text diffusion training plugin (#3067)

* diffusion training plugin

* cleanup

* nits

* fixes + improvements

* add back in reinit_weights (clobbered?); masking / pretrain fixes

* nits

* cleanup; tests draft

* sample generation, tests fixes

* fixes

* nits

* add inference support; add auto-mask token support

* nits

* nits

* progress

* simplify logging

* lint

* prefix args with diffusion_

* coderabbito

* tests fix

* nit

* nits

* cleanup + nits

* nits

* fix SFT sample gen

* fixes

* fix

* comments

* comments

* lint

* reward model lora fix

* cleanup; fix pretraining_dataset case

* gradio inference

* update cfgs

* update cfgs

* train, generation parity, cleanup

* fix

* simplify

* test

* test fix

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
     rev: v0.12.12
     hooks:
     -   id: ruff
-        args: [--fix]
+        args: [--fix, --select, I]
     -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.17.1

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -176,8 +176,8 @@
     }
    ],
    "source": [
-    "from axolotl.utils.dict import DictDefault\n",
     "from axolotl.cli.config import load_cfg\n",
+    "from axolotl.utils.dict import DictDefault\n",
     "\n",
     "# Axolotl provides full control and transparency over model and training configuration\n",
     "config = DictDefault(\n",

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

@@ -0,0 +1,56 @@
+base_model: meta-llama/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+pretraining_dataset:
+  - path: wikitext
+    name: wikitext-103-raw-v1
+    type: completion
+    field: text
+
+plugins:
+  - axolotl.integrations.diffusion.DiffusionPlugin
+
+diffusion:
+  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: 250
+
+output_dir: ./outputs/model-out
+
+sequence_len: 512
+sample_packing: true
+
+gradient_accumulation_steps: 8
+micro_batch_size: 4
+max_steps: 10000
+warmup_ratio: 0.1
+
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 3e-4
+sdp_attention: true
+
+bf16: auto
+tf32: true
+
+logging_steps: 1
+save_strategy: steps
+save_steps: 1000
+
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

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

@@ -0,0 +1,59 @@
+base_model: meta-llama/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+val_set_size: 0.05
+
+plugins:
+  - axolotl.integrations.diffusion.DiffusionPlugin
+
+diffusion:
+  noise_schedule: cosine
+  min_mask_ratio: 0.1
+  max_mask_ratio: 0.9
+  num_diffusion_steps: 128
+  eps: 1e-3
+  importance_weighting: true
+  mask_token_id: 128002
+  generate_samples: true
+  generation_interval: 250
+
+output_dir: ./outputs/model-out
+
+sequence_len: 512
+sample_packing: true
+eval_sample_packing: true
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 1
+warmup_steps: 0.1
+
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+sdp_attention: true
+
+logging_steps: 1
+save_strategy: best
+eval_strategy: epoch
+
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

src/axolotl/cli/inference.py

@@ -14,6 +14,13 @@ from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 from axolotl.cli.args import InferenceCliArgs
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.cli.utils.diffusion import (
+    diffusion_inference,
+    launch_diffusion_gradio_ui,
+    render_html,
+    run_diffusion,
+)
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
@@ -29,6 +36,7 @@ def get_multi_line_input() -> str:
         Possibly multi-line, possibly empty stdin input as a string.
     """
     print("Give me an instruction (Ctrl + D to submit): ")
+    print("=" * 80)
 
