wip

src/axolotl/core/trainers/base.py

@@ -274,6 +274,18 @@ class AxolotlTrainer(
                     num_workers=self.args.dataloader_num_workers,
                     rank=self.args.process_index,
                 )
+        if (self.args.accelerator_config is not None
+            and self.args.accelerator_config.split_batches
+            and self.args.accelerator_config.dispatch_batches
+        ):
+            if self.args.sample_packing and self.args.pretraining:
+                if not self.args.eval_sample_packing and not is_training:
+                    dataloader_params["batch_size"] *= self.accelerator.num_processes
+                else:
+                    dataloader_params["batch_size"] = self.accelerator.num_processes
+            elif not self.args.sample_packing and self.args.pretraining:
+                dataloader_params["batch_size"] *= self.accelerator.num_processes
+
         if self.args.sample_packing and (
             (is_training and not self.args.pretraining)
             or (not is_training and self.args.eval_sample_packing is not False)

src/axolotl/integrations/diffusion/README.md

@@ -64,25 +64,11 @@ learning_rate: 3e-4
 
 ## Supported Models
 
-Currently supported base model types:
+Any models that support 4D attention masks should work out of the box. If not, please
-- **Llama** (meta-llama/Llama-*, etc.) - Uses `LlamaForDiffusionLM`
+create an [issue](https://github.com/axolotl-ai-cloud/axolotl/issues)!
-- **Mistral** (mistralai/Mistral-*, etc.) - Uses `MistralForDiffusionLM`
-
-The plugin automatically creates custom model classes that inherit from the base model
-while adding diffusion training capabilities. This provides full compatibility with
-HuggingFace's ecosystem for saving, loading, and inference.
 
 ## How It Works
 
-### Custom Model Architecture
-
-The plugin creates custom model classes (`LlamaForDiffusionLM`, `MistralForDiffusionLM`) that inherit from
-standard HuggingFace models. During training, these models:
-
-1. **Apply forward diffusion process**: Randomly mask tokens based on sampled timesteps
-2. **Use bidirectional attention**: Override causal attention with full bidirectional attention
-3. **Compute diffusion loss**: Calculate loss only on masked tokens with optional importance weighting
-
 ### Random Masking
 During training, tokens are randomly masked based on a sampled timestep:
 - Sample timestep `t` uniformly from [0, 1]
@@ -90,10 +76,11 @@ During training, tokens are randomly masked based on a sampled timestep:
 - Randomly mask tokens with probability `p`
 
 ### Bidirectional Attention
-The models override causal attention with bidirectional attention:
+The plugin uses native 4D attention masks to:
-- Creates 4D attention masks allowing all-to-all attention
+- Enable bidirectional attention without patches
-- Maintains proper padding and sample packing masks
+- Allow all tokens to attend to all other tokens
-- Compatible with standard HuggingFace attention implementations
+- Maintain proper padding masks
+- Work with modern `transformers` models out of the box
 
 ### Diffusion Loss
 
@@ -103,22 +90,6 @@ Loss is computed only on masked tokens with (optional) importance weighting:
 loss = sum(cross_entropy(pred, target) / p_mask) / total_tokens
 ```
 
-### Model Loading and Saving
-
-The custom models work seamlessly with HuggingFace's AutoModel system:
-
-```python
-from transformers import AutoModel, AutoConfig
-
-# Load a diffusion model
-model = AutoModel.from_pretrained("path/to/diffusion/model", trust_remote_code=True)
-
-# Save a diffusion model
-model.save_pretrained("path/to/save/diffusion/model")
-```
-
-During inference, the models behave like standard causal language models.
-
 ## Sample Generation
 
 When `generate_samples: true`, the plugin generates samples during training:
@@ -144,19 +115,9 @@ The plugin adds several metrics to track diffusion training:
 - `train/ce_loss`: Unweighted cross-entropy loss
 - `train/importance_weight_avg`: Average importance weight
 
-## Benefits of Custom Model Approach
-
-✅ **Type Safety**: Full IDE support and type checking  
-✅ **HuggingFace Integration**: Works with AutoModel, Hub, pipelines  
-✅ **Maintainability**: Clean architecture, no monkey patching  
-✅ **Ecosystem Compatibility**: Standard save/load, PEFT support  
-✅ **Testing**: Easier to test and debug  
-
 ## Limitations
 
-- **Model Support**: Currently limited to Llama and Mistral architectures
+- No flash attention support
-- **Flash Attention**: Not yet optimized for flash attention
-- **Inference Speed**: Bidirectional attention is slower than causal for generation
 
 ## References
 

src/axolotl/integrations/diffusion/__init__.py

@@ -1,26 +1,6 @@
 """Diffusion LM training plugin init."""
