[..., None]).squeeze(-1)
+                conf = torch.where(  # already decoded tokens have 'inf' confidence
+                    (denoiser_inputs.xt == self.mask_token_id).bool(),  # type: ignore
+                    conf,
+                    torch.inf,
+                )
+                noise_indices = conf.argsort(dim=-1)[..., :num_noise_indices]
+            elif generation_config.confidence_margin_based_noising:
+                top2 = torch.topk(x_theta, k=2, dim=-1).values  # shape: (B, L, 2)
+                conf = (top2[..., 0] - top2[..., 1]).abs()
+                conf = torch.where(  # already decoded tokens have 'inf' confidence
+                    (denoiser_inputs.xt == self.mask_token_id).bool(),  # type: ignore
+                    conf,
+                    torch.inf,
+                )
+                noise_indices = conf.argsort(dim=-1)[..., :num_noise_indices]
+            else:
+                # TODO: implement random noise indices selection
+                raise NotImplementedError
+            output[..., noise_indices] = self.mask_token_id
+            output = torch.where(
+                xs_probs >= generation_config.confidence_threshold, xs, output
+            )
+        else:
+            raise NotImplementedError(
+                f"Sampling strategy {generation_config.sampling_strategy} not"
+                " implemented."
+            )
+        return output, model_output_cache, cache  # type: ignore
+
+    @torch.no_grad()
+    def generate(
+        self,
+        inputs: Optional[torch.LongTensor] = None,
+        generation_config: Optional[DiffusionGenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        max_length: Optional[int] = None,
+        max_new_tokens: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        device: Optional[str] = None,
+        tokenizer: Optional[PreTrainedTokenizer] = None,
+        disable_pbar: bool = False,
+        **kwargs: Any,
+    ) -> torch.LongTensor:
+        # Setup sampling variables
+        if generation_config is None:
+            assert getattr(self, "generation_config", None) is not None, (
+                "Generation config must be provided if not present in the model."
+            )
+            generation_config = self.generation_config
+        if inputs is None:
+            inputs = torch.ones((batch_size, 1), device=device) * self.bos_token_id
+        if max_length is None:
+            if hasattr(generation_config, "max_length"):
+                max_length = generation_config.max_length
+            else:
+                max_length = self.max_length
+        if max_new_tokens is None:
+            if hasattr(generation_config, "max_new_tokens"):
+                max_new_tokens = generation_config.max_new_tokens
+            else:
+                max_new_tokens = max_length - inputs.shape[-1]
+        batch_size = batch_size if batch_size is not None else inputs.shape[0]
+        assert batch_size == 1, "Batched sampling not supported yet"
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        block_size = generation_config.block_size
+        max_blocks = max_new_tokens // block_size
+
+        # Sample max generation length tensor from prior
+        accumulated_samples = self._sample_prior(
+            device=device,
+            batch_size=batch_size,
+            length=max_blocks * block_size,
+        )
+        accumulated_samples = torch.cat([inputs, accumulated_samples], dim=-1)
+        if generation_config.use_cache and inputs.numel() > 0:
+            cache = self.update_cache(
+                inputs=inputs[:, : block_size * (inputs.shape[-1] // block_size)]
+                if generation_config.align_inputs_to_blocks
+                else inputs,
+                cache={},
+            )
+        else:
+            cache = None
+
+        if generation_config.align_inputs_to_blocks:
+            inputs_offset = (
+                block_size * (inputs.shape[-1] // block_size)
+                if inputs.numel() > 0
+                else 0
+            )
+        else:
+            inputs_offset = inputs.shape[-1] if inputs.numel() > 0 else 0
+
+        total_NFEs = 0
+        timesteps = self._sample_generation_timesteps(  # Re-use in every block
+            generation_config, max_length=block_size, device=device
+        )
+        dt = (1 - generation_config.min_t) / len(timesteps)
+        block_pbar = tqdm(
+            range(max_blocks),
+            desc="Blocks",
+            leave=True,
