diffusion alt: custom loss impl

src/axolotl/integrations/diffusion/loss.py

@@ -0,0 +1,115 @@
+"""Diffusion LM loss function for integration with transformers LOSS_MAPPING."""
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+
+def ForDiffusionLMLoss(
+    logits: torch.Tensor,
+    labels: torch.Tensor,
+    vocab_size: int,
+    config: Optional[dict] = None,
+    inputs: Optional[dict] = None,
+    model: Optional[torch.nn.Module] = None,
+    **kwargs,
+) -> torch.Tensor:
+    """
+    Diffusion Language Modeling loss function.
+
+    This function computes cross-entropy loss only on masked tokens using
+    diffusion info stored by the model patch during forward pass.
+
+    Args:
+        logits: Model predictions [batch_size, seq_len, vocab_size]
+        labels: Ground truth tokens [batch_size, seq_len]
+        vocab_size: Size of vocabulary
+        config: Model configuration (contains diffusion parameters)
+        inputs: Input batch dictionary (contains input_ids, attention_mask)
+        model: The model instance (to access stored diffusion info)
+        **kwargs: Additional arguments
+
+    Returns:
+        loss: Computed diffusion loss
+    """
+    # Get diffusion info stored by model patch
+    if model is None or not hasattr(model, "_diffusion_info"):
+        # Fallback to regular causal LM loss if no diffusion info
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        loss_fct = torch.nn.CrossEntropyLoss()
+        return loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+        )
+
+    diffusion_info = model._diffusion_info
+    original_input_ids = diffusion_info["original_input_ids"]
+    masked_indices = diffusion_info["masked_indices"]
+    p_mask = diffusion_info["p_mask"]
+
+    # Get diffusion config parameters
+    diffusion_config = getattr(config, "diffusion_config", {})
+    importance_weighting = diffusion_config.get("importance_weighting", True)
+
+    # Check if we have any masked tokens
+    if not masked_indices.any():
+        return torch.tensor(0.0, device=logits.device, requires_grad=True)
+
+    # Get predictions and targets for masked positions only
+    masked_logits = logits[masked_indices]
+    masked_targets = original_input_ids[masked_indices]  # Original unmasked tokens
+
+    # Compute cross-entropy loss without reduction
+    token_loss = F.cross_entropy(
+        masked_logits.float(), masked_targets, reduction="none"
+    )
+
+    if importance_weighting:
+        # Apply importance weighting: 1 / p_mask
+        masked_p_mask = p_mask.expand_as(masked_indices)[masked_indices]
+        weighted_loss = token_loss / masked_p_mask
+
+        if labels is not None:
+            # For SFT data: normalize by answer length per sample
+            answer_mask = labels != -100
+            answer_lengths = answer_mask.sum(dim=1).float()
+
+            # Group losses by batch sample
+            batch_indices = torch.arange(
+                original_input_ids.shape[0], device=original_input_ids.device
+            )
+            batch_indices = batch_indices.unsqueeze(1).expand_as(masked_indices)
+            masked_batch_indices = batch_indices[masked_indices]
+
+            # Sum losses per sample and normalize by answer length
+            loss_per_sample = torch.zeros(
+                original_input_ids.shape[0], device=original_input_ids.device
+            )
+            for i in range(original_input_ids.shape[0]):
+                sample_mask = masked_batch_indices == i
+                if sample_mask.any():
+                    sample_loss = weighted_loss[sample_mask].sum()
+                    loss_per_sample[i] = sample_loss / max(answer_lengths[i], 1)
+
+            loss = loss_per_sample.mean()
+        else:
+            # For completion data: simple average
+            loss = weighted_loss.mean()
+    else:
+        # No importance weighting
+        loss = token_loss.mean()
+
+    return loss
+
+
+def register_diffusion_loss():
+    """Register the diffusion loss function in transformers LOSS_MAPPING."""
+    try:
+        from transformers.loss.loss_utils import LOSS_MAPPING
+
+        LOSS_MAPPING["ForDiffusionLM"] = ForDiffusionLMLoss
+        return True
+    except ImportError:
+        # Fallback for older transformers versions
+        return False