 
-from transformers import AutoConfig, AutoModel
-
 from .args import DiffusionArgs
-from .configuration import DiffusionConfig, LlamaForDiffusionConfig, MistralForDiffusionConfig
-from .models import LlamaForDiffusionLM, MistralForDiffusionLM
 from .plugin import DiffusionPlugin
 
-# Register custom configurations
+__all__ = ["DiffusionArgs", "DiffusionPlugin"]
-AutoConfig.register("llama_diffusion", LlamaForDiffusionConfig)
-AutoConfig.register("mistral_diffusion", MistralForDiffusionConfig)
-
-# Register custom models
-AutoModel.register(LlamaForDiffusionConfig, LlamaForDiffusionLM)
-AutoModel.register(MistralForDiffusionConfig, MistralForDiffusionLM)
-
-__all__ = [
-    "DiffusionArgs", 
-    "DiffusionPlugin",
-    "DiffusionConfig",
-    "LlamaForDiffusionConfig",
-    "MistralForDiffusionConfig", 
-    "LlamaForDiffusionLM",
-    "MistralForDiffusionLM",
-]

src/axolotl/integrations/diffusion/callbacks.py

@@ -26,31 +26,29 @@ class DiffusionGenerationCallback(TrainerCallback):
         **kwargs,
     ):
         """Generate samples at specified intervals."""
-        config = getattr(self.trainer, 'diffusion_config', self.trainer.args)
-        
         if (
             state.global_step > 0
-            and state.global_step % config.get('generation_interval', 100) == 0
+            and state.global_step % self.trainer.config.generation_interval == 0
         ):
             # Use eval dataloader if available, otherwise use train dataloader
             if (
                 hasattr(self.trainer, "eval_dataset")
                 and self.trainer.eval_dataset is not None
             ):
-                dataloader = self.trainer.get_eval_dataloader()
+                dataloader = self.trainer.callback_handler.eval_dataloader
             else:
-                dataloader = self.trainer.get_train_dataloader()
+                dataloader = self.trainer.callback_handler.train_dataloader
 
             # Generate samples
             samples = generate_samples(
                 model=self.trainer.model,
                 tokenizer=self.trainer.tokenizer,
                 dataloader=dataloader,
-                num_generation_samples=config.get('num_generation_samples', 3),
+                num_generation_samples=self.trainer.config.num_generation_samples,
-                max_length=config.get('generation_max_length', 256),
+                max_length=self.trainer.config.generation_max_length,
-                num_diffusion_steps=config.get('generation_steps', 10),
+                num_diffusion_steps=self.trainer.config.generation_steps,
-                temperature=config.get('generation_temperature', 1.0),
+                temperature=self.trainer.config.generation_temperature,
-                mask_token_id=config.get('mask_token_id', 32000),
+                mask_token_id=self.trainer.config.mask_token_id,
             )
 
             # Log samples
@@ -83,8 +81,7 @@ class DiffusionGenerationCallback(TrainerCallback):
 
         LOG.info("=" * 60)
 
-        config = getattr(self.trainer, 'diffusion_config', self.trainer.args)
+        if self.trainer.config.use_wandb and self.trainer.state.is_world_process_zero:
-        if config.get('use_wandb', False) and self.trainer.state.is_world_process_zero:
             if wandb.run is not None:
                 wandb.log(
                     {

src/axolotl/integrations/diffusion/configuration.py

@@ -1,71 +0,0 @@
-"""Configuration classes for diffusion language models."""
-
-from transformers import LlamaConfig, MistralConfig
-
-
-class LlamaForDiffusionConfig(LlamaConfig):
-    """Configuration class for Llama models with diffusion training."""
-    
-    model_type = "llama_diffusion"
-    
-    def __init__(
-        self,
-        mask_token_id: int = 32000,
-        eps: float = 1e-3,
-        importance_weighting: bool = False,
-        sample_packing: bool = False,
-        min_mask_ratio: float = 0.0,
-        max_mask_ratio: float = 1.0,
-        noise_schedule: str = "linear",
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-        # Diffusion-specific parameters
-        self.mask_token_id = mask_token_id
-        self.eps = eps
-        self.importance_weighting = importance_weighting
-        self.sample_packing = sample_packing
-        self.min_mask_ratio = min_mask_ratio
-        self.max_mask_ratio = max_mask_ratio
-        self.noise_schedule = noise_schedule
-
-
-class MistralForDiffusionConfig(MistralConfig):
-    """Configuration class for Mistral models with diffusion training."""