+            disable=disable_pbar,
+        )
+        for block_id in block_pbar:
+            block_NFEs = 0
+            xt = accumulated_samples[
+                :,
+                inputs_offset + (block_id * block_size) : inputs_offset
+                + ((block_id + 1) * block_size),
+            ]
+            if self.mask_token_id not in xt:
+                continue
+            step_pbar = tqdm(
+                timesteps,
+                desc="T",
+                total=timesteps.shape[0],
+                leave=False,
+                disable=disable_pbar,
+            )
+            model_output_cache = None
+            context = (
+                accumulated_samples[:, : (block_id * block_size) + inputs_offset]
+                if not generation_config.use_cache
+                else None
+            )
+            # Used for logit processing
+            running_generation = accumulated_samples[
+                :,
+                inputs_offset : inputs_offset + (block_id * block_size),
+            ]
+            for t in step_pbar:
+                if model_output_cache is None:
+                    block_NFEs += 1
+                    total_NFEs += 1
+                # t is 0-dim tensor, reshape to (1, 1, 1) for broadcasting
+                alpha_t, _ = self.noise_schedule(t)
+                alpha_s, _ = self.noise_schedule(t - dt)
+                alpha_t = alpha_t[None, None, None]
+                alpha_s = alpha_s[None, None, None]
+                denoiser_inputs, cache = self._prepare_inputs_inference(
+                    input_ids=xt,
+                    context=context,
+                    cache=cache if generation_config.use_cache else None,
+                )
+                xs, model_output_cache, cache = self._generate_unconditional(
+                    generation_config=generation_config,
+                    alpha_t=alpha_t,
+                    alpha_s=alpha_s,
+                    denoiser_inputs=denoiser_inputs,
+                    model_output_cache=model_output_cache,
+                    cache=cache,
+                    running_generation=running_generation,  # type: ignore
+                    logits_processor=logits_processor,
+                    tokenizer=tokenizer,
+                    **kwargs,
+                )
+                block_pbar.set_postfix(
+                    NFEs=total_NFEs,
+                    block_NFEs=block_NFEs,
+                )
+
+                if (
+                    not torch.allclose(xs, denoiser_inputs.xt)
+                    or not generation_config.use_model_output_cache
+                ):
+                    model_output_cache = None
+                if not generation_config.use_cache:
+                    xt[..., -block_size:] = xs[..., -block_size:]
+                else:
+                    xt = xs
+                if (
+                    xt == self.mask_token_id
+                ).sum().item() == 0 and self.config.diffusion_type == "absorbing":
+                    break
+            accumulated_samples[
+                :,
+                inputs_offset + (block_id * block_size) : inputs_offset
+                + ((block_id + 1) * block_size),
+            ] = xt
+            if tokenizer is not None:  # Useful for debugging
+                print(tokenizer.batch_decode(accumulated_samples))
+            if stopping_criteria is not None:
+                is_done = stopping_criteria(
+                    input_ids=accumulated_samples[  # type: ignore
+                        :,
+                        inputs_offset : inputs_offset + ((block_id + 1) * block_size),
+                    ],
+                    scores=None,  # type: ignore
+                )
+                if torch.any(is_done):
+                    accumulated_samples = accumulated_samples[
+                        :,
+                        : inputs_offset + ((block_id + 1) * block_size),
+                    ]
+                    break
+            if generation_config.use_cache:
+                cache = self.update_cache(
+                    inputs=xt,
+                    cache=cache,
+                )
+        return accumulated_samples  # type: ignore
+
+
+class MDLMConfig(D3PMConfig):
+    """Configuration class for MDLM models."""
+
+    model_type = "mdlm"
+    auto_map = {
+        "AutoConfig": "diffusion.MDLMConfig",
+        "AutoModel": "diffusion.MDLM",
+        "AutoModelForMaskedLM": "diffusion.MDLM",
+    }
+
+
+class MDLM(D3PM):
+    """Denoiser class for MDLM models."""