src/axolotl/integrations/diffusion/model_patch.py

@@ -0,0 +1,149 @@
+"""Model patches for diffusion training."""
+
+import torch
+
+
+def patch_model_for_bidirectional_attention(model):
+    """
+    Patch model to handle diffusion training with forward process and bidirectional
+    attention.
+
+    This monkey-patches the model's forward method to:
+    - Apply forward diffusion process (masking) during training
+    - Use bidirectional attention masks
+    - Store info for loss computation
+    """
+    original_forward = model.forward
+
+    def diffusion_forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs,
+    ):
+        # Check if this is diffusion training
+        if (
+            hasattr(self.config, "loss_type")
+            and self.config.loss_type == "ForDiffusionLM"
+            and self.training
+        ):
+
+            # Store original input_ids for loss computation
+            original_input_ids = input_ids.clone()
+
+            # Apply forward diffusion process (masking)
+            diffusion_config = getattr(self.config, "diffusion_config", {})
+            noisy_input_ids, masked_indices, p_mask = _forward_process(
+                input_ids, attention_mask, labels, diffusion_config
+            )
+
+            # Use noisy input for model forward
+            input_ids = noisy_input_ids
+
+            # Convert attention mask to bidirectional
+            if attention_mask is not None:
+                attention_mask = _create_bidirectional_attention_mask(
+                    input_ids, attention_mask
+                )
+
+            # Store diffusion info in the model for loss computation
+            self._diffusion_info = {
+                "original_input_ids": original_input_ids,
+                "masked_indices": masked_indices,
+                "p_mask": p_mask,
+            }
+
+        return original_forward(
+            input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs
+        )
+
+    # Replace the forward method
+    model.forward = diffusion_forward.__get__(model, model.__class__)
+
+
+def _create_bidirectional_attention_mask(
+    input_ids: torch.Tensor, attention_mask: torch.Tensor
+) -> torch.Tensor:
+    """
+    Create bidirectional attention mask from 2D attention mask.
+
+    Args:
+        input_ids: Input token IDs [batch_size, seq_len]
+        attention_mask: 2D attention mask [batch_size, seq_len]
+
+    Returns:
+        bidirectional_mask: 4D attention mask [batch_size, 1, seq_len, seq_len]
+    """
+    batch_size, seq_len = input_ids.shape
+
+    # Simple bidirectional mask - all tokens can attend to all valid tokens
+    # Expand 2D mask to 4D: [batch_size, seq_len] -> [batch_size, 1, seq_len, seq_len]
+    bidirectional_mask = attention_mask.unsqueeze(1).unsqueeze(2)  # [B, 1, 1, S]
+    bidirectional_mask = bidirectional_mask.expand(batch_size, 1, seq_len, seq_len)
+
+    # Apply row-wise masking (padded tokens can't attend to anything)
+    row_mask = attention_mask.unsqueeze(1).unsqueeze(3)  # [B, 1, S, 1]
+    bidirectional_mask = bidirectional_mask & row_mask
+
+    return bidirectional_mask
+
+
+def _forward_process(
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None = None,
+    labels: torch.Tensor | None = None,
+    diffusion_config: dict | None = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Apply forward diffusion process (random masking).
+
+    Args:
+        input_ids: Input token IDs [batch_size, seq_len]
+        attention_mask: Attention mask [batch_size, seq_len]
+        labels: Labels for SFT training [batch_size, seq_len]
+        diffusion_config: Diffusion configuration dict
+
+    Returns:
+        noisy_input_ids: Input with masked tokens
+        masked_indices: Boolean mask of which tokens were masked
+        p_mask: Masking probabilities used
+    """
+    if diffusion_config is None:
+        diffusion_config = {}
+
+    batch_size, seq_len = input_ids.shape
+    device = input_ids.device
+
+    eps = diffusion_config.get("eps", 1e-3)
+    mask_token_id = diffusion_config.get("mask_token_id", 128002)
+
+    # Sample random timesteps for each sample
+    t = torch.rand(batch_size, device=device)
+
+    # Calculate masking probability with epsilon
+    p_mask = (1 - eps) * t + eps  # [batch_size]
+    p_mask = p_mask.unsqueeze(1).expand(-1, seq_len)  # [batch_size, seq_len]
+
+    # Don't mask padding tokens
+    if attention_mask is not None:
+        p_mask = p_mask * attention_mask.float()
+
+    # Create random mask based on p_mask
+    random_values = torch.rand_like(p_mask)
+    masked_indices = random_values < p_mask
+
+    # Apply attention mask constraints
+    if attention_mask is not None:
+        masked_indices = masked_indices & attention_mask.bool()
+
+    # For SFT data, only mask answer tokens (where labels != -100)
+    if labels is not None:
+        answer_mask = labels != -100
+        masked_indices = masked_indices & answer_mask
+
+    # Create noisy input by replacing masked tokens
+    noisy_input_ids = input_ids.clone()
+    noisy_input_ids[masked_indices] = mask_token_id
+
+    return noisy_input_ids, masked_indices, p_mask

src/axolotl/integrations/diffusion/plugin.py

@@ -7,7 +7,10 @@ from axolotl.integrations.base import BasePlugin
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
-from .trainer import DiffusionTrainer
+from .args import DiffusionArgs
+from .callbacks import DiffusionGenerationCallback
+from .loss import register_diffusion_loss
+from .model_patch import patch_model_for_bidirectional_attention
 
 LOG = get_logger(__name__)
 
@@ -24,18 +27,70 @@ class DiffusionPlugin(BasePlugin):
         super().__init__()
         self.cfg = None
 
+        if register_diffusion_loss():
+            LOG.info("Registered ForDiffusionLM loss function")
+        else:
+            LOG.warning(
+                "Failed to register diffusion loss - older transformers version"
+            )
+
     def get_input_args(self) -> str:
         """Returns the pydantic model for LLaDA plugin arguments."""
         return "axolotl.integrations.diffusion.DiffusionArgs"
 
     def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
-        """Perform actions after model is loaded."""
+        """Configure model for diffusion training after loading."""
         self.cfg = cfg
 
-    def get_trainer_cls(self, cfg: DictDefault) -> type[DiffusionTrainer] | None:
+        # Set loss type for diffusion training
-        """Return custom trainer class for diffusion training."""
+        if hasattr(model, "config"):
-        return DiffusionTrainer
+            model.config.loss_type = "ForDiffusionLM"
 