-    
-    model_type = "mistral_diffusion"
-    
-    def __init__(
-        self,
-        mask_token_id: int = 32000,
-        eps: float = 1e-3,
-        importance_weighting: bool = False,
-        sample_packing: bool = False,
-        min_mask_ratio: float = 0.0,
-        max_mask_ratio: float = 1.0,
-        noise_schedule: str = "linear",
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-        # Diffusion-specific parameters
-        self.mask_token_id = mask_token_id
-        self.eps = eps
-        self.importance_weighting = importance_weighting
-        self.sample_packing = sample_packing
-        self.min_mask_ratio = min_mask_ratio
-        self.max_mask_ratio = max_mask_ratio
-        self.noise_schedule = noise_schedule
-
-
-# Keep the base class for backward compatibility but mark as deprecated
-class DiffusionConfig(LlamaForDiffusionConfig):
-    """
-    Deprecated: Use LlamaForDiffusionConfig or MistralForDiffusionConfig instead.
-    """
-    
-    model_type = "diffusion"
-    
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)

src/axolotl/integrations/diffusion/models.py

@@ -1,426 +0,0 @@
-"""Custom model classes for diffusion language models."""
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from transformers import LlamaForCausalLM, MistralForCausalLM
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from .configuration import LlamaForDiffusionConfig, MistralForDiffusionConfig
-
-
-class DiffusionModelMixin:
-    """Mixin class providing diffusion functionality to language models."""
-    
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._special_token_ids = None
-        
-    def _cache_special_token_ids(self, tokenizer=None):
-        """Cache special token IDs to avoid repeated tokenizer access."""
-        if tokenizer is None:
-            self._special_token_ids = set()
-            return
-            
-        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
-    
-    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
-
-    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,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        logits: torch.Tensor | None = None,
-        masked_indices: torch.Tensor | None = None,
-        p_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """
-        Compute diffusion loss given logits and masking information.
-
-        Args:
-            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].
-            logits: Model logits [batch_size, seq_len, vocab_size].
-            masked_indices: Boolean mask indicating which tokens were masked.
-            p_mask: Masking probabilities for each token [batch_size, seq_len].
-
-        Returns:
-            loss: Cross-entropy loss.
-        """
-        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])
-        else:
-            loss = torch.tensor(0.0, device=input_ids.device, requires_grad=True)
-
-        return loss
-
-
-class LlamaForDiffusionLM(DiffusionModelMixin, LlamaForCausalLM):
-    """
-    Llama model for diffusion language modeling.
-    
-    This model extends LlamaForCausalLM with diffusion training capabilities,
-    including bidirectional attention and forward diffusion process.
-    """
-    
-    config_class = LlamaForDiffusionConfig
-    
-    def __init__(self, config):
-        super().__init__(config)
-        
-        # Initialize diffusion-specific attributes
-        self._special_token_ids = None
-        
-        # Initialize weights and apply final processing
-        self.post_init()
-        
-    def set_tokenizer(self, tokenizer):
-        """Set tokenizer for special token handling."""
-        self._cache_special_token_ids(tokenizer)
-    
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[list[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        """
-        Forward pass with diffusion training logic.
-        
-        During training, applies forward diffusion process and bidirectional attention.
-        During inference, behaves like standard causal language model.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
-        if self.training and input_ids is not None:
-            # Apply diffusion process during training
-            original_input_ids = input_ids.clone()
-            
-            # Apply forward process to get noisy input
-            noisy_input_ids, masked_indices, p_mask = self._forward_process(
-                input_ids, attention_mask, labels, self.config.eps
-            )
-            
-            # Create bidirectional attention mask
-            bidirectional_attention_mask = self._create_bidirectional_attention_mask(
-                input_ids, attention_mask
-            )
-            
-            # Forward pass with noisy input and bidirectional attention
-            outputs = super().forward(
-                input_ids=noisy_input_ids,
-                attention_mask=bidirectional_attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=None,  # Don't use standard loss computation
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-            
-            # Compute diffusion loss
-            loss = self._compute_diffusion_loss(
-                original_input_ids,
-                attention_mask,
-                labels,
-                outputs.logits,
-                masked_indices,
-                p_mask,
-            )
-            
-            if return_dict:
-                outputs.loss = loss
-                return outputs
-            else:
-                return (loss,) + outputs[1:]
-        else:
-            # Standard forward pass for inference
-            return super().forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=labels,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-
-
-class MistralForDiffusionLM(DiffusionModelMixin, MistralForCausalLM):
-    """
-    Mistral model for diffusion language modeling.
-    
-    This model extends MistralForCausalLM with diffusion training capabilities,
-    including bidirectional attention and forward diffusion process.
-    """
-    
-    config_class = MistralForDiffusionConfig
-    
-    def __init__(self, config):
-        super().__init__(config)
-        
-        # Initialize diffusion-specific attributes
-        self._special_token_ids = None
-        
-        # Initialize weights and apply final processing
-        self.post_init()
-        
-    def set_tokenizer(self, tokenizer):
-        """Set tokenizer for special token handling."""
-        self._cache_special_token_ids(tokenizer)
-    
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[list[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        """
-        Forward pass with diffusion training logic.
-        
-        During training, applies forward diffusion process and bidirectional attention.