+
+    config_class = MDLMConfig
+
+    def __init__(self, config: MDLMConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        self.neg_infinity = -1e12
+
+    def _forward(
+        self,
+        backbone_output: torch.FloatTensor,
+        denoiser_inputs: DenoiserInput,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        # Zero-mask probability
+        backbone_output[..., self.mask_token_id] = self.neg_infinity
+        log_probs = backbone_output - torch.logsumexp(
+            backbone_output, dim=-1, keepdim=True
+        )
+        # Copy-over unmasked: For the log_probs of the unmasked tokens, set all values
+        # to -infinity except for the indices corresponding to
+        # the unmasked tokens.
+        xt = denoiser_inputs.xt
+        unmasked_indices = xt != self.mask_token_id
+        log_probs[unmasked_indices] = self.neg_infinity
+        log_probs[unmasked_indices, xt[unmasked_indices]] = 0
+        return log_probs  # type: ignore
+
+    def _compute_loss(
+        self,
+        model_output: torch.FloatTensor,
+        denoiser_inputs: DenoiserInput,
+        **kwargs: Any,
+    ) -> LossAndNllOutput:
+        log_p_theta = torch.gather(
+            input=model_output, dim=-1, index=denoiser_inputs.x0[:, :, None]
+        ).squeeze(-1)
+        nlls = (
+            log_p_theta
+            * denoiser_inputs.alpha_t_prime
+            / (1 - denoiser_inputs.alpha_t)
+            * denoiser_inputs.tokens_mask
+        )
+        if self.training:
+            batch_nll = -(log_p_theta * denoiser_inputs.tokens_mask).sum(dim=-1)
+        else:
+            batch_nll = nlls.sum(dim=-1)
+        count = denoiser_inputs.tokens_mask.sum(dim=-1)
+        token_nll = (batch_nll / count).mean()
+        return LossAndNllOutput(
+            loss=token_nll,  # type: ignore
+            nlls=nlls,
+            other_loss_terms={
+                "masked_tokens": (denoiser_inputs.xt == self.mask_token_id).int()
+            },
+        )
+
+
+class BD3LMConfig(MDLMConfig):
+    """Configuration class for BD3LM models."""
+
+    model_type = "bd3lm"
+    auto_map = {
+        "AutoConfig": "diffusion.BD3LMConfig",
+        "AutoModel": "diffusion.BD3LM",
+        "AutoModelForMaskedLM": "diffusion.BD3LM",
+    }
+
+    def __init__(
+        self,
+        block_size: Optional[int] = None,
+        eval_block_size: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.block_size = block_size
+        self.eval_block_size = (
+            eval_block_size if eval_block_size is not None else block_size
+        )
+
+
+class BD3LM(MDLM):
+    """Denoiser class for BD3LM models."""
+
+    config_class = BD3LMConfig
+
+    def __init__(self, config: BD3LMConfig, **kwargs):
+        super().__init__(config, **kwargs)
+
+    # noinspection PyUnusedLocal
+    @staticmethod
+    def _block_mask(
+        b,
+        h,
+        q_idx,
+        kv_idx,
+        block_size: Optional[int] = None,
+        seq_length: Optional[int] = None,
+    ) -> torch.Tensor:
+        del b, h
+
+        # Indicate whether token belongs to xt or x0:
+        xt_flag_q = (q_idx >= seq_length).bool()
+        xt_flag_kv = (kv_idx >= seq_length).bool()
+
+        # Compute block indices
+        block_q = torch.where(
+            xt_flag_q, (q_idx - seq_length) // block_size, q_idx // block_size
+        )
+        block_kv = torch.where(
+            xt_flag_kv, (kv_idx - seq_length) // block_size, kv_idx // block_size
+        )
+        # **1. Offset Block-Causal Mask (M_OBC) **
+        offset_block_causal = (block_q > block_kv) & ~xt_flag_kv & xt_flag_q
+
+        # **2. Block Diagonal Mask (M_BD) **
+        block_diagonal = (block_q == block_kv) & (xt_flag_q == xt_flag_kv)
+
+        # **3. Block-Causal Mask (M_BC) **
+        block_causal = (block_q >= block_kv) & ~xt_flag_kv & ~xt_flag_q
+
+        # **3. Combine Masks **
+        return block_diagonal | offset_block_causal | block_causal
+
+    def _create_static_mask(self) -> None:
+        if self.config.attn_backend == "sdpa":
+            static_mask = self._block_mask(
+                b=None,
+                h=None,
+                q_idx=torch.arange(self.config.length * 2)[:, None],