-    def post_trainer_create(self, cfg: DictDefault, trainer: DiffusionTrainer):
+            # Store diffusion config in model config
+            model.config.diffusion_config = {
+                "eps": getattr(cfg, "eps", 1e-3),
+                "importance_weighting": getattr(cfg, "importance_weighting", True),
+                "mask_token_id": getattr(cfg, "mask_token_id", 128002),
+            }
+
+            LOG.info("Configured model for diffusion training with ForDiffusionLM loss")
+
+        # Patch model for bidirectional attention during training
+        patch_model_for_bidirectional_attention(model)
+        LOG.info("Applied bidirectional attention patch to model")
+
+        return model
+
+    def post_trainer_create(self, cfg: DictDefault, trainer):
         """Configure trainer after creation."""
-        trainer.set_config(cfg)
+        # Create diffusion config from cfg
+        diffusion_config = DiffusionArgs(
+            noise_schedule=getattr(cfg, "noise_schedule", "linear"),
+            min_mask_ratio=getattr(cfg, "min_mask_ratio", 0.1),
+            max_mask_ratio=getattr(cfg, "max_mask_ratio", 0.9),
+            num_diffusion_steps=getattr(cfg, "num_diffusion_steps", 128),
+            eps=getattr(cfg, "eps", 1e-3),
+            importance_weighting=getattr(cfg, "importance_weighting", True),
+            mask_token_id=getattr(cfg, "mask_token_id", 128002),
+            generate_samples=getattr(cfg, "generate_samples", True),
+            generation_interval=getattr(cfg, "generation_interval", 100),
+            num_generation_samples=getattr(cfg, "num_generation_samples", 3),
+            generation_steps=getattr(cfg, "generation_steps", 128),
+            generation_temperature=getattr(cfg, "generation_temperature", 0.0),
+            generation_max_length=getattr(cfg, "generation_max_length", 100),
+        )
+
+        # Store diffusion config on trainer for callbacks to access
+        trainer.diffusion_config = diffusion_config
+        LOG.info("Stored diffusion config on trainer")
+
+    def add_callbacks_post_trainer(self, cfg: DictDefault, trainer):
+        """Add diffusion generation callback if enabled."""
+        if (
+            hasattr(trainer, "diffusion_config")
+            and trainer.diffusion_config.generate_samples
+        ):
+            generation_callback = DiffusionGenerationCallback(trainer)
+            LOG.info("Added diffusion generation callback")
+            return [generation_callback]
+        return []