-        During inference, behaves like standard causal language model.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
-        if self.training and input_ids is not None:
-            # Apply diffusion process during training
-            original_input_ids = input_ids.clone()
-            
-            # Apply forward process to get noisy input
-            noisy_input_ids, masked_indices, p_mask = self._forward_process(
-                input_ids, attention_mask, labels, self.config.eps
-            )
-            
-            # Create bidirectional attention mask
-            bidirectional_attention_mask = self._create_bidirectional_attention_mask(
-                input_ids, attention_mask
-            )
-            
-            # Forward pass with noisy input and bidirectional attention
-            outputs = super().forward(
-                input_ids=noisy_input_ids,
-                attention_mask=bidirectional_attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=None,  # Don't use standard loss computation
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-            
-            # Compute diffusion loss
-            loss = self._compute_diffusion_loss(
-                original_input_ids,
-                attention_mask,
-                labels,
-                outputs.logits,
-                masked_indices,
-                p_mask,
-            )
-            
-            if return_dict:
-                outputs.loss = loss
-                return outputs
-            else:
-                return (loss,) + outputs[1:]
-        else:
-            # Standard forward pass for inference
-            return super().forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=labels,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )

src/axolotl/integrations/diffusion/plugin.py

@@ -1,20 +1,13 @@
 """Diffusion LM training plugin for Axolotl."""
 
-from typing import TYPE_CHECKING
-
 from peft import PeftModel
-from transformers import AutoConfig, AutoModel, PreTrainedModel
+from transformers import PreTrainedModel
 
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
-from .callbacks import DiffusionGenerationCallback
+from .trainer import DiffusionTrainer
-from .configuration import LlamaForDiffusionConfig, MistralForDiffusionConfig
-from .models import LlamaForDiffusionLM, MistralForDiffusionLM
-
-if TYPE_CHECKING:
-    from transformers import Trainer
 
 LOG = get_logger(__name__)
 
@@ -35,64 +28,14 @@ class DiffusionPlugin(BasePlugin):
         """Returns the pydantic model for LLaDA plugin arguments."""
         return "axolotl.integrations.diffusion.DiffusionArgs"
 
-    def pre_model_load(self, cfg: DictDefault):
-        """Configure model loading to use diffusion model classes."""
-        # Map base model types to diffusion equivalents
-        base_model_type = cfg.get("model_type")
-        
-        if base_model_type == "llama":
-            # Create diffusion config from base config
-            diffusion_config = LlamaForDiffusionConfig(
-                mask_token_id=getattr(cfg, "mask_token_id", 32000),
-                eps=getattr(cfg, "eps", 1e-3),
-                importance_weighting=getattr(cfg, "importance_weighting", False),
-                sample_packing=getattr(cfg, "sample_packing", False),
-                min_mask_ratio=getattr(cfg, "min_mask_ratio", 0.0),
-                max_mask_ratio=getattr(cfg, "max_mask_ratio", 1.0),
-                noise_schedule=getattr(cfg, "noise_schedule", "linear"),
-            )
-            
-            # Override model type for loading
-            cfg.model_type = "llama_diffusion"
-            
-        elif base_model_type == "mistral":
-            # Create diffusion config from base config
-            diffusion_config = MistralForDiffusionConfig(
-                mask_token_id=getattr(cfg, "mask_token_id", 32000),
-                eps=getattr(cfg, "eps", 1e-3),
-                importance_weighting=getattr(cfg, "importance_weighting", False),
-                sample_packing=getattr(cfg, "sample_packing", False),
-                min_mask_ratio=getattr(cfg, "min_mask_ratio", 0.0),
-                max_mask_ratio=getattr(cfg, "max_mask_ratio", 1.0),
-                noise_schedule=getattr(cfg, "noise_schedule", "linear"),
-            )
-            
-            # Override model type for loading
-            cfg.model_type = "mistral_diffusion"
-        else:
-            LOG.warning(f"Diffusion plugin not implemented for model type: {base_model_type}")
-
     def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
-        """Configure model after loading."""
+        """Perform actions after model is loaded."""
         self.cfg = cfg
-        
-        # Set tokenizer on diffusion models for special token handling
-        if hasattr(model, "set_tokenizer"):
-            # Get tokenizer from cfg if available
-            tokenizer = getattr(cfg, "tokenizer", None)
-            if tokenizer is not None:
-                model.set_tokenizer(tokenizer)
 
-    def add_callbacks_post_trainer(self, cfg: DictDefault, trainer: "Trainer"):
+    def get_trainer_cls(self, cfg: DictDefault) -> type[DiffusionTrainer] | None:
-        """Add diffusion-specific callbacks after trainer creation."""
+        """Return custom trainer class for diffusion training."""