     instruction = ""
     for line in sys.stdin:
@@ -43,9 +51,9 @@ def do_inference(
     cli_args: InferenceCliArgs,
 ):
     """
-    Runs inference on the command line in a loop. User input is accepted, a chat template
-    is (optionally) applied, and the model specified in the `axolotl` config is used to
-    generate completions according to a default generation config.
+    Runs inference on the command line in a loop. User input is accepted, a chat
+    template is (optionally) applied, and the model specified in the `axolotl` config is
+    used to generate completions according to a default generation config.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -64,16 +72,28 @@ def do_inference(
         chat_template_str = get_chat_template_from_config(
             cfg, ds_cfg=None, tokenizer=tokenizer
         )
-    elif cfg.datasets[0].type == "chat_template":
+    elif cfg.datasets and cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
         )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
+    # Detect diffusion mode
+    plugin_manager = PluginManager.get_instance()
+    is_diffusion = any(
+        plugin.__class__.__name__ == "DiffusionPlugin"
+        for plugin in plugin_manager.plugins.values()
+    )
+
+    if is_diffusion:
+        print("=" * 80)
+        print("Commands:")
+        print(":complete N -> completion mode with N tokens (default 64)")
+        print(":mask R     -> random masking with ratio R (0.0–1.0)")
+
     while True:
         print("=" * 80)
-        # support for multiline inputs
         instruction = get_multi_line_input()
         if not instruction:
             return
@@ -103,9 +123,19 @@ def do_inference(
         else:
             batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
-        print("=" * 40)
+        print("=" * 80)
         model.eval()
         with torch.no_grad():
+            if is_diffusion:
+                diffusion_inference(
+                    model=model,
+                    tokenizer=tokenizer,
+                    cfg=cfg,
+                    prompt=prompt,
+                    chat_template_str=chat_template_str,
+                )
+                continue
+
             generation_config = GenerationConfig(
                 repetition_penalty=1.1,
                 max_new_tokens=1024,
@@ -128,7 +158,7 @@ def do_inference(
                 generation_config=generation_config,
                 streamer=streamer,
             )
-        print("=" * 40)
+        print("=" * 80)
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
@@ -161,13 +191,30 @@ def do_inference_gradio(
         chat_template_str = get_chat_template_from_config(
             cfg, ds_cfg=None, tokenizer=tokenizer
         )
-    elif cfg.datasets[0].type == "chat_template":
+    elif cfg.datasets and cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
         )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
+    # Detect diffusion mode
+    plugin_manager = PluginManager.get_instance()
+    is_diffusion = any(
+        plugin.__class__.__name__ == "DiffusionPlugin"
+        for plugin in plugin_manager.plugins.values()
+    )
+
+    if is_diffusion:
+        launch_diffusion_gradio_ui(
+            model=model,
+            tokenizer=tokenizer,
+            cfg=cfg,
+            prompter_module=prompter_module,
+            chat_template_str=chat_template_str,
+        )
+        return
+
     def generate(instruction):
         if not instruction:
             return