src/axolotl/integrations/diffusion/trainer.py

@@ -1,279 +0,0 @@
-"""Custom trainer for diffusion LM training."""
-
-from typing import Any, Literal
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from axolotl.core.trainers.base import AxolotlTrainer
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.logging import get_logger
-
-from .callbacks import DiffusionGenerationCallback
-
-LOG = get_logger(__name__)
-
-
-class DiffusionTrainer(AxolotlTrainer):  # pylint: disable=too-many-ancestors
-    """Custom trainer for diffusion LM training that overrides loss computation."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.config = None
-        self._special_token_ids = None
-
-    def set_config(self, config: DictDefault):
-        """Set config for diffusion training."""
-        self.config = config
-        self._cache_special_token_ids()
-
-        if config.generate_samples:
-            generation_callback = DiffusionGenerationCallback(self)
-            self.add_callback(generation_callback)
-
-    def compute_loss(
-        self,
-        model: nn.Module,
-        inputs: dict[str, torch.Tensor],
-        return_outputs: bool = False,
-        num_items_in_batch: torch.Tensor | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        """Override compute_loss to use diffusion loss."""
-        input_ids = inputs.get("input_ids")
-        attention_mask = inputs.get("attention_mask")
-        labels = inputs.get("labels")
-
-        if input_ids is None:
-            raise ValueError("input_ids is required for diffusion training")
-
-        loss, outputs = self._compute_diffusion_loss(
-            model, input_ids, attention_mask, labels
-        )
-
-        if return_outputs:
-            return loss, outputs
-        return loss
-
-    def _cache_special_token_ids(self):
-        """Cache special token IDs to avoid repeated tokenizer access."""
-        if self.processing_class is None:
-            self._special_token_ids = set()
-            return
-
-        tokenizer = self.processing_class
-        special_tokens = set()
-
-        if hasattr(tokenizer, "bos_token_id") and tokenizer.bos_token_id is not None:
-            special_tokens.add(tokenizer.bos_token_id)
-        if hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id is not None:
-            special_tokens.add(tokenizer.eos_token_id)
-        if hasattr(tokenizer, "pad_token_id") and tokenizer.pad_token_id is not None:
-            special_tokens.add(tokenizer.pad_token_id)
-
-        self._special_token_ids = special_tokens
-
-    @torch.compile
-    def _forward_process(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        eps: float = 1e-3,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Forward noising process. A timestep is sampled along the process, and tokens are
-        masked with probability determined by the configured noise schedule.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-            eps: Small epsilon value for minimum masking probability.
-
-        Returns:
-            noisy_batch: Input with some tokens masked.
-            masked_indices: Boolean mask indicating which tokens were masked.
-            p_mask: Masking probabilities for each token [batch_size, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        # Sample random timesteps for each sample in batch
-        t = torch.rand(batch_size, device=device)
-
-        # Calculate masking probability with epsilon
-        p_mask = (1 - eps) * t + eps  # [batch_size]
-        p_mask = p_mask[:, None].repeat(1, seq_len)  # [batch_size, seq_len]
-
-        # Don't mask padding tokens if attention_mask is provided
-        if attention_mask is not None:
-            valid_mask = attention_mask.bool()
-            p_mask = p_mask * valid_mask.float()
-
-        # Create mask to exclude special tokens
-        special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
-        if self._special_token_ids:
-            for token_id in self._special_token_ids:
-                special_token_mask |= input_ids == token_id
-
-        # Create random mask based on p_mask
-        masked_indices = torch.rand((batch_size, seq_len), device=device) < p_mask
-        masked_indices = masked_indices & ~special_token_mask
-        if attention_mask is not None:
-            masked_indices = masked_indices & attention_mask.bool()
-
-        # For SFT data, only mask answer tokens
-        if labels is not None:
-            answer_mask = labels != -100
-            masked_indices = masked_indices & answer_mask
-
-        # Create masked input
-        mask_token_id = self.config.mask_token_id
-        noisy_batch = torch.where(masked_indices, mask_token_id, input_ids)
-
-        return noisy_batch, masked_indices, p_mask
-
-    @torch.compile
-    def _create_bidirectional_attention_mask(
-        self, input_ids: torch.Tensor, attention_mask: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        """
-        Create bidirectional attention mask to override default causal masking. Handles
-        sample-packed sequences where different samples are identified by different
-        attention mask values.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len]
-
-        Returns:
-            bidirectional_mask: 4D attention mask [batch_size, 1, seq_len, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        if attention_mask is None or not self.config.sample_packing:
-            return torch.ones(
-                batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=device
-            )
-
-        # Create attention mask by comparing sample IDs element-wise
-        mask_i = attention_mask.unsqueeze(2)  # [batch_size, seq_len, 1]
-        mask_j = attention_mask.unsqueeze(1)  # [batch_size, 1, seq_len]
-
-        # Tokens can attend to each other if they have the same non-zero sample ID
-        bidirectional_mask = (mask_i == mask_j) & (mask_i > 0)
-
-        # Add head dimension: [batch_size, 1, seq_len, seq_len]
-        bidirectional_mask = bidirectional_mask.unsqueeze(1)
-
-        return bidirectional_mask
-
-    def _compute_diffusion_loss(
-        self,
-        model: nn.Module,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | Any]:
-        """
-        Compute diffusion loss.
-
-        Args:
-            model: The model to compute loss for.
-            input_ids: Ground truth token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-
-        Returns:
-            loss: Cross-entropy loss.
-            metrics: Dictionary of metrics.
-        """
-        # Apply forward process
-        noisy_batch, masked_indices, p_mask = self._forward_process(
-            input_ids, attention_mask, labels, self.config.eps
-        )
-
-        # Create bidirectional attention mask
-        bidirectional_mask = self._create_bidirectional_attention_mask(
-            input_ids, attention_mask
-        )
-
-        # Forward pass
-        outputs = model(
-            input_ids=noisy_batch,
-            attention_mask=bidirectional_mask,
-        )
-        logits = outputs.logits
-
-        if masked_indices.sum() > 0:
-            valid_indices = torch.where(masked_indices)
-            batch_indices, seq_indices = valid_indices
-
-            masked_logits = logits[batch_indices, seq_indices]
-            masked_targets = input_ids[batch_indices, seq_indices]
-            masked_p_mask = p_mask[batch_indices, seq_indices]
-
-            # Compute cross-entropy loss without reduction
-            token_loss = F.cross_entropy(
-                masked_logits.float(), masked_targets, reduction="none"
-            )
-
-            if self.config.importance_weighting:
-                masked_p_mask = masked_p_mask.float()
-                weighted_loss = token_loss / masked_p_mask
-            else:
-                weighted_loss = token_loss
-
-            # Final loss: sum weighted losses, normalize
-            if labels is not None:
-                # For SFT data: normalize by answer length per sample
-                answer_mask = labels != -100
-                answer_lengths = answer_mask.sum(dim=1).float()  # [batch_size]
-
-                # Get batch indices for masked tokens
-                masked_batch_indices = batch_indices
-
-                # Sum losses per sample and divide by answer length
-                loss_per_sample = torch.zeros(
-                    input_ids.shape[0], device=input_ids.device
-                )
-                for i in range(input_ids.shape[0]):
-                    sample_mask = masked_batch_indices == i
-                    if sample_mask.sum() > 0:
-                        sample_loss = weighted_loss[sample_mask].sum()
-                        loss_per_sample[i] = sample_loss / answer_lengths[i]
-
-                loss = loss_per_sample.mean()
-            else:
-                # Original normalization for non-SFT data
-                loss = weighted_loss.sum() / (input_ids.shape[0] * input_ids.shape[1])
-
-            ce_loss = token_loss.mean()
-
-            # Compute accuracy on masked tokens
-            with torch.no_grad():
-                pred_tokens = masked_logits.argmax(dim=-1)
-                accuracy = (pred_tokens == masked_targets).float().mean()
-        else:
-            loss = torch.tensor(0.0, device=input_ids.device, requires_grad=True)
-            accuracy = torch.tensor(0.0, device=input_ids.device)
-            ce_loss = torch.tensor(0.0, device=input_ids.device)
-            masked_p_mask = torch.tensor(1.0, device=input_ids.device)
-
-        metrics = {
-            "loss": loss.item(),
-            "accuracy": accuracy.item(),
-            "mask_ratio": masked_indices.float().mean().item(),
-            "num_masked_tokens": (masked_indices.sum().item(), "sum"),
-            "avg_p_mask": p_mask[masked_indices].mean().item(),
-            "ce_loss": ce_loss.item(),
-        }
-        if self.config.importance_weighting:
-            metrics["importance_weight_avg"] = (1.0 / masked_p_mask).mean().item()
-
-        train_eval: Literal["train", "eval"] = "train" if model.training else "eval"
-        self.store_metrics(metrics, train_eval=train_eval)
-
-        return loss, outputs