-        callbacks = []
+        return DiffusionTrainer
-        
+
-        # Store diffusion config on trainer for callbacks
+    def post_trainer_create(self, cfg: DictDefault, trainer: DiffusionTrainer):
-        trainer.diffusion_config = cfg
+        """Configure trainer after creation."""
-        
+        trainer.set_config(cfg)
-        # Add generation callback if enabled
-        if cfg.get("generate_samples", False):
-            generation_callback = DiffusionGenerationCallback(trainer)
-            callbacks.append(generation_callback)
-            
-        return callbacks

src/axolotl/integrations/diffusion/trainer.py

@@ -0,0 +1,336 @@
+"""Custom trainer for diffusion LM training."""
+
+from typing import Any, Literal
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers.masking_utils import find_packed_sequence_indices
+
+from axolotl.core.trainers.base import AxolotlTrainer
+from axolotl.integrations.diffusion.utils import create_bidirectional_block_mask
+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")
+        position_ids = inputs.get("position_ids")
+
+        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, position_ids
+        )
+
+        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,
+        min_p: float = 0.0,
+        max_p: float = 1.0,
+    ) -> 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 = min_p + (max_p - min_p) * (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, position_ids: 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]
+            position_ids: Position ids [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
+            )
+
+        if position_ids is None:
+            # 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
+
+        if self._config.flex_attention:
+            block_mask = create_bidirectional_block_mask(
+                input_ids, attention_mask, position_ids
+            )
+        else:
+            packed_seq_mask = find_packed_sequence_indices(position_ids)
+            block_mask = packed_seq_mask.unsqueeze(2) == packed_seq_mask.unsqueeze(1)
+
+        return block_mask
+
+    def _compute_diffusion_loss(
+        self,
+        model: nn.Module,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        position_ids: 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].
+            position_ids: Position ids [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, self._config.min_mask_ratio, self._config.max_mask_ratio
+        )
+
+        # Create bidirectional attention mask (optional: use causal if you want strict AR behavior)
+        bidirectional_mask = self._create_bidirectional_attention_mask(
+            input_ids, attention_mask, position_ids
+        )
+
+        # Forward pass
+        outputs = model(
+            input_ids=noisy_batch,
+            attention_mask=bidirectional_mask,
+        )
+        logits = outputs.logits  # [B, L, V]
+
+        # ----- AR label shift toggle -----
+        use_ar_shift = False
+        if use_ar_shift:
+            # Predict token at t from logits at t-1: drop last logit step, drop first target step
+            logits_eff = logits[:, :-1, :]
+            input_ids_eff = input_ids[:, 1:]
+            masked_indices_eff = masked_indices[:, 1:]
+            p_mask_eff = p_mask[:, 1:]
+            labels_eff = labels[:, 1:] if labels is not None else None
+        else:
+            logits_eff = logits
+            input_ids_eff = input_ids
+            masked_indices_eff = masked_indices
+            p_mask_eff = p_mask
+            labels_eff = labels
+
+        if masked_indices_eff.sum() > 0:
+            valid_indices = torch.where(masked_indices_eff)
+            batch_indices, seq_indices = valid_indices
+
+            masked_logits = logits_eff[batch_indices, seq_indices]
+            masked_targets = input_ids_eff[batch_indices, seq_indices]
+            masked_p_mask = p_mask_eff[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().clamp_min(1e-6)
+                weighted_loss = token_loss / masked_p_mask
+            else:
+                weighted_loss = token_loss
+
+            # Final loss: sum weighted losses, normalize
+            if labels_eff is not None:
+                # For SFT data: normalize by answer length per sample
+                answer_mask = labels_eff != -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.any():
+                        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)
+
+            # Keep eff tensors around for metrics
+            masked_indices_eff = masked_indices
+            p_mask_eff = p_mask
+            labels_eff = labels
+
+        # Metrics (aligned to the effective tensors)
+        if masked_indices_eff.any():
+            avg_p = p_mask_eff[masked_indices_eff].float().mean().item()
+            num_masked = int(masked_indices_eff.sum().item())
+            mask_ratio = masked_indices_eff.float().mean().item()
+        else:
+            avg_p = 0.0
+            num_masked = 0
+            mask_ratio = 0.0
+
+        metrics = {
+            "loss": float(loss.detach()),
+            "accuracy": float(accuracy.detach()),
+            "mask_ratio": mask_ratio,
+            "num_masked_tokens": (num_masked, "sum"),
+            "avg_p_mask": avg_p,
+            "ce_loss": float(ce_loss.detach()),
+        }
+
+        # SFT-specific metrics (aligned)
+        if labels_eff is not None:
+            answer_mask = labels_eff != -100
+            metrics["answer_ratio"] = answer_mask.float().mean().item()
+            metrics["avg_answer_length"] = answer_mask.sum(dim=1).float().mean().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

src/axolotl/integrations/diffusion/utils.py

@@ -0,0 +1,50 @@
+import torch
+from torch.nn.attention.flex_attention import BlockMask, create_block_mask
+from transformers.masking_utils import find_packed_sequence_indices, packed_sequence_mask_function
+
+
+def create_bidirectional_block_mask(
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None = None,
+    position_ids: torch.Tensor | None = None,
+) -> "BlockMask":
+    """
+    Creates a bidirectional block mask for FlexAttention.