+                kv_idx=torch.arange(self.config.length * 2)[None, :],
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                seq_length=self.config.length,
+            )
+            self.register_buffer(
+                "static_attention_mask",
+                static_mask,
+            )
+        elif self.config.attn_backend == "flex_attention":
+            mask = partial(
+                self._block_mask,
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                seq_length=self.config.length,
+            )
+            self.static_attention_mask = create_block_mask(
+                mask,
+                B=None,
+                H=None,
+                Q_LEN=self.config.length * 2,
+                KV_LEN=self.config.length * 2,
+            )
+
+    def _ensure_no_unmasked_blocks(
+        self,
+        input_ids: torch.LongTensor,
+        xt: torch.LongTensor,
+        context_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.Tensor:
+        n_blocks = xt.shape[1] // self.config.block_size
+        # If context overlaps w/block, ignore it
+        blocks_without_masks = ((xt == self.mask_token_id) + context_mask).reshape(
+            -1, n_blocks, self.config.block_size
+        ).sum(dim=-1) == 0
+        if blocks_without_masks.sum() > 0:
+            num_remasks_per_block = torch.randint(
+                0,
+                self.config.block_size,
+                blocks_without_masks.shape,
+                device=xt.device,
+            )
+            rand = torch.rand(xt.shape[0], xt.shape[1], device=xt.device)
+            perm_indices = torch.argsort(
+                rand.view(xt.shape[0], n_blocks, self.config.block_size),
+                stable=True,
+                dim=-1,
+            )
+            remask_indices = perm_indices <= num_remasks_per_block[..., None]
+            xt = torch.where(
+                remask_indices.view(xt.shape[0], xt.shape[1])
+                * blocks_without_masks.repeat_interleave(self.config.block_size, dim=1),
+                self.mask_token_id,
+                xt,
+            )
+            if self.config.keep_clean_bos:
+                xt[..., 0] = input_ids[..., 0]
+        return xt
+
+    def _prepare_inputs(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        t: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Cache] = None,
+    ):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if context_mask is None:
+            context_mask = torch.zeros_like(attention_mask)
+
+        if torch.is_floating_point(attention_mask):
+            attention_mask = attention_mask.to(torch.int)
+            context_mask = context_mask.to(torch.int)
+
+        if t is None:
+            t = torch.rand(
+                input_ids.shape[0],
+                input_ids.shape[1] // self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                device=input_ids.device,
+            ).repeat_interleave(
+                self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                dim=-1,
+            )
+        alpha_t, alpha_t_prime = self.noise_schedule(t)
+        while alpha_t.ndim < 2:
+            alpha_t = alpha_t[..., None]
+            alpha_t_prime = alpha_t_prime[..., None]
+        xt = self._sample_q_xt(x0=input_ids, alpha_t=alpha_t, context_mask=context_mask)
+        # Ensure each block has at least 1 masked token
+        if self.training:
+            xt = self._ensure_no_unmasked_blocks(
+                input_ids,
+                xt,
+                context_mask,
+            )
+        if self.config.attn_backend == "sdpa":
+            decoder_attention_mask = (
+                self.static_attention_mask[None, ...]
+                & attention_mask.repeat(1, 2)[:, None, :]
+                & attention_mask.repeat(1, 2)[..., None]
+            )[:, None, ...]  # Make attention mask 4D
+            decoder_attention_mask = self._preprocess_attention_mask(
+                decoder_attention_mask, dtype=torch.float
+            )
+        elif self.config.attn_backend == "flex_attention":
+            if context_mask.any():
+                raise NotImplementedError(
+                    "flex_attention with context_mask not implemented yet."