tests/integrations/test_diffusion.py

@@ -2,111 +2,180 @@
 
 # pylint: disable=redefined-outer-name,protected-access
 
-from unittest.mock import Mock
+from unittest.mock import Mock, patch
 
 import pytest
 import torch
 
-from axolotl.integrations.diffusion.trainer import DiffusionTrainer
+from axolotl.integrations.diffusion.args import DiffusionArgs
-from axolotl.utils.dict import DictDefault
+from axolotl.integrations.diffusion.loss import (
-
+    ForDiffusionLMLoss,
-
+    register_diffusion_loss,
-@pytest.fixture
+)
-def mock_tokenizer():
+from axolotl.integrations.diffusion.model_patch import (
-    """Create a mock tokenizer."""
+    _create_bidirectional_attention_mask,
-    tokenizer = Mock()
+    _forward_process,
-    tokenizer.bos_token_id = 1
+    patch_model_for_bidirectional_attention,
-    tokenizer.eos_token_id = 2
+)
-    tokenizer.pad_token_id = 0
+from axolotl.integrations.diffusion.plugin import DiffusionPlugin
-    return tokenizer
 
 
 @pytest.fixture
 def diffusion_config():
     """Create a diffusion config."""
-    return DictDefault(
+    return DiffusionArgs(
-        {
+        eps=1e-3,
-            "mask_token_id": 32000,
+        importance_weighting=False,
-            "eps": 1e-3,
+        mask_token_id=32000,
-            "importance_weighting": False,
+        generate_samples=False,
-            "sample_packing": False,
-        }
     )
 
 
 @pytest.fixture
-def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
+def mock_model():
-    """Create a diffusion trainer instance for testing methods directly."""
+    """Create a mock model."""
-    # Create a minimal trainer instance just for testing methods
+    model = Mock()
-    trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
+    model.config = Mock()
-    trainer.config = diffusion_config
+    model.config.loss_type = "ForDiffusionLM"
-    trainer._special_token_ids = {0, 1, 2}  # pad, bos, eos
+    model.config.diffusion_config = {
-    trainer.processing_class = mock_tokenizer
+        "eps": 1e-3,
-    trainer.store_metrics = Mock()  # Mock metrics storage
+        "importance_weighting": False,
-    return trainer
+        "mask_token_id": 32000,
+    }
+    model.training = True
+    return model
 
 
-class TestDiffusionTrainer:
+class TestDiffusionLoss:
-    """Test the DiffusionTrainer class."""
+    """Test the ForDiffusionLMLoss function."""
 
-    def test_forward_process_basic(self, diffusion_trainer_instance):
+    def test_loss_with_diffusion_info(self, mock_model):
-        """Test basic forward process without labels."""
+        """Test loss computation with stored diffusion info."""
+        batch_size, seq_len, vocab_size = 1, 5, 1000
+
+        # Mock stored diffusion info
+        original_input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        masked_indices = torch.tensor(
+            [[False, True, True, False, False]], dtype=torch.bool
+        )
+        p_mask = torch.tensor([[0.5, 0.5, 0.5, 0.5, 0.5]], dtype=torch.float)
+
+        mock_model._diffusion_info = {
+            "original_input_ids": original_input_ids,
+            "masked_indices": masked_indices,
+            "p_mask": p_mask,
+        }
+
+        # Mock logits
+        logits = torch.randn(batch_size, seq_len, vocab_size, requires_grad=True)
+        labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
+
+        loss = ForDiffusionLMLoss(
+            logits=logits,
+            labels=labels,
+            vocab_size=vocab_size,
+            config=mock_model.config,
+            model=mock_model,
+        )
+
+        assert isinstance(loss, torch.Tensor)
+        assert loss.requires_grad
+        assert loss.item() >= 0
+
+    def test_loss_fallback_without_diffusion_info(self, mock_model):
+        """Test fallback to causal LM loss when no diffusion info."""
+        batch_size, seq_len, vocab_size = 1, 5, 1000
+
+        # Remove diffusion info to trigger fallback
+        if hasattr(mock_model, "_diffusion_info"):
+            delattr(mock_model, "_diffusion_info")
+
+        logits = torch.randn(batch_size, seq_len, vocab_size, requires_grad=True)
+        labels = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+
+        loss = ForDiffusionLMLoss(
+            logits=logits,
+            labels=labels,
+            vocab_size=vocab_size,
+            config=mock_model.config,
+            model=mock_model,
+        )
+
+        assert isinstance(loss, torch.Tensor)
+        assert loss.requires_grad
+
+    def test_loss_no_masked_tokens(self, mock_model):
+        """Test loss when no tokens are masked."""
+        batch_size, seq_len, vocab_size = 1, 3, 1000
+
+        # No masked tokens
+        original_input_ids = torch.tensor([[1, 10, 2]], dtype=torch.long)
+        masked_indices = torch.tensor([[False, False, False]], dtype=torch.bool)
+        p_mask = torch.tensor([[0.1, 0.1, 0.1]], dtype=torch.float)
+
+        mock_model._diffusion_info = {
+            "original_input_ids": original_input_ids,
+            "masked_indices": masked_indices,
+            "p_mask": p_mask,
+        }
+
+        logits = torch.randn(batch_size, seq_len, vocab_size)
+        labels = torch.tensor([[1, 10, 2]], dtype=torch.long)
+
+        loss = ForDiffusionLMLoss(
+            logits=logits,
+            labels=labels,
+            vocab_size=vocab_size,
+            config=mock_model.config,
+            model=mock_model,
+        )
+
+        assert loss.item() == 0.0
+
+
+class TestModelPatch:
+    """Test the model patching functionality."""
+
+    def test_forward_process_basic(self):
+        """Test basic forward process."""
         input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        diffusion_config = {"eps": 0.1, "mask_token_id": 32000}
 