src/axolotl/cli/utils/diffusion.py

@@ -0,0 +1,375 @@
+"""Helpers for diffusion-mode inference in CLI and Gradio."""
+
+from __future__ import annotations
+
+import gradio as gr
+import torch
+from colorama import Fore, Style
+
+from axolotl.integrations.diffusion import generate, resolve_mask_token_id
+from axolotl.utils.dict import DictDefault
+
+
+def diffusion_inference(
+    model,
+    tokenizer,
+    cfg,
+    prompt: str,
+    chat_template_str: str | None = None,
+):
+    """Diffusion inference helper method."""
+    mode = "random"
+    completion_tokens = 0
+    target_mask_ratio = None
+    mode, completion_tokens, target_mask_ratio, cleaned = _parse_commands(prompt)
+
+    if cleaned:
+        prompt = cleaned
+
+    info = run_diffusion(
+        model=model,
+        tokenizer=tokenizer,
+        cfg=cfg,
+        prompt=prompt,
+        chat_template_str=chat_template_str,
+        mode=mode,
+        target_mask_ratio=target_mask_ratio,
+        completion_tokens=completion_tokens,
+    )
+    masked_text = info["masked_text"]
+    mask_ratio = info["mask_ratio"]
+    generated_ids = info["generated_ids"]
+    masked_positions = info["masked_positions"]
+    orig_ids = info["orig_ids"]
+
+    # Display with masked preview and colored diff
+    if masked_text is not None and mask_ratio is not None:
+        print(f"Masked ({mask_ratio:.1%}):\n{masked_text}\n")
+    if generated_ids is not None:
+        # Compute per-token style
+        styles: list[str] = []
+        for i, tid in enumerate(generated_ids):
+            if i in masked_positions:
+                if i < len(orig_ids) and tid == orig_ids[i]:
+                    styles.append("green")  # correct fill
+                elif i < len(orig_ids):
+                    styles.append("red")  # incorrect fill
+                else:
+                    styles.append("normal")  # appended
+            else:
+                same = i < len(orig_ids) and tid == orig_ids[i]
+                styles.append("dim" if same else "normal")
+
+        # Group contiguous spans by style
+        styled_spans: list[tuple[str, int, int]] = []
+        if generated_ids:
+            current_style = styles[0]
+            start = 0
+            for i in range(1, len(generated_ids)):
+                s = styles[i]
+                if s != current_style:
+                    styled_spans.append((current_style, start, i))
+                    current_style, start = s, i
+            styled_spans.append((current_style, start, len(generated_ids)))
+
+        out_parts = []
+        for style_name, a, b in styled_spans:
+            chunk_text = tokenizer.decode(generated_ids[a:b], skip_special_tokens=False)
+            if style_name == "green":
+                out_parts.append(Fore.GREEN + chunk_text + Style.RESET_ALL)
+            elif style_name == "red":
+                out_parts.append(Fore.RED + chunk_text + Style.RESET_ALL)
+            else:
+                if style_name == "dim":
+                    out_parts.append(Style.DIM + chunk_text + Style.RESET_ALL)
+                else:
+                    out_parts.append(chunk_text)
+        print("Generated:\n" + "".join(out_parts))
+    else:
+        print("Generated:\n(no output)")
+
+
+def _parse_commands(text: str):
+    """
+    Parse leading diffusion commands.
+
+    Supported at start of input (can be chained):
+      :complete N  -> completion mode with N tokens (default 64)
+      :mask R      -> random masking with ratio R in [0, 1]
+    """
+    tokens = text.strip().split()
+    i = 0
+    mode = "random"
+    completion_tokens = 0
+    target_mask_ratio = None
+    consumed = 0
+    while i < len(tokens) and tokens[i].startswith(":"):
+        cmd = tokens[i]
+        i += 1
+        consumed = i
+        if cmd == ":complete":
+            mode = "completion"
+            if i < len(tokens):
+                try:
+                    completion_tokens = int(tokens[i])
+                    i += 1
+                    consumed = i
+                except Exception:
+                    completion_tokens = 64
+            else:
+                completion_tokens = 64
+        elif cmd == ":mask":
+            mode = "random"
+            if i < len(tokens):
+                try:
+                    target_mask_ratio = float(tokens[i])
+                    i += 1
+                    consumed = i
+                except Exception:
+                    target_mask_ratio = None
+        else:
+            i -= 1
+            consumed = i
+            break
+
+    cleaned = " ".join(tokens[consumed:])
+
+    return mode, completion_tokens, target_mask_ratio, cleaned
+
+
+def run_diffusion(
+    *,
+    model,
+    tokenizer,
+    cfg: DictDefault,
+    prompt: str,
+    chat_template_str: str | None,
+    mode: str = "random",
+    target_mask_ratio: float | None = None,
+    completion_tokens: int = 0,
+):
+    """Run a single diffusion generation and return a structured result dict."""
+    if chat_template_str:
+        batch = tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            return_tensors="pt",
+            add_special_tokens=True,
+            add_generation_prompt=True,
+            chat_template=chat_template_str,
+            tokenize=True,
+            return_dict=True,
+        )
+    else:
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+    mask_token_id = resolve_mask_token_id(tokenizer, cfg, allow_add=False)
+
+    seq = batch["input_ids"].to(cfg.device)
+    gen_mode = "completion" if mode == "completion" else "random"
+    comp_tokens = int(completion_tokens) if gen_mode == "completion" else 0
+
+    result = generate(
+        model,
+        tokenizer,
+        original_sequence=seq[:1],
+        num_diffusion_steps=cfg.diffusion.num_diffusion_steps,
+        temperature=cfg.diffusion.generation_temperature,
+        mask_token_id=int(mask_token_id),
+        mode=gen_mode,  # type: ignore[arg-type]
+        completion_tokens=comp_tokens,
+        target_mask_ratio=target_mask_ratio,
+    )
+
+    masked_text = result.get("masked") if isinstance(result, dict) else None
+    mask_ratio = result.get("mask_ratio") if isinstance(result, dict) else None
+    generated_ids = result.get("generated_ids") if isinstance(result, dict) else None
+    masked_positions = (