+                )
+            elif attention_mask is not None and (attention_mask != 1).any():
+                padding_mask = create_attn_mask(
+                    attention_mask.bool().repeat(2, 2).bool()
+                )
+                dec_masks = [
+                    partial(
+                        self._block_mask,
+                        block_size=self.config.block_size
+                        if self.training
+                        else self.config.eval_block_size,
+                        seq_length=self.config.length,
+                    ),
+                    padding_mask,
+                ]
+                decoder_attention_mask = create_block_mask(
+                    and_masks(*dec_masks),
+                    B=input_ids.shape[0],
+                    H=None,
+                    Q_LEN=input_ids.shape[1] * 2,
+                    KV_LEN=input_ids.shape[1] * 2,
+                )
+            else:
+                decoder_attention_mask = self.static_attention_mask
+        else:
+            raise ValueError("Unknown backbone backend")
+        backbone_input_ids = torch.cat((input_ids, xt), dim=-1)
+        position_ids = (
+            torch.arange(input_ids.shape[1]).repeat(2).to(input_ids.device)[None, :]
+        )
+        if self.training and self.config.train_on_context:
+            tokens_mask = attention_mask
+        else:
+            tokens_mask = attention_mask * (1 - context_mask)
+        return DenoiserInput(
+            xt=backbone_input_ids,  # type: ignore
+            x0=input_ids,
+            attention_mask=decoder_attention_mask,  # type: ignore
+            tokens_mask=tokens_mask,
+            t=t,
+            alpha_t=alpha_t,
+            alpha_t_prime=alpha_t_prime,
+            backbone_kwargs={
+                "cache_position": position_ids[0],
+                "position_ids": position_ids,
+            },
+        )
+
+    def _prepare_inputs_inference(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context: Optional[torch.LongTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        cache: Optional[Dict[str, Any]] = None,
+        return_updated_cache: bool = False,
+        **backbone_kwargs: Dict[str, Any],
+    ) -> Tuple[DenoiserInput, Union[Dict[str, Any], None]]:
+        device = input_ids.device if input_ids is not None else context.device
+        assert input_ids is not None or context is not None, (
+            "Must provide either input_ids or context."
+        )
+        cache = cache if cache is not None else {}
+        past_key_values = cache.pop("past_key_values", DynamicCache())
+        if context is not None:
+            if input_ids is not None:
+                input_ids = torch.cat([context, input_ids], dim=-1)
+            else:
+                input_ids = context
+        cache_length = self._get_past_key_values_seq_length(past_key_values)
+        full_seq_length = cache_length + input_ids.shape[-1]
+        decoder_attention_mask = self.static_attention_mask[
+            None,
+            None,
+            cache_length:full_seq_length,
+            :full_seq_length,
+        ]  # Make attention mask 4D
+        decoder_attention_mask = self._preprocess_attention_mask(
+            decoder_attention_mask, dtype=torch.float
+        )
+        position_ids = torch.arange(cache_length, full_seq_length).to(device)[None, :]
+        return DenoiserInput(
+            xt=input_ids,
+            attention_mask=decoder_attention_mask,
+            context_mask=context_mask,
+            past_key_values=past_key_values,
+            backbone_kwargs={
+                "position_ids": position_ids,
+            }
+            | backbone_kwargs,
+        ), cache
+
+    def _compute_loss(
+        self,
+        model_output: torch.FloatTensor,
+        denoiser_inputs: DenoiserInput,
+        **kwargs: Any,
+    ) -> LossAndNllOutput:
+        if self.config.backbone_is_decoder_only:
+            input_length = denoiser_inputs.xt.shape[1] // 2
+            model_output = model_output[:, input_length:, ...]
+        return super()._compute_loss(
+            model_output=model_output,
+            denoiser_inputs=denoiser_inputs,
+            **kwargs,
+        )
+
+
+class E2D2Config(BD3LMConfig):
+    """Configuration class for E2D2 models."""
+
+    model_type = "e2d2"
+    auto_map = {
+        "AutoConfig": "diffusion.E2D2Config",
+        "AutoModel": "diffusion.E2D2",
+        "AutoModelForMaskedLM": "diffusion.E2D2",
+    }
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+
+class E2D2(BD3LM):
+    """Denoiser class for E2D2 models."""