-        noisy_batch, masked_indices, p_mask = (
+        noisy_input_ids, masked_indices, p_mask = _forward_process(
-            diffusion_trainer_instance._forward_process(input_ids, eps=0.1)
+            input_ids, diffusion_config=diffusion_config
         )
 
         # Check shapes
-        assert noisy_batch.shape == input_ids.shape
+        assert noisy_input_ids.shape == input_ids.shape
         assert masked_indices.shape == input_ids.shape
         assert p_mask.shape == input_ids.shape
 
-        # Check that special tokens are not masked
+        # Check that mask token is applied where masked
-        special_token_positions = (input_ids == 1) | (input_ids == 2) | (input_ids == 0)
+        if masked_indices.any():
-        assert not masked_indices[special_token_positions].any()
+            assert (noisy_input_ids[masked_indices] == 32000).all()
 
-        # Check that mask token is applied
+    def test_forward_process_with_labels(self):
-        mask_token_id = diffusion_trainer_instance._config.mask_token_id
-        masked_positions = masked_indices
-        if masked_positions.any():
-            assert (noisy_batch[masked_positions] == mask_token_id).all()
-
-    def test_forward_process_with_labels(self, diffusion_trainer_instance):
         """Test forward process with SFT labels."""
         input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
         labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
+        diffusion_config = {"eps": 0.1, "mask_token_id": 32000}
 
-        noisy_batch, masked_indices, p_mask = (
+        _, masked_indices, _ = _forward_process(
-            diffusion_trainer_instance._forward_process(
+            input_ids, labels=labels, diffusion_config=diffusion_config
-                input_ids, labels=labels, eps=0.1
-            )
         )
 
-        # Check shapes
-        assert noisy_batch.shape == input_ids.shape
-        assert masked_indices.shape == input_ids.shape
-        assert p_mask.shape == input_ids.shape
-
         # Check that only answer tokens can be masked (where labels != -100)
         non_answer_mask = labels == -100
-
-        # No masking should occur on non-answer tokens
         assert not masked_indices[non_answer_mask].any()
 
-        # p_mask should be the same for all positions (sampled timestep),
+    def test_forward_process_with_attention_mask(self):
-        # but masking is only applied to answer tokens
-        assert p_mask.shape == input_ids.shape
-        # Verify that masked_indices respects the answer mask
-        assert not masked_indices[non_answer_mask].any()
-
-    def test_forward_process_with_attention_mask(self, diffusion_trainer_instance):
         """Test forward process with attention mask."""
         input_ids = torch.tensor([[1, 10, 20, 0]], dtype=torch.long)
         attention_mask = torch.tensor([[1, 1, 1, 0]], dtype=torch.long)
+        diffusion_config = {"eps": 0.1, "mask_token_id": 32000}
 
-        _, masked_indices, p_mask = diffusion_trainer_instance._forward_process(
+        _, masked_indices, p_mask = _forward_process(
-            input_ids, attention_mask=attention_mask, eps=0.1
+            input_ids, attention_mask=attention_mask, diffusion_config=diffusion_config
         )
 
         # Check that padding tokens are not masked
@@ -114,158 +183,153 @@ class TestDiffusionTrainer:
         assert not masked_indices[padding_positions].any()
         assert (p_mask[padding_positions] == 0).all()
 
-    def test_bidirectional_attention_mask_no_packing(self, diffusion_trainer_instance):
+    def test_bidirectional_attention_mask(self):
-        """Test bidirectional attention mask without sample packing."""
+        """Test bidirectional attention mask creation."""
         input_ids = torch.tensor([[1, 10, 20, 2]], dtype=torch.long)
+        attention_mask = torch.tensor([[1, 1, 1, 1]], dtype=torch.long)
 