+        set(result.get("masked_positions") or []) if isinstance(result, dict) else set()
+    )
+    orig_ids = seq[0].detach().cpu().tolist()
+
+    return {
+        "masked_text": masked_text,
+        "mask_ratio": mask_ratio,
+        "generated_ids": generated_ids,
+        "masked_positions": masked_positions,
+        "orig_ids": orig_ids,
+    }
+
+
+def render_html(
+    *,
+    generated_ids: list[int] | None,
+    orig_ids: list[int],
+    masked_positions: set[int],
+    tokenizer,
+) -> str:
+    """Render HTML visualizing diffusion outputs."""
+    if not generated_ids:
+        return "<pre>Generated:\n(no output)</pre>"
+
+    def _style_for(i: int, tid: int) -> str:
+        if i in masked_positions:
+            if i < len(orig_ids) and tid == orig_ids[i]:
+                return "green"
+            if i < len(orig_ids):
+                return "red"
+            return "normal"
+        same = i < len(orig_ids) and tid == orig_ids[i]
+        return "dim" if same else "normal"
+
+    # Group contiguous spans by style to reduce HTML size
+    spans: list[tuple[str, int, int]] = []
+    if generated_ids:
+        cur = _style_for(0, generated_ids[0])
+        start = 0
+        for i in range(1, len(generated_ids)):
+            s = _style_for(i, generated_ids[i])
+            if s != cur:
+                spans.append((cur, start, i))
+                cur, start = s, i
+        spans.append((cur, start, len(generated_ids)))
+
+    html_parts = []
+    for style_name, a, b in spans:
+        txt = tokenizer.decode(generated_ids[a:b], skip_special_tokens=False)
+        if style_name == "green":
+            html_parts.append(f'<span style="color:#2e7d32">{txt}</span>')
+        elif style_name == "red":
+            html_parts.append(f'<span style="color:#c62828">{txt}</span>')
+        elif style_name == "dim":
+            html_parts.append(f'<span style="opacity:0.6">{txt}</span>')
+        else:
+            html_parts.append(txt)
+
+    legend = (
+        '<div style="font-size:0.9em;margin-bottom:4px">'
+        '<span style="color:#2e7d32">correct</span>, '
+        '<span style="color:#c62828">incorrect</span>, '
+        '<span style="opacity:0.6">unchanged</span>'
+        "</div>"
+    )
+
+    return (
+        legend
+        + '<pre style="white-space:pre-wrap">Generated:\n'
+        + "".join(html_parts)
+        + "</pre>"
+    )
+
+
+def launch_diffusion_gradio_ui(
+    *,
+    model,
+    tokenizer,
+    cfg: DictDefault,
+    prompter_module=None,
+    chat_template_str: str | None = None,
+):
+    """Build and launch a simple Gradio UI for diffusion inference."""
+    with gr.Blocks(
+        title=cfg.get("gradio_title", "Axolotl Diffusion Interface")
+    ) as demo:
+        gr.Markdown(
+            """
+            ## Axolotl Diffusion Inference
+            - Mode "Random" masks tokens at a target ratio and fills them.
+            - Mode "Completion" appends N masked tokens at the end and fills them.
+            """
+        )
+
+        with gr.Row():
+            mode = gr.Radio(
+                choices=["random", "completion"],
+                value="random",
+                label="Mode",
+            )
+            mask_ratio = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.4,
+                label="Mask ratio (random mode)",
+                interactive=True,
+            )
+            completion_tokens = gr.Number(
+                value=64,
+                precision=0,
+                label="Completion tokens (completion mode)",
+                interactive=True,
+                visible=False,
+            )
+
+        instruction = gr.Textbox(label="Instruction", lines=6)
+        run_btn = gr.Button("Generate")
+
+        masked_preview = gr.Textbox(label="Masked preview", lines=6)
+        html_out = gr.HTML(label="Generated")
+
+        def _toggle_controls(selected_mode: str):
+            return (
+                gr.update(visible=(selected_mode == "random")),
+                gr.update(visible=(selected_mode == "completion")),
+            )
+
+        mode.change(
+            _toggle_controls,
+            inputs=[mode],
+            outputs=[mask_ratio, completion_tokens],
+        )
+
+        def _gen(instruction_text: str, selected_mode: str, mratio: float, ctoks: int):
+            if not instruction_text:
+                return "", "<pre>Generated:\n(no output)</pre>"
+
+            if prompter_module:
+                prompt: str = next(
+                    prompter_module().build_prompt(
+                        instruction=instruction_text.strip("\n")
+                    )
+                )
+            else:
+                prompt = instruction_text.strip()
+
+            info = run_diffusion(
+                model=model,
+                tokenizer=tokenizer,
+                cfg=cfg,
+                prompt=prompt,
+                chat_template_str=chat_template_str,
+                mode=selected_mode,
+                target_mask_ratio=mratio if selected_mode == "random" else None,
+                completion_tokens=int(ctoks) if selected_mode == "completion" else 0,
+            )
+
+            masked_text = info.get("masked_text")
+            mask_ratio_val = info.get("mask_ratio")
+            generated_ids = info.get("generated_ids")
+            masked_positions = info.get("masked_positions") or set()
+            orig_ids = info.get("orig_ids") or []
+
+            preview = (
+                f"Masked ({mask_ratio_val:.1%}):\n{masked_text}"
+                if masked_text is not None and mask_ratio_val is not None
+                else ""
+            )
+            html = render_html(
+                generated_ids=generated_ids,
+                orig_ids=orig_ids,
+                masked_positions=masked_positions,
+                tokenizer=tokenizer,
+            )
+            return preview, html
+
+        run_btn.click(
+            _gen,
+            inputs=[instruction, mode, mask_ratio, completion_tokens],
+            outputs=[masked_preview, html_out],
+        )
+
+        demo.queue().launch(
+            show_api=False,
+            share=cfg.get("gradio_share", True),
+            server_name=cfg.get("gradio_server_name", "127.0.0.1"),
+            server_port=cfg.get("gradio_server_port", None),
+        )