+
+    config_class = E2D2Config
+
+    def __init__(self, config: E2D2Config, **kwargs):
+        super().__init__(config, **kwargs)
+
+    # noinspection PyUnusedLocal
+    @staticmethod
+    def _encoder_block_mask(
+        b,
+        h,
+        q_idx,
+        kv_idx,
+        block_size: Optional[int] = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            q_idx (Tensor): Query indices.
+            kv_idx (Tensor): Key indices
+            b (Optional: int): batch size
+            h (Optional: int): number of heads
+            block_size (Optional: int): Defines the block structure.
+
+        Returns:
+            Encoder block-causal attention mask.
+        """
+
+        # Compute block indices
+        block_q = q_idx // block_size
+        block_kv = kv_idx // block_size
+
+        # ** Block-Causal Mask **
+        return block_q >= block_kv
+
+    # noinspection PyUnusedLocal
+    @staticmethod
+    def _decoder_block_mask(
+        b,
+        h,
+        q_idx,
+        kv_idx,
+        block_size: Optional[int] = None,
+        seq_length: Optional[int] = None,
+    ) -> torch.Tensor:
+        # Indicate whether token belongs to xt or x0:
+        xt_flag_kv = (kv_idx >= seq_length).bool()
+
+        # Compute block indices
+        block_q = q_idx // block_size
+        block_kv = torch.where(
+            xt_flag_kv, (kv_idx - seq_length) // block_size, kv_idx // block_size
+        )
+        # **1. Offset Block-Causal Mask (M_OBC) **
+        offset_block_causal = (block_q > block_kv) & ~xt_flag_kv
+
+        # **2. Block Diagonal Mask (M_BD) **
+        block_diagonal = (block_q == block_kv) & xt_flag_kv
+
+        # **3. Combine Masks **
+        return block_diagonal | offset_block_causal
+
+    def _create_static_mask(self) -> None:
+        if self.config.attn_backend == "flex_attention":
+            enc_mask = partial(
+                self._encoder_block_mask,
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+            )
+            encoder_attention_mask = create_block_mask(
+                enc_mask,
+                B=None,
+                H=None,
+                Q_LEN=self.config.length,
+                KV_LEN=self.config.length,
+            )
+            dec_mask = partial(
+                self._decoder_block_mask,
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                seq_length=self.config.length,
+            )
+            decoder_attention_mask = create_block_mask(
+                dec_mask,
+                B=None,
+                H=None,
+                Q_LEN=self.config.length,
+                KV_LEN=self.config.length * 2,
+            )
+            self.encoder_static_attention_mask = encoder_attention_mask
+            self.static_attention_mask = decoder_attention_mask
+        else:
+            encoder_static_mask = self._encoder_block_mask(
+                b=None,  # type: ignore
+                h=None,  # type: ignore
+                q_idx=torch.arange(self.config.length)[:, None],
+                kv_idx=torch.arange(self.config.length)[None, :],
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+            )
+            decoder_static_mask = self._decoder_block_mask(
+                b=None,
+                h=None,
+                q_idx=torch.arange(self.config.length)[:, None],
+                kv_idx=torch.arange(self.config.length * 2)[None, :],
+                block_size=self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                seq_length=self.config.length,
+            )
+            self.register_buffer(
+                "encoder_static_attention_mask",
+                encoder_static_mask,
+            )
+            self.register_buffer(
+                "static_attention_mask",
+                decoder_static_mask,
+            )
+
+    def _prepare_inputs(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        t: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Cache] = None,
+    ):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if context_mask is None:
+            context_mask = torch.zeros_like(attention_mask)
+
+        if torch.is_floating_point(attention_mask):
+            attention_mask = attention_mask.to(torch.int)
+            context_mask = context_mask.to(torch.int)
+
+        if t is None:
+            t = torch.rand(
+                input_ids.shape[0],
+                input_ids.shape[1] // self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                device=input_ids.device,
+            ).repeat_interleave(
+                self.config.block_size
+                if self.training
+                else self.config.eval_block_size,
+                dim=-1,
+            )
+        alpha_t, alpha_t_prime = self.noise_schedule(t)
+        while alpha_t.ndim < 2:
+            alpha_t = alpha_t[..., None]
+            alpha_t_prime = alpha_t_prime[..., None]
+        xt = self._sample_q_xt(x0=input_ids, alpha_t=alpha_t, context_mask=context_mask)
+        # Ensure each block has at least 1 masked token
+        if self.training:
+            xt = self._ensure_no_unmasked_blocks(
+                input_ids,
+                xt,
+                context_mask,
+            )
+        if self.config.attn_backend == "sdpa":
+            decoder_attention_mask = (
+                self.static_attention_mask[None, ...]