-        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
+        mask = _create_bidirectional_attention_mask(input_ids, attention_mask)
-            input_ids
-        )
 
         # Should be all-to-all attention
         expected_shape = (1, 1, 4, 4)
         assert mask.shape == expected_shape
         assert mask.all()
 
-    def test_bidirectional_attention_mask_with_packing(
+    def test_bidirectional_attention_mask_with_padding(self):
-        self, diffusion_trainer_instance
+        """Test bidirectional attention mask with padding."""
-    ):
+        input_ids = torch.tensor([[1, 10, 20, 0]], dtype=torch.long)
-        """Test bidirectional attention mask with sample packing."""
+        attention_mask = torch.tensor([[1, 1, 1, 0]], dtype=torch.long)
-        diffusion_trainer_instance._config.sample_packing = True
-        input_ids = torch.tensor([[1, 10, 20, 30, 40, 2]], dtype=torch.long)
-        # Sample IDs: first sample (1), second sample (2)
-        attention_mask = torch.tensor([[1, 1, 1, 2, 2, 2]], dtype=torch.long)
 
-        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
+        mask = _create_bidirectional_attention_mask(input_ids, attention_mask)
-            input_ids, attention_mask
-        )
 
-        # Check that tokens within same sample can attend to each other
+        # Padding positions should not attend or be attended to
-        # but not across samples
+        assert not mask[0, 0, 3, :].any()  # Padding can't attend to anything
-        assert mask[0, 0, 0, 1].item()  # First sample tokens can attend to each other
+        assert not mask[0, 0, :, 3].any()  # Nothing can attend to padding
-        assert mask[0, 0, 1, 2].item()
-        assert not mask[0, 0, 0, 3].item()  # Can't attend across samples
-        assert not mask[0, 0, 2, 4].item()
-        assert mask[0, 0, 3, 4].item()  # Second sample tokens can attend to each other
 
-    def test_compute_loss_basic(self, diffusion_trainer_instance):
+    def test_patch_model_for_bidirectional_attention(self):
-        """Test basic loss computation."""
+        """Test that model patching works."""
-        # Mock model that returns logits
         mock_model = Mock()
-        mock_outputs = Mock()
+        mock_model.config = Mock()
-        vocab_size = 1000
+        mock_model.config.loss_type = "ForDiffusionLM"
-        seq_len = 5
+        mock_model.config.diffusion_config = {"eps": 1e-3, "mask_token_id": 32000}
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
-        mock_model.return_value = mock_outputs
         mock_model.training = True
 
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        original_forward = Mock()
+        mock_model.forward = original_forward
 
-        loss, outputs = diffusion_trainer_instance._compute_diffusion_loss(
+        # Patch the model
-            mock_model, input_ids
+        patch_model_for_bidirectional_attention(mock_model)
-        )
 
-        # Check that loss is computed
+        # Check that forward method was replaced
-        assert isinstance(loss, torch.Tensor)
+        assert mock_model.forward != original_forward
-        assert loss.requires_grad
-        assert outputs == mock_outputs
 
-        # Check that metrics were stored
-        diffusion_trainer_instance.store_metrics.assert_called_once()
 
-    def test_compute_loss_with_labels(self, diffusion_trainer_instance):
+class TestDiffusionPlugin:
-        """Test loss computation with SFT labels."""
+    """Test the DiffusionPlugin."""
-        # Mock model
+
+    def test_plugin_registers_loss_function(self):
+        """Test that plugin registers diffusion loss function."""
+        with patch(
+            "axolotl.integrations.diffusion.plugin.register_diffusion_loss",
+            return_value=True,
+        ) as mock_register:
+            plugin = DiffusionPlugin()
+            mock_register.assert_called_once()
+
+    def test_post_model_load_configuration(self):
+        """Test that post_model_load configures model correctly."""
+        plugin = DiffusionPlugin()
+
+        # Mock model and config
         mock_model = Mock()
-        mock_outputs = Mock()
+        mock_model.config = Mock()
-        vocab_size = 1000
+        mock_cfg = Mock()
-        seq_len = 5
+        mock_cfg.eps = 1e-3
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
+        mock_cfg.importance_weighting = True
-        mock_model.return_value = mock_outputs
+        mock_cfg.mask_token_id = 32000
-        mock_model.training = True
 
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        with patch(
-        labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
+            "axolotl.integrations.diffusion.plugin.patch_model_for_bidirectional_attention"
+        ) as mock_patch:
+            result = plugin.post_model_load(mock_cfg, mock_model)
 
-        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
+            # Check model configuration
-            mock_model, input_ids, labels=labels
+            assert mock_model.config.loss_type == "ForDiffusionLM"
-        )
+            assert mock_model.config.diffusion_config is not None
+            assert mock_model.config.diffusion_config["eps"] == 1e-3
 
-        # Check that loss is computed
+            # Check model was patched
-        assert isinstance(loss, torch.Tensor)
+            mock_patch.assert_called_once_with(mock_model)
-        assert loss.requires_grad
 
