modeling_diffusion_lm.py

"""Modeling for DiffusionLM: masked (absorbing-state) discrete diffusion
language model, LLaDA / Diffusion-LM style.

Bidirectional transformer over a sequence where a fraction t of tokens is
replaced by a learned [MASK] embedding. Time conditioning is configurable:
legacy additive, bounded normalized, or absent. The model predicts the original token
at masked positions. Training loss: masked-position cross-entropy weighted
by 1/t and normalized by source token count, with t ~ U(0, 1).

Generation: iterative denoising from all-[MASK]; at each step the most
confident tokens are committed (confidence = max softmax prob), the rest stay
masked.

Follows the transformers v5 modeling pattern where applicable
(masking_utils, GradientCheckpointingLayer).
"""

import math
from collections.abc import Callable

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.masking_utils import create_bidirectional_mask
from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.modeling_outputs import MaskedLMOutput
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs

from .configuration_diffusion_lm import DiffusionLMConfig


def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def _apply_rotary_pos_emb(x, cos, sin):
    cos = cos.unsqueeze(1)
    sin = sin.unsqueeze(1)
    return x * cos + _rotate_half(x) * sin


class DiffusionRMSNorm(nn.Module):
    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return (self.weight * hidden_states.to(input_dtype)).to(input_dtype)


class DiffusionRotaryEmbedding(nn.Module):
    def __init__(self, config: DiffusionLMConfig, device=None):
        super().__init__()
        self.config = config
        inv_freq = 1.0 / (
            config.rope_theta
            ** (torch.arange(0, config.head_dim, 2, dtype=torch.float32, device=device) / config.head_dim)
        )
        self.inv_freq = nn.Buffer(inv_freq, persistent=True)

    @torch.no_grad()
    def forward(self, x, position_ids):
        inv_freq_expanded = (
            self.inv_freq[None, :, None].expand(position_ids.shape[0], -1, 1)
            .to(dtype=torch.float32, device=x.device)
        )
        position_ids_expanded = position_ids[:, None, :].float()
        freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb.cos().to(dtype=x.dtype), emb.sin().to(dtype=x.dtype)


def _eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()
    return attn_output, attn_weights


def _sdpa_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    """Local SDPA wrapper. HF's generic SDPA interface falls back to the math
    backend for GQA shapes (q heads != kv heads), which materializes
    (batch, heads, seq, seq) fp32 attention scores. Expanding kv first keeps
    the fused flash / memory-efficient kernels eligible."""
    n_rep = getattr(module, "num_key_value_groups", 1)
    is_causal = (
        attention_mask is None
        and getattr(module, "is_causal", False)
        and query.shape[2] > 1
    )
    attn_output = nn.functional.scaled_dot_product_attention(
        query,
        key,
        value,
        attn_mask=attention_mask,
        dropout_p=dropout,
        is_causal=is_causal,
        scale=scaling,
    )
    return attn_output.transpose(1, 2).contiguous(), None


class DiffusionAttention(nn.Module):
    def __init__(self, config: DiffusionLMConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = config.head_dim
        self.num_heads = config.num_attention_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = False

        inner = self.num_heads * self.head_dim
        self.q_proj = nn.Linear(config.hidden_size, inner, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, inner, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, inner, bias=False)
        self.o_proj = nn.Linear(inner, config.hidden_size, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)
        q = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        k = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        v = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        q = _apply_rotary_pos_emb(q, cos, sin)
        k = _apply_rotary_pos_emb(k, cos, sin)

        if self.config._attn_implementation == "sdpa":
            attention_interface: Callable = _sdpa_attention_forward
        else:
            attention_interface = ALL_ATTENTION_FUNCTIONS.get_interface(
                self.config._attn_implementation, _eager_attention_forward
            )
        attn_output, attn_weights = attention_interface(
            self,
            q, k, v,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )
        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class DiffusionMLP(nn.Module):
    def __init__(self, config: DiffusionLMConfig):
        super().__init__()
        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)

    def forward(self, hidden_states):
        return self.down_proj(F.silu(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))


class DiffusionLayer(GradientCheckpointingLayer):
    def __init__(self, config: DiffusionLMConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = DiffusionAttention(config, layer_idx)
        self.mlp = DiffusionMLP(config)
        self.input_layernorm = DiffusionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = DiffusionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        hidden_states = hidden_states + self.self_attn(
            self.input_layernorm(hidden_states),
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            **kwargs,
        )[0]
        hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states))
        return hidden_states


class DiffusionLMPreTrainedModel(PreTrainedModel):
    config_class = DiffusionLMConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DiffusionLayer"]
    _supports_sdpa = True
    _supports_flash_attn = False
    _supports_flex_attn = False


class DiffusionLMModel(DiffusionLMPreTrainedModel):
    def __init__(self, config: DiffusionLMConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        # learned [MASK] vector used for masked positions
        self.mask_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
        self.layers = nn.ModuleList(
            [DiffusionLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = DiffusionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = DiffusionRotaryEmbedding(config=config)
        self.timestep_proj = nn.Sequential(
            nn.Linear(config.hidden_size, config.hidden_size),
            nn.SiLU(),
            nn.Linear(config.hidden_size, config.hidden_size),
        )
        self.gradient_checkpointing = False
        self.post_init()