+                & attention_mask.repeat(1, 2)[:, None, :]
+                & attention_mask[..., None]
+            )[:, None, ...]  # Make attention mask 4D
+            encoder_attention_mask = (
+                (
+                    self.encoder_static_attention_mask[None, ...]
+                    | context_mask[:, None, :]
+                )
+                & attention_mask[:, None, :]
+                & attention_mask[..., None]
+            )[:, None, ...]  # Make attention mask 4D
+            encoder_attention_mask = self._preprocess_attention_mask(
+                encoder_attention_mask, dtype=torch.float
+            )
+            decoder_attention_mask = self._preprocess_attention_mask(
+                decoder_attention_mask, dtype=torch.float
+            )
+        elif self.config.attn_backend == "flex_attention":
+            # TODO enable bidirectional attention on context for seq2seq tasks
+            if context_mask.any():
+                raise NotImplementedError(
+                    "flex_attention with context_mask not implemented yet."
+                )
+            elif attention_mask is not None and (attention_mask != 1).any():
+                padding_mask = create_attn_mask(attention_mask.bool())
+                dec_padding_mask = create_attn_mask(attention_mask.repeat(1, 2).bool())
+                enc_masks = [
+                    partial(
+                        self._encoder_block_mask,
+                        block_size=self.config.block_size
+                        if self.training
+                        else self.config.eval_block_size,
+                    ),
+                    padding_mask,
+                ]
+                encoder_attention_mask = create_block_mask(
+                    and_masks(*enc_masks),
+                    B=input_ids.shape[0],
+                    H=None,
+                    Q_LEN=input_ids.shape[1],
+                    KV_LEN=input_ids.shape[1],
+                )
+                dec_masks = [
+                    partial(
+                        self._decoder_block_mask,
+                        block_size=self.config.block_size
+                        if self.training
+                        else self.config.eval_block_size,
+                        seq_length=input_ids.shape[1],
+                    ),
+                    dec_padding_mask,
+                ]
+                decoder_attention_mask = create_block_mask(
+                    and_masks(*dec_masks),
+                    B=input_ids.shape[0],
+                    H=None,
+                    Q_LEN=input_ids.shape[1],
+                    KV_LEN=input_ids.shape[1] * 2,
+                )
+            else:
+                encoder_attention_mask = self.encoder_static_attention_mask
+                decoder_attention_mask = self.static_attention_mask
+        else:
+            raise ValueError("Unknown backbone backend")
+        position_ids = torch.arange(input_ids.shape[1]).to(input_ids.device)[None, :]
+        if self.training and self.config.train_on_context:
+            tokens_mask = attention_mask
+        else:
+            tokens_mask = attention_mask * (1 - context_mask)
+        return DenoiserInput(
+            xt=xt,
+            x0=input_ids,
+            attention_mask=decoder_attention_mask,
+            tokens_mask=tokens_mask,
+            t=t,
+            alpha_t=alpha_t,
+            alpha_t_prime=alpha_t_prime,
+            backbone_kwargs={
+                "encoder_input_ids": input_ids,
+                "encoder_attention_mask": encoder_attention_mask,
+                "encoder_position_ids": position_ids,
+                "encoder_cache_position": position_ids[0],
+            },
+        )
+
+    def _prepare_inputs_inference(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        context: Optional[torch.LongTensor] = None,
+        context_mask: Optional[torch.FloatTensor] = None,
+        cache: Optional[Dict[str, Any]] = None,
+        return_updated_cache: bool = False,
+        **backbone_kwargs: Dict[str, Any],
+    ) -> Tuple[DenoiserInput, Union[Dict[str, Any], None]]:
+        device = input_ids.device if input_ids is not None else context.device
+        batch_size = input_ids.shape[0] if input_ids is not None else context.shape[0]
+        assert input_ids is not None or context is not None, (
+            "Must provide either input_ids or context."
