fixes

examples/llama-3/diffusion-3.2-1b-pretrain.yaml

@@ -2,29 +2,27 @@ base_model: meta-llama/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
-# Dataset configuration for pretraining
+pretraining_dataset:
-datasets:
   - path: wikitext
     name: wikitext-103-raw-v1
     type: completion
     field: text
-val_set_size: 0.001
 
 plugins:
   - diffusion.DiffusionPlugin
-noise_schedule: "cosine"
+noise_schedule: cosine
 min_mask_ratio: 0.15
 max_mask_ratio: 0.85
-num_diffusion_steps: 128
 eps: 5e-4
 importance_weighting: true
 mask_token_id: 128002
+generate_samples: true
+generation_interval: 10
 
 output_dir: ./outputs/model-out
 
 sequence_len: 512
-sample_packing: false
+sample_packing: true
-eval_sample_packing: false
 
 gradient_accumulation_steps: 8
 micro_batch_size: 4
@@ -42,12 +40,10 @@ resume_from_checkpoint:
 logging_steps: 1
 sdp_attention: true
 
-warmup_steps: 500
+warmup_steps: 1000
 
 save_strategy: steps
-eval_strategy: steps
 save_steps: 1000
-eval_steps: 1000
 
 special_tokens:
   pad_token: "<|end_of_text|>"

examples/llama-3/diffusion-3.2-1b-sft.yaml

@@ -9,7 +9,7 @@ val_set_size: 0.05
 
 plugins:
   - diffusion.DiffusionPlugin
-noise_schedule: "linear"
+noise_schedule: cosine
 min_mask_ratio: 0.1
 max_mask_ratio: 0.9
 num_diffusion_steps: 128
@@ -39,6 +39,8 @@ resume_from_checkpoint:
 logging_steps: 1
 sdp_attention: true
 
+warmup_steps: 1000
+
 save_strategy: steps
 eval_strategy: steps
 save_steps: 500

src/axolotl/integrations/diffusion/args.py

@@ -27,8 +27,6 @@ class DiffusionArgs(BaseModel):
     num_diffusion_steps: int = Field(
         default=128, ge=1, description="Number of diffusion timesteps"
     )
-
-    # Forward process config
     eps: float = Field(
         default=1e-3,
         ge=0.0,

src/axolotl/integrations/diffusion/callbacks.py

@@ -26,26 +26,24 @@ class DiffusionGenerationCallback(TrainerCallback):
         **kwargs,
     ):
         """Generate samples at specified intervals."""
-        # Only generate samples at the specified interval and after step 0
         if (
             state.global_step > 0
             and state.global_step % self.trainer.config.generation_interval == 0
-            and hasattr(self.trainer, "eval_dataset")
-            and self.trainer.eval_dataset is not None
         ):
-
+            # Use eval dataloader if available, otherwise use train dataloader
-            LOG.info(
+            if (
-                f"Generating {self.trainer.config.num_generation_samples} samples at step {state.global_step}..."
+                hasattr(self.trainer, "eval_dataset")
-            )
+                and self.trainer.eval_dataset is not None
-
+            ):
-            # Create a simple dataloader from eval dataset for sampling
+                dataloader = self.trainer.callback_handler.eval_dataloader
-            eval_dataloader = self.trainer.get_eval_dataloader()
+            else:
+                dataloader = self.trainer.callback_handler.train_dataloader
 
             # Generate samples
             samples = generate_samples(
                 model=self.trainer.model,
                 tokenizer=self.trainer.tokenizer,
-                val_dataloader=eval_dataloader,
+                dataloader=dataloader,
                 num_generation_samples=self.trainer.config.num_generation_samples,
                 max_length=self.trainer.config.generation_max_length,
                 num_diffusion_steps=self.trainer.config.generation_steps,

src/axolotl/integrations/diffusion/generation.py

@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 def generate_samples(
     model: torch.nn.Module,
     tokenizer: Any,
-    val_dataloader: Optional[Any] = None,
+    dataloader: Optional[Any] = None,
     num_generation_samples: int = 3,
     max_length: int = 100,
     num_diffusion_steps: int = 128,
@@ -19,13 +19,13 @@ def generate_samples(
     mask_token_id: int = 32000,
 ) -> List[dict]:
     """
-    Generate text samples using the diffusion model by randomly masking sequences
+    Generate text samples using the diffusion model by randomly masking sequences from
-    from the validation dataset and running the reverse diffusion process.
+    the given dataset and running the reverse diffusion process.
 