+
+    Args:
+        input_ids: Input token ids [batch_size, seq_len]
+        attention_mask: Padding mask [batch_size, seq_len]
+
+    Returns:
+        BlockMask for bidirectional attention with padding
+    """
+    batch_size, seq_len = input_ids.shape
+
+    if position_ids is not None:
+        packed_seq_mask = find_packed_sequence_indices(position_ids)
+        mask_fn =packed_sequence_mask_function(packed_seq_mask, batch_size, seq_len)
+    elif attention_mask is None:
+        # If no padding mask, all positions can attend to all positions
+        def mask_fn(b, h, q_idx, kv_idx):
+            # Always return True for bidirectional attention
+            return True
+    else:
+        # Convert attention_mask to boolean if needed
+        attention_mask = attention_mask.bool()
+
+        def mask_fn(b, h, q_idx, kv_idx):
+            # Both query and key positions must be valid (not padding)
+            return attention_mask[b, q_idx] & attention_mask[b, kv_idx]
+
+    # Create the block mask
+    block_mask = create_block_mask(
+        mask_fn,
+        B=batch_size,
+        H=None,  # Will be set by the attention layer
+        Q_LEN=seq_len,
+        KV_LEN=seq_len,
+        device=input_ids.device,
+        _compile=True,
+    )
+
+    return block_mask

src/axolotl/integrations/spectrum/__init__.py

@@ -57,7 +57,7 @@ class SpectrumPlugin(BasePlugin):
     Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
     """
 
-    base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
+    base_url = "https://raw.githubusercontent.com/QuixiAI/spectrum/main/model_snr_results/"
     base_path = "./model_snr_results/"
     snr_file_template = "snr_results_{model_name_slug}.json"
 

src/axolotl/utils/environment.py

@@ -16,7 +16,7 @@ from packaging.version import Version, parse
 def check_cuda_p2p_ib_support():
     if not accelerate_check_cuda_p2p_ib_support():
         return False
-    unsupported_devices = {"RTX 6000 Ada", "L40S"}
+    unsupported_devices = {"RTX 6000 Ada", "L40S", "A40"}
     try:
         device_names, device_count = get_gpu_info()
         if 1 < device_count < 8:

tests/integrations/test_diffusion.py

@@ -1,14 +1,13 @@
-"""Tests for diffusion model integration."""
+"""Tests for diffusion trainer integration."""
 
 # pylint: disable=redefined-outer-name,protected-access
 
-from unittest.mock import Mock, patch
+from unittest.mock import Mock
 
 import pytest
 import torch
 
-from axolotl.integrations.diffusion.configuration import LlamaForDiffusionConfig
+from axolotl.integrations.diffusion.trainer import DiffusionTrainer
-from axolotl.integrations.diffusion.models import LlamaForDiffusionLM
 from axolotl.utils.dict import DictDefault
 
 
@@ -25,44 +24,37 @@ def mock_tokenizer():
 @pytest.fixture
 def diffusion_config():
     """Create a diffusion config."""
-    return LlamaForDiffusionConfig(
+    return DictDefault(
-        mask_token_id=32000,
+        {
-        eps=1e-3,
+            "mask_token_id": 32000,
-        importance_weighting=False,
+            "eps": 1e-3,
-        sample_packing=False,
+            "importance_weighting": False,
-        # Basic llama config fields - smaller for testing
+            "sample_packing": False,
-        vocab_size=1000,
+        }
-        hidden_size=256,
-        intermediate_size=512,
-        num_hidden_layers=2,
-        num_attention_heads=4,
     )
 
 
 @pytest.fixture
-def diffusion_model_instance(mock_tokenizer, diffusion_config):
+def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
-    """Create a diffusion model instance for testing methods directly."""
+    """Create a diffusion trainer instance for testing methods directly."""
-    # Create a minimal model instance for testing
+    # Create a minimal trainer instance just for testing methods
-    model = object.__new__(LlamaForDiffusionLM)
+    trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
-    model.config = diffusion_config
+    trainer.config = diffusion_config
-    model._special_token_ids = {0, 1, 2}  # pad, bos, eos
+    trainer._special_token_ids = {0, 1, 2}  # pad, bos, eos
-    model.training = True
+    trainer.processing_class = mock_tokenizer
-    
+    trainer.store_metrics = Mock()  # Mock metrics storage
-    # Set tokenizer
+    return trainer
-    model.set_tokenizer(mock_tokenizer)
-    
-    return model
 
 
-class TestDiffusionModel:
+class TestDiffusionTrainer:
-    """Test the DiffusionModel class."""