+        )
+        if return_updated_cache:  # Indicates this is a cache update step
+            context = input_ids
+            input_ids = None
+        position_ids, encoder_position_ids = None, None
+        if cache is not None:
+            past_key_values = cache.pop("past_key_values", DynamicCache())
+            encoder_past_key_values = cache.pop(
+                "encoder_past_key_values", DynamicCache()
+            )
+            encoder_last_hidden_state = cache.pop("encoder_last_hidden_state", None)
+            if input_ids is not None:  # Skip enc: nothing new to cache
+                cache_length = self._get_past_key_values_seq_length(past_key_values)
+                if encoder_last_hidden_state is not None:
+                    full_seq_length = (
+                        cache_length
+                        + encoder_last_hidden_state.shape[1]  # type: ignore
+                        + input_ids.shape[-1]
+                    )
+                else:
+                    full_seq_length = cache_length + input_ids.shape[-1]
+                encoder_attention_mask = None
+                position_ids = torch.arange(
+                    cache_length, full_seq_length, device=device
+                )[None, :]
+            else:  # Caching new tokens in the enc
+                encoder_cache_length = self._get_past_key_values_seq_length(
+                    encoder_past_key_values
+                    if len(encoder_past_key_values) > 0
+                    else past_key_values
+                )
+                encoder_full_seq_length = encoder_cache_length + context.shape[-1]
+                encoder_attention_mask = torch.ones(
+                    (
+                        1,
+                        1,
+                        encoder_full_seq_length - encoder_cache_length,
+                        encoder_full_seq_length,
+                    ),
+                    device=context.device,
+                )
+                encoder_position_ids = torch.arange(
+                    encoder_cache_length, encoder_full_seq_length
+                ).to(device)[None, :]
+                encoder_attention_mask = self._preprocess_attention_mask(
+                    encoder_attention_mask, dtype=torch.float
+                )
+                full_seq_length = -1  # Not used
+        else:  # Not using kv-cache
+            past_key_values = None
+            encoder_past_key_values, encoder_last_hidden_state = None, None
+            if context is not None:
+                context_len = context.shape[1]
+                encoder_attention_mask = torch.ones(
+                    (1, 1, context_len, context_len), device=context.device
+                )
+                encoder_attention_mask = self._preprocess_attention_mask(
+                    encoder_attention_mask, dtype=torch.float
+                )
+                encoder_position_ids = torch.arange(context_len).to(device)[None, :]
+            else:
+                context_len = 0
+                encoder_attention_mask = None
+            if input_ids is not None:
+                full_seq_length = context_len + input_ids.shape[1]
+            else:
+                full_seq_length = context_len
+            position_ids = torch.arange(context_len, full_seq_length).to(device)[
+                None, :
+            ]
+        if input_ids is not None:
+            decoder_attention_mask = torch.ones(
+                (batch_size, 1, input_ids.shape[1], full_seq_length),
+                device=device,
+            )  # Make attention mask 4D
+            decoder_attention_mask = self._preprocess_attention_mask(
+                decoder_attention_mask, dtype=torch.float
+            )
+        else:
+            decoder_attention_mask = None
+        return DenoiserInput(
+            xt=input_ids,
+            attention_mask=decoder_attention_mask,
+            context_mask=context_mask,
+            past_key_values=past_key_values,
+            backbone_kwargs={
+                "position_ids": position_ids,
+                "encoder_input_ids": context,
+                "encoder_position_ids": encoder_position_ids,
+                "encoder_attention_mask": encoder_attention_mask,
+                "encoder_past_key_values": encoder_past_key_values,
+                "encoder_last_hidden_state": encoder_last_hidden_state,
+            }
+            | backbone_kwargs,
+        ), cache  # TODO: potentially returning cache None, violates return type

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