     Args:
         model: The wrapped or unwrapped model
         tokenizer: Tokenizer for encoding/decoding
-        val_dataloader: Validation dataloader (for sampling sequences)
+        dataloader: Validation dataloader (for sampling sequences)
         num_generation_samples: Number of samples to generate
         max_length: Maximum length of sequences to use
         num_diffusion_steps: Number of diffusion steps for generation
@@ -35,7 +35,7 @@ def generate_samples(
     Returns:
         List of dictionaries with original text, masked text, and generated text
     """
-    if val_dataloader is None:
+    if dataloader is None:
         logger.warning("No validation dataloader provided, cannot generate samples")
         return []
 
@@ -46,7 +46,7 @@ def generate_samples(
 
     # Sample sequences from validation dataset
     sampled_sequences = _sample_sequences_from_dataloader(
-        val_dataloader, num_generation_samples, max_length, unwrapped_model.device
+        dataloader, num_generation_samples, max_length, unwrapped_model.device
     )
     logger.info(f"Sampled {len(sampled_sequences)} sequences from validation dataset")
 
@@ -68,7 +68,7 @@ def generate_samples(
 
 
 def _sample_sequences_from_dataloader(
-    val_dataloader: Any, num_samples: int, max_length: int, device: torch.device
+    dataloader: Any, num_samples: int, max_length: int, device: torch.device
 ) -> List[torch.Tensor]:
     """Sample sequences from validation dataloader."""
     sampled_sequences = []
@@ -78,7 +78,7 @@ def _sample_sequences_from_dataloader(
     skip_batches = torch.randint(0, 6, (1,)).item()
     batch_count = 0
 
-    for batch in val_dataloader:
+    for batch in dataloader:
         # Skip some batches for variety
         if batch_count < skip_batches:
             batch_count += 1
@@ -183,13 +183,15 @@ def _generate(
 
 def _clean_masked_text(masked_text: str, tokenizer: Any, mask_token_id: int) -> str:
     """Clean up masked text for display."""
-    # Get the mask token representation from the tokenizer
     mask_token_repr = tokenizer.decode([mask_token_id], skip_special_tokens=False)
     cleaned = masked_text.replace(mask_token_repr, "[MASK]")
 
-    # Clean up special tokens and whitespace
+    if hasattr(tokenizer, "special_tokens_map"):
-    cleaned = cleaned.replace("<s>", "").replace("</s>", "").strip()
+        for token_value in tokenizer.special_tokens_map.values():
-    cleaned = " ".join(cleaned.split())
+            if token_value and isinstance(token_value, str):
+                cleaned = cleaned.replace(token_value, "")
+
+    cleaned = " ".join(cleaned.split()).strip()
 
     return cleaned
 

src/axolotl/integrations/diffusion/trainer.py

@@ -270,13 +270,6 @@ class DiffusionTrainer(AxolotlTrainer):  # pylint: disable=too-many-ancestors
             "avg_p_mask": p_mask[masked_indices].mean().item(),
             "ce_loss": ce_loss.item(),
         }
-
-        # Add SFT-specific metrics
-        if labels is not None:
-            answer_mask = labels != -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()
 

src/axolotl/prompt_tokenizers.py

@@ -75,7 +75,7 @@ class PromptTokenizingStrategy(abc.ABC):
     ) -> BatchEncoding:
         empty = BatchEncoding(data={"input_ids": [], "attention_mask": []})
         if not prompt:
-            LOG.warning("Empty text requested for tokenization.")
+            LOG.warning_once("Empty text requested for tokenization.")
             return empty
 
         result = self.tokenizer(