src/axolotl/core/builders/causal.py

@@ -7,7 +7,11 @@ from pathlib import Path
 from typing import Type, Union
 
 import transformers
-from transformers import DataCollatorWithFlattening, EarlyStoppingCallback
+from transformers import (
+    DataCollatorWithFlattening,
+    EarlyStoppingCallback,
+    Trainer,
+)
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
 from axolotl.core.builders.base import TrainerBuilderBase
@@ -23,15 +27,16 @@ from axolotl.monkeypatch.relora import ReLoRACallback
 from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
+    LossWatchDogCallback,
+    SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     colab_inference_post_train_callback,
     log_prediction_callback_factory,
-    LossWatchDogCallback,
-    SaveBetterTransformerModelCallback,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
 from axolotl.utils.callbacks.qat import QATCallback
+from axolotl.utils.callbacks.tokens_per_second import TokensPerSecondCallback
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
@@ -39,7 +44,6 @@ from axolotl.utils.collators import (
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
-from axolotl.utils.callbacks.tokens_per_second import TokensPerSecondCallback
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.import_helper import get_cls_from_module_str
 from axolotl.utils.logging import get_logger
@@ -391,10 +395,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 **data_collator_kwargs,
             )
         sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters:
+        if "processing_class" in sig.parameters or issubclass(trainer_cls, Trainer):
             trainer_kwargs["processing_class"] = self.tokenizer
         elif "tokenizer" in sig.parameters:
             trainer_kwargs["tokenizer"] = self.tokenizer
+
         if (
             trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
             and self.cfg.datasets is not None

src/axolotl/core/trainers/base.py

@@ -49,6 +49,13 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger(__name__)
 