    @staticmethod
    def _timestep_embedding(timesteps: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
        half = dim // 2
        exponent = -math.log(max_period) * torch.arange(
            half, dtype=torch.float32, device=timesteps.device
        )
        emb = torch.exp(exponent / half)
        emb = timesteps.float()[:, None] * emb[None, :]
        return torch.cat([emb.cos(), emb.sin()], dim=-1)

    def forward(
        self,
        input_ids: torch.LongTensor,
        timesteps: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        inputs_embeds = self.embed_tokens(input_ids.clamp(0, self.config.vocab_size - 1))
        is_mask = (input_ids == self.config.mask_token_id).unsqueeze(-1)
        inputs_embeds = torch.where(
            is_mask, self.mask_embedding.to(inputs_embeds.dtype), inputs_embeds
        )
        # Preserve legacy checkpoint behavior; the recovery recipe disables this
        # branch to avoid a shared time vector dominating token content.
        if self.config.time_conditioning != 'none':
            t_emb = self._timestep_embedding(timesteps, self.config.hidden_size)
            t_emb = self.timestep_proj(t_emb.to(self.timestep_proj[0].weight.dtype)).to(inputs_embeds.dtype)
            if self.config.time_conditioning == 'normalized':
                t_emb = F.normalize(t_emb.float(), dim=-1) * (self.config.hidden_size ** .5 * self.config.time_conditioning_scale)
                t_emb = t_emb.to(inputs_embeds.dtype)
            inputs_embeds = inputs_embeds + t_emb[:, None, :]

        if position_ids is None:
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)

        # Fully bidirectional unpadded attention needs no mask, even in a
        # compiled graph. A dense all-true mask prevents the fastest SDPA path.
        padding_mask = None if attention_mask is None else create_bidirectional_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
        )
        position_embeddings = self.rotary_emb(inputs_embeds, position_ids=position_ids)

        hidden_states = inputs_embeds
        for layer in self.layers:
            if self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    layer.forward, hidden_states, padding_mask, position_embeddings
                )
            else:
                hidden_states = layer(
                    hidden_states,
                    attention_mask=padding_mask,
                    position_embeddings=position_embeddings,
                    **kwargs,
                )
        hidden_states = self.norm(hidden_states)
        return hidden_states


class DiffusionLMForMaskedLM(DiffusionLMPreTrainedModel):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}

    def __init__(self, config: DiffusionLMConfig):
        super().__init__(config)
        self.model = DiffusionLMModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def forward(
        self,
        input_ids: torch.LongTensor,
        timesteps: torch.Tensor,
        labels: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MaskedLMOutput:
        hidden_states = self.model(
            input_ids=input_ids,
            timesteps=timesteps,
            attention_mask=attention_mask,
            **kwargs,
        )
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            token_loss = F.cross_entropy(
                logits.float().view(-1, logits.size(-1)),
                labels.view(-1),
                ignore_index=-100,
                reduction="none",
            )
            # Linear absorbing noise: E[sum(masked CE / t)] / source tokens.
            loss = (token_loss.view_as(labels) / timesteps[:, None].clamp_min(1e-5)).mean()
        return MaskedLMOutput(loss=loss, logits=logits)

    @torch.no_grad()
    def generate_masked(
        self,
        batch_size: int,
        seq_len: int,
        steps: int | None = None,
        temperature: float = 0.0,
        device: torch.device | str | None = None,
        attention_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Iterative denoising from all-[MASK] to a fully unmasked sequence.

        Commit the most confident remaining predictions on a linear schedule.
        Noise time decreases from one to zero; committed tokens never change.
        """
        steps = self.config.num_diffusion_steps if steps is None else steps
        if steps < 1 or temperature < 0:
            raise ValueError("steps must be positive and temperature nonnegative")
        device = device or next(self.parameters()).device
        mask_id = self.config.mask_token_id
        x = torch.full((batch_size, seq_len), mask_id, dtype=torch.long, device=device)
        if attention_mask is None:
            attention_mask = torch.ones_like(x)
        valid = attention_mask.bool()
        x.masked_fill_(~valid, self.config.pad_token_id)
        lengths = valid.sum(dim=1)

        for i in range(steps):
            t = 1.0 - i / steps
            timesteps = torch.full((batch_size,), t, device=device)
            logits = self(input_ids=x, timesteps=timesteps, attention_mask=attention_mask).logits
            probs = torch.softmax(logits.float(), dim=-1)
            if temperature > 0:
                sampling_probs = torch.softmax(logits.float() / temperature, dim=-1)
                predicted = torch.multinomial(sampling_probs.reshape(-1, sampling_probs.shape[-1]), 1).reshape_as(x)
                confidence = probs.gather(-1, predicted[..., None]).squeeze(-1)
            else:
                confidence, predicted = probs.max(dim=-1)

            masked = (x == mask_id) & valid
            remaining = masked.sum(dim=1)
            desired_remaining = torch.floor(lengths * (1.0 - (i + 1) / steps)).long()
            n_commit = (remaining - desired_remaining).clamp_min(0)
            order = confidence.masked_fill(~masked, -torch.inf).argsort(dim=1, descending=True)
            rank = order.argsort(dim=1)
            commit = masked & (rank < n_commit[:, None])

            x = torch.where(commit, predicted, x)
        return x