-        # Check that SFT metrics were added
+            # Should return the model
-        call_args = diffusion_trainer_instance.store_metrics.call_args[0][0]
+            assert result == mock_model
-        assert "answer_ratio" in call_args
-        assert "avg_answer_length" in call_args
 
-    def test_compute_loss_no_masked_tokens(self, diffusion_trainer_instance):
+    def test_post_trainer_create_stores_config(self, diffusion_config):
-        """Test loss computation when no tokens are masked."""
+        """Test that post_trainer_create stores config on trainer."""
-        # Mock model
+        plugin = DiffusionPlugin()
-        mock_model = Mock()
+        mock_trainer = Mock()
-        mock_outputs = Mock()
+        mock_cfg = Mock()
-        vocab_size = 1000
-        seq_len = 3
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
 
-        # Only special tokens (which won't be masked)
+        # Set config attributes
-        input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
+        for attr, value in diffusion_config.model_dump().items():
+            setattr(mock_cfg, attr, value)
 
-        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
+        plugin.post_trainer_create(mock_cfg, mock_trainer)
-            mock_model, input_ids
-        )
 
-        # Loss should be zero when no tokens are masked
+        # Check that diffusion config was stored on trainer
-        assert loss.item() == 0.0
+        assert hasattr(mock_trainer, "diffusion_config")
-        assert loss.requires_grad
+        assert mock_trainer.diffusion_config.eps == diffusion_config.eps
 
-    def test_cache_special_token_ids(self, diffusion_trainer_instance):
+    def test_add_callbacks_post_trainer_with_generation_enabled(self):
-        """Test caching of special token IDs."""
+        """Test callback addition when generation is enabled."""
-        # Should cache BOS, EOS, PAD tokens
+        plugin = DiffusionPlugin()
-        expected_tokens = {0, 1, 2}  # pad, bos, eos
+        mock_trainer = Mock()
-        assert diffusion_trainer_instance._special_token_ids == expected_tokens
+        mock_cfg = Mock()
 
-    def test_cache_special_token_ids_no_tokenizer(self):
+        # Mock trainer with diffusion config that has generation enabled
-        """Test caching when no tokenizer is available."""
+        mock_trainer.diffusion_config = DiffusionArgs(generate_samples=True)
-        trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
-        trainer.processing_class = None
-        trainer._cache_special_token_ids()
 
-        assert trainer._special_token_ids == set()
+        with patch(
+            "axolotl.integrations.diffusion.plugin.DiffusionGenerationCallback"
+        ) as mock_callback_class:
+            callbacks = plugin.add_callbacks_post_trainer(mock_cfg, mock_trainer)
 
-    def test_main_compute_loss_interface(self, diffusion_trainer_instance):
+            # Should return one callback
-        """Test the main compute_loss interface."""
+            assert len(callbacks) == 1
-        # Mock model
+            mock_callback_class.assert_called_once_with(mock_trainer)
-        mock_model = Mock()
-        mock_outputs = Mock()
-        mock_outputs.logits = torch.randn(1, 5, 1000)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
 
-        inputs = {
+    def test_add_callbacks_post_trainer_with_generation_disabled(self):
-            "input_ids": torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long),
+        """Test callback addition when generation is disabled."""
-            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.long),
+        plugin = DiffusionPlugin()
-            "labels": torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long),
+        mock_trainer = Mock()
-        }
+        mock_cfg = Mock()
 
-        # Test without return_outputs
+        # Mock trainer with diffusion config that has generation disabled
-        loss = diffusion_trainer_instance.compute_loss(mock_model, inputs)
+        mock_trainer.diffusion_config = DiffusionArgs(generate_samples=False)
-        assert isinstance(loss, torch.Tensor)
 
-        # Test with return_outputs
+        callbacks = plugin.add_callbacks_post_trainer(mock_cfg, mock_trainer)
-        loss, outputs = diffusion_trainer_instance.compute_loss(
-            mock_model, inputs, return_outputs=True
-        )
-        assert isinstance(loss, torch.Tensor)
-        assert outputs == mock_outputs
 
-    def test_missing_input_ids_raises_error(self, diffusion_trainer_instance):
+        # Should return no callbacks
-        """Test that missing input_ids raises ValueError."""
+        assert len(callbacks) == 0
-        mock_model = Mock()
-        inputs = {"attention_mask": torch.tensor([[1, 1, 1]])}
 
-        with pytest.raises(ValueError, match="input_ids is required"):
+
-            diffusion_trainer_instance.compute_loss(mock_model, inputs)
+class TestLossRegistration:
+    """Test loss function registration."""
+
+    def test_register_diffusion_loss(self):
+        """Test that loss function can be registered."""
+        with patch("transformers.loss.loss_utils.LOSS_MAPPING", {}) as mock_mapping:
+            result = register_diffusion_loss()
+            assert result is True
+            assert "ForDiffusionLM" in mock_mapping
+            assert mock_mapping["ForDiffusionLM"] == ForDiffusionLMLoss
+
+    def test_register_diffusion_loss_import_error(self):
+        """Test fallback when LOSS_MAPPING import fails."""
+        # Patch the import to raise ImportError
+        with patch(
+            "builtins.__import__",
+            side_effect=ImportError("transformers.loss.loss_utils not found"),
+        ):
+            result = register_diffusion_loss()
+            assert result is False