+REDUCTION_FNS = {
+    "mean": torch.mean,
+    "min": torch.min,
+    "max": torch.max,
+    "sum": torch.sum,
+}
+
 
 class AxolotlTrainer(
     PackingMixin,
@@ -89,7 +96,9 @@ class AxolotlTrainer(
 
         super().__init__(*_args, **kwargs)
         self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        self._stored_metrics = defaultdict(
+            lambda: defaultdict(lambda: {"values": [], "reduction": "mean"})
+        )
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
@@ -585,9 +594,17 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
+
+        for key, metric_data in self._stored_metrics[train_eval].items():
+            values = torch.tensor(metric_data["values"])  # type: ignore[arg-type]
+            reduction_type = metric_data["reduction"]
+
+            fn = REDUCTION_FNS.get(reduction_type)
+            if fn is None:
+                raise NotImplementedError(
+                    "Metric reduction must be one of [mean, min, max, sum]"
+                )
+            logs[key] = round(fn(values).item(), 4)
 
         if is_main_process():
             # Add memory usage
@@ -611,10 +628,27 @@ class AxolotlTrainer(
         return super().log(logs, start_time)
 
     def store_metrics(
-        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
+        self,
+        metrics: dict[str, float] | dict[str, tuple[int | float, str]],
+        train_eval: Literal["train", "eval"] = "train",
+        reduction: Literal["mean", "min", "max", "sum"] = "mean",
     ) -> None:
+        """
+        Store metrics with specified reduction type.
+
+        Args:
+            metrics: Dictionary of metric names to values, or metric names to (value,
+                reduction_type) tuples.
+            train_eval: Whether this is for training or evaluation.
+        """
         for key, value in metrics.items():
-            self._stored_metrics[train_eval][key].append(value)
+            if isinstance(value, tuple):
+                value, _reduction = value  # type: ignore[assignment]
+            else:
+                value, _reduction = value, reduction
+
+            self._stored_metrics[train_eval][key]["values"].append(value)
+            self._stored_metrics[train_eval][key]["reduction"] = _reduction
 
     def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey

src/axolotl/integrations/base.py

@@ -142,7 +142,7 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    def get_trainer_cls(self, cfg: DictDefault) -> Trainer | None:
+    def get_trainer_cls(self, cfg: DictDefault) -> type[Trainer] | None:
         """Returns a custom class for the trainer.
 
         Args:

src/axolotl/integrations/config.py

@@ -20,8 +20,8 @@ from typing import Any, Dict, List, Type
 
 from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+    AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
 
 
 def merge_input_args():