+    """Test the DiffusionTrainer class."""
 
-    def test_forward_process_basic(self, diffusion_model_instance):
+    def test_forward_process_basic(self, diffusion_trainer_instance):
         """Test basic forward process without labels."""
         input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
 
         noisy_batch, masked_indices, p_mask = (
-            diffusion_model_instance._forward_process(input_ids, eps=0.1)
+            diffusion_trainer_instance._forward_process(input_ids, eps=0.1)
         )
 
         # Check shapes
@@ -75,18 +67,18 @@ class TestDiffusionModel:
         assert not masked_indices[special_token_positions].any()
 
         # Check that mask token is applied
-        mask_token_id = diffusion_model_instance.config.mask_token_id
+        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_model_instance):
+    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)
 
         noisy_batch, masked_indices, p_mask = (
-            diffusion_model_instance._forward_process(
+            diffusion_trainer_instance._forward_process(
                 input_ids, labels=labels, eps=0.1
             )
         )
@@ -108,12 +100,12 @@ class TestDiffusionModel:
         # 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_model_instance):
+    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)
 
-        _, masked_indices, p_mask = diffusion_model_instance._forward_process(
+        _, masked_indices, p_mask = diffusion_trainer_instance._forward_process(
             input_ids, attention_mask=attention_mask, eps=0.1
         )
 
@@ -122,11 +114,11 @@ class TestDiffusionModel:
         assert not masked_indices[padding_positions].any()
         assert (p_mask[padding_positions] == 0).all()
 
-    def test_bidirectional_attention_mask_no_packing(self, diffusion_model_instance):
+    def test_bidirectional_attention_mask_no_packing(self, diffusion_trainer_instance):
         """Test bidirectional attention mask without sample packing."""
         input_ids = torch.tensor([[1, 10, 20, 2]], dtype=torch.long)
 
-        mask = diffusion_model_instance._create_bidirectional_attention_mask(
+        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
             input_ids
         )
 
@@ -136,15 +128,15 @@ class TestDiffusionModel:
         assert mask.all()
 
     def test_bidirectional_attention_mask_with_packing(
-        self, diffusion_model_instance
+        self, diffusion_trainer_instance
     ):
         """Test bidirectional attention mask with sample packing."""
-        diffusion_model_instance.config.sample_packing = True
+        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_model_instance._create_bidirectional_attention_mask(
+        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
             input_ids, attention_mask
         )
 
@@ -156,135 +148,124 @@ class TestDiffusionModel:
         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_model_instance):
+    def test_compute_loss_basic(self, diffusion_trainer_instance):
         """Test basic loss computation."""
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        # Mock model that returns logits
-        
+        mock_model = Mock()
-        # Create mock data for loss computation
+        mock_outputs = Mock()
         vocab_size = 1000
         seq_len = 5
-        logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
+        mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
-        
+        mock_model.return_value = mock_outputs
-        # Create a simple masked indices tensor (mask middle tokens)
+        mock_model.training = True
-        masked_indices = torch.tensor([[False, True, True, False, False]], dtype=torch.bool)
-        p_mask = torch.tensor([[0.1, 0.5, 0.5, 0.1, 0.1]], dtype=torch.float)
 
-        loss = diffusion_model_instance._compute_diffusion_loss(
+        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-            input_ids=input_ids,
+
-            logits=logits,
+        loss, outputs = diffusion_trainer_instance._compute_diffusion_loss(
-            masked_indices=masked_indices,
+            mock_model, input_ids
-            p_mask=p_mask,
         )
 
         # Check that loss is computed
         assert isinstance(loss, torch.Tensor)
         assert loss.requires_grad
+        assert outputs == mock_outputs
 
-    def test_compute_loss_with_labels(self, diffusion_model_instance):
+        # Check that metrics were stored
+        diffusion_trainer_instance.store_metrics.assert_called_once()
+
+    def test_compute_loss_with_labels(self, diffusion_trainer_instance):
         """Test loss computation with SFT labels."""