src/axolotl/integrations/diffusion/README.md

@@ -0,0 +1,154 @@
+# Diffusion LM Training Plugin for Axolotl
+
+This plugin enables diffusion language model training using an approach inspired by
+LLaDA (Large Language Diffusion Models) within Axolotl.
+
+## Overview
+
+LLaDA is a diffusion-based approach to language model training that uses:
+- **Random token masking** during training instead of next-token prediction
+- **Bidirectional attention** to allow the model to attend to the full context
+- **Importance weighting** based on masking probabilities for stable training
+
+This approach can lead to more robust language models with better understanding of
+bidirectional context.
+
+## Installation
+
+The plugin is included with Axolotl. See our
+[installation docs](https://docs.axolotl.ai/docs/installation.html).
+
+## Quickstart
+
+Train with an example config (Llama‑3.2 1B):
+   - Pretrain: `axolotl train examples/llama-3/diffusion-3.2-1b-pretrain.yaml`
+   - SFT: `axolotl train examples/llama-3/diffusion-3.2-1b-sft.yaml`
+
+### Basic Configuration
+
+You can also modify your existing configs to enable / customize diffusion training.
+
+Add the following to your Axolotl config:
+
+```yaml
+# Enable diffusion LM training plugin
+plugins:
+  - axolotl.integrations.diffusion.DiffusionPlugin
+```
+
+And, configure the nested `diffusion` block (defaults shown):
+
+```yaml
+diffusion:
+  noise_schedule: linear  # or "cosine"
+  min_mask_ratio: 0.1
+  max_mask_ratio: 0.9
+  num_diffusion_steps: 128
+  eps: 1e-3
+  importance_weighting: true
+
+  # Mask token (training auto-adds if missing, avoid pad/eos)
+  mask_token_str: "<|diffusion_mask|>"
+  # Or use an existing special token id (e.g., 128002 for Llama-3.x)
+  # mask_token_id: 128002
+
+  # Sample generation during training (optional)
+  generate_samples: true
+  generation_interval: 100
+  num_generation_samples: 3
+  generation_steps: 128
+  generation_temperature: 0.0
+  generation_max_length: 100
+```
+
+## Supported Models
+
+Any models that support 4D attention masks should work out of the box. If not, please
+create an [issue](https://github.com/axolotl-ai-cloud/axolotl/issues) or open a
+[PR](https://github.com/axolotl-ai-cloud/axolotl/compare)!
+
+## How It Works
+
+### Random Masking
+During training, tokens are randomly masked:
+- Sample timestep `t` uniformly from [0, 1]
+- Calculate masking probability: `p = (1 - eps) * t + eps`
+- Randomly mask tokens with probability `p`
+
+### Diffusion Loss
+
+Loss is computed only on masked tokens with (optional) importance weighting:
+
+```python
+loss = sum(cross_entropy(pred, target) / p_mask) / total_tokens
+```
+
+## Sample Generation
+
+When `diffusion.generate_samples: true`, the plugin generates samples during training:
+
+```
+Sample 1:
+   Original (45 tokens): The quick brown fox jumps over the lazy dog...
+   Masked (18/45 tokens, 40.0%): The [MASK] [MASK] fox [MASK] over [MASK] lazy [MASK]...
+   Generated: The quick brown fox jumps over the lazy dog...
+```
+
+Samples are logged to console and wandb (if enabled).
+
+## Inference
+
+Diffusion inference is integrated into the standard Axolotl CLI. Use the same config
+you trained with and run:
+
+```
+axolotl inference path/to/your-config.yaml
+```
+
+Optionally, pass `--gradio` to use a simple web interface.
+
+Interactive controls (prefix the prompt with commands):
+- `:complete N` → completion mode with N new masked tokens appended (default 64)
+- `:mask R` → random masking mode with target mask ratio R in [0.0, 1.0]
+
+Example session:
+
+```
+================================================================================
+Commands:
+:complete N -> completion mode with N tokens (default 64)
+:mask R     -> random masking with ratio R (0.0–1.0)
+================================================================================
+Give me an instruction (Ctrl + D to submit):
+
+:mask 0.4 The quick brown fox jumps over the lazy dog
+
+Masked (40.0%):
+The [MASK] brown [MASK] jumps over the [MASK] dog
+
+Generated:
+The quick brown fox jumps over the loud dog
+```
+
+## Metrics and Monitoring
+
+The plugin adds (or modifies) several metrics to track diffusion training:
+
+- `train/loss`: Weighted diffusion loss
+- `train/accuracy`: Accuracy on masked tokens
+- `train/mask_ratio`: Average fraction of tokens masked
+- `train/num_masked_tokens`: Number of tokens masked
+- `train/avg_p_mask`: Average masking probability
+- `train/ce_loss`: Unweighted cross-entropy loss
+- `train/importance_weight_avg`: Average importance weight
+
+## Limitations
+
+- No flash attention support
+- No RL training support
+
+## References
+
+- [LLaDA Paper](https://arxiv.org/abs/2404.10406)
+- [Axolotl Documentation](https://docs.axolotl.ai/)
+- [API reference for plugin](https://docs.axolotl.ai/docs/api/integrations.diffusion.args.html#axolotl.integrations.diffusion.args)

src/axolotl/integrations/diffusion/__init__.py

@@ -0,0 +1,19 @@
+"""Diffusion LM training plugin init."""
+
+from .args import DiffusionArgs, DiffusionConfig
+from .callbacks import DiffusionGenerationCallback
+from .generation import generate
+from .plugin import DiffusionPlugin
+from .trainer import DiffusionTrainer
+from .utils import create_bidirectional_attention_mask, resolve_mask_token_id
+
+__all__ = [
+    "DiffusionArgs",
+    "DiffusionPlugin",
+    "DiffusionTrainer",
+    "generate",
+    "resolve_mask_token_id",
+    "create_bidirectional_attention_mask",
+    "DiffusionGenerationCallback",
+    "DiffusionConfig",
+]