+        # Mock model
+        mock_model = Mock()
+        mock_outputs = Mock()
+        vocab_size = 1000
+        seq_len = 5
+        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)
         labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
-        
-        # Create mock data for loss computation
-        vocab_size = 1000
-        seq_len = 5
-        logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
-        
-        # Create masked indices that only covers answer tokens
-        masked_indices = torch.tensor([[False, False, True, True, False]], dtype=torch.bool)
-        p_mask = torch.tensor([[0.1, 0.1, 0.5, 0.5, 0.1]], dtype=torch.float)
 
-        loss = diffusion_model_instance._compute_diffusion_loss(
+        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
-            input_ids=input_ids,
+            mock_model, input_ids, labels=labels
-            labels=labels,
-            logits=logits,
-            masked_indices=masked_indices,
-            p_mask=p_mask,
         )
 
         # Check that loss is computed
         assert isinstance(loss, torch.Tensor)
         assert loss.requires_grad
 
-    def test_compute_loss_no_masked_tokens(self, diffusion_model_instance):
+        # Check that SFT metrics were added
+        call_args = diffusion_trainer_instance.store_metrics.call_args[0][0]
+        assert "answer_ratio" in call_args
+        assert "avg_answer_length" in call_args
+
+    def test_compute_loss_no_masked_tokens(self, diffusion_trainer_instance):
         """Test loss computation when no tokens are masked."""
-        input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
+        # Mock model
-        
+        mock_model = Mock()
-        # Create mock data for loss computation
+        mock_outputs = Mock()
         vocab_size = 1000
         seq_len = 3
-        logits = torch.randn(1, seq_len, vocab_size)
+        mock_outputs.logits = torch.randn(1, seq_len, vocab_size)
-        
+        mock_model.return_value = mock_outputs
-        # No tokens masked
+        mock_model.training = True
-        masked_indices = torch.tensor([[False, False, False]], dtype=torch.bool)
-        p_mask = torch.tensor([[0.1, 0.1, 0.1]], dtype=torch.float)
 
-        loss = diffusion_model_instance._compute_diffusion_loss(
+        # Only special tokens (which won't be masked)
-            input_ids=input_ids,
+        input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
-            logits=logits,
+
-            masked_indices=masked_indices,
+        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
-            p_mask=p_mask,
+            mock_model, input_ids
         )
 
         # Loss should be zero when no tokens are masked
         assert loss.item() == 0.0
         assert loss.requires_grad
 
-    def test_cache_special_token_ids(self, diffusion_model_instance):
+    def test_cache_special_token_ids(self, diffusion_trainer_instance):
         """Test caching of special token IDs."""
         # Should cache BOS, EOS, PAD tokens
         expected_tokens = {0, 1, 2}  # pad, bos, eos
-        assert diffusion_model_instance._special_token_ids == expected_tokens
+        assert diffusion_trainer_instance._special_token_ids == expected_tokens
 
     def test_cache_special_token_ids_no_tokenizer(self):
         """Test caching when no tokenizer is available."""
-        # Mock the parent model initialization to avoid loading pretrained weights
+        trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
-        with patch('transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__'):
+        trainer.processing_class = None
-            model = LlamaForDiffusionLM.__new__(LlamaForDiffusionLM)
+        trainer._cache_special_token_ids()
-            model._cache_special_token_ids(None)
-            assert model._special_token_ids == set()
 
-    def test_forward_training_mode(self, diffusion_model_instance):
+        assert trainer._special_token_ids == set()
-        """Test forward pass in training mode."""
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-        attention_mask = torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.bool)
-        
-        # Mock the parent forward method
-        with patch.object(diffusion_model_instance.__class__.__bases__[1], 'forward') as mock_forward:
-            mock_output = Mock()
-            mock_output.logits = torch.randn(1, 5, 32000)
-            mock_forward.return_value = mock_output
-            
-            # Set training mode
-            diffusion_model_instance.training = True
-            
-            result = diffusion_model_instance.forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                return_dict=True
-            )
-            
-            # Should call parent forward and compute loss
-            assert mock_forward.called
-            assert hasattr(result, 'loss')
 
-    def test_forward_inference_mode(self, diffusion_model_instance):
+    def test_main_compute_loss_interface(self, diffusion_trainer_instance):
-        """Test forward pass in inference mode."""
+        """Test the main compute_loss interface."""
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        # Mock model
-        
+        mock_model = Mock()
-        # Mock the parent forward method
+        mock_outputs = Mock()
-        with patch.object(diffusion_model_instance.__class__.__bases__[1], 'forward') as mock_forward:
+        mock_outputs.logits = torch.randn(1, 5, 1000)
-            mock_output = Mock()
+        mock_model.return_value = mock_outputs
-            mock_forward.return_value = mock_output
+        mock_model.training = True
-            
+
-            # Set inference mode
+        inputs = {
-            diffusion_model_instance.training = False
+            "input_ids": torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long),
-            
+            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.long),
-            result = diffusion_model_instance.forward(
+            "labels": torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long),
-                input_ids=input_ids,
+        }
-                return_dict=True
+
-            )
+        # Test without return_outputs
-            
+        loss = diffusion_trainer_instance.compute_loss(mock_model, inputs)
-            # Should just call parent forward without diffusion processing
+        assert isinstance(loss, torch.Tensor)
-            assert mock_forward.called
+
-            assert result == mock_output
+        # Test with return_outputs
+        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):
+        """Test that missing input_ids raises ValueError."""
+        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)