src/axolotl/integrations/diffusion/args.py

@@ -0,0 +1,95 @@
+"""Config args for diffusion LM training (nested under `diffusion:`)."""
+
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class DiffusionConfig(BaseModel):
+    """Nested diffusion configuration available under the `diffusion` key."""
+
+    # Noise schedule config
+    noise_schedule: Literal["linear", "cosine"] = Field(
+        default="linear", description="Type of noise schedule for diffusion training"
+    )
+    min_mask_ratio: float = Field(
+        default=0.1,
+        ge=0.0,
+        le=1.0,
+        description="Minimum masking ratio for diffusion noise schedule",
+    )
+    max_mask_ratio: float = Field(
+        default=0.9,
+        ge=0.0,
+        le=1.0,
+        description="Maximum masking ratio for diffusion noise schedule",
+    )
+    num_diffusion_steps: int = Field(
+        default=128, ge=1, description="Number of diffusion timesteps"
+    )
+    eps: float = Field(
+        default=1e-3,
+        ge=0.0,
+        le=1.0,
+        description="Epsilon value for minimum masking probability in forward process",
+    )
+
+    # Training config
+    importance_weighting: bool = Field(
+        default=True,
+        description="Apply importance weighting to loss based on masking probability",
+    )
+    mask_token_id: int | None = Field(
+        default=None,
+        description=(
+            "Token ID to use for masking. Unset by default; can use one of the "
+            "tokenizer's special tokens here."
+        ),
+    )
+    mask_token_str: str | None = Field(
+        default=None,
+        description=(
+            "Token string to use as a mask. If `mask_token_id` is invalid or unset, "
+            "this token will be ensured to exist as an additional special token and "
+            "used. If absent, a default '<|diffusion_mask|>' will be added."
+        ),
+    )
+
+    # Sample generation config
+    generate_samples: bool = Field(
+        default=True, description="Enable sample generation during training"
+    )
+    generation_interval: int = Field(
+        default=100, ge=1, description="Generate samples every N steps"
+    )
+    num_generation_samples: int = Field(
+        default=3, ge=1, description="Number of samples to generate each time"
+    )
+    generation_steps: int = Field(
+        default=128, ge=1, description="Number of diffusion steps for generation"
+    )
+    generation_temperature: float = Field(
+        default=0.0,
+        ge=0.0,
+        description="Temperature for generation sampling (0.0 = deterministic)",
+    )
+    generation_max_length: int = Field(
+        default=100, ge=1, description="Maximum sequence length for generation"
+    )
+
+    @model_validator(mode="after")
+    def _validate_mask_ratios(self) -> "DiffusionConfig":
+        if self.min_mask_ratio > self.max_mask_ratio:
+            raise ValueError("min_mask_ratio must be ≤ max_mask_ratio")
+        return self
+
+
+class DiffusionArgs(BaseModel):
+    """Plugin entry that exposes the nested `diffusion` block to the core config."""
+
+    diffusion: DiffusionConfig = Field(
+        default_factory=DiffusionConfig,
+        description="Diffusion training configuration. Only nested block is supported.",
+    )

src/axolotl/integrations/diffusion/callbacks.py

@@ -0,0 +1,174 @@
+"""Callbacks for diffusion training."""
+
+import logging
+import sys
+
+import wandb
+from colorama import Fore, Style
+from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
+from transformers.training_args import TrainingArguments
+
+from .generation import generate_samples
+
+# Simpler logger for more readable sample generation
+logger = logging.getLogger(__name__)
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(logging.Formatter("%(message)s"))
+    logger.addHandler(handler)
+    logger.propagate = False
+logger.setLevel(logging.INFO)
+
+
+class DiffusionGenerationCallback(TrainerCallback):
+    """Callback for generating samples during diffusion training."""
+
+    def __init__(self, trainer):
+        self.trainer = trainer
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Generate samples at specified intervals."""
+        if (
+            state.global_step > 0
+            and state.global_step % self.trainer.cfg.diffusion.generation_interval == 0
+        ):
+            if not self.trainer.state.is_world_process_zero:
+                return
+
+            # Use eval dataloader if available, otherwise use train dataloader
+            dataloader = None
+            try:
+                if getattr(self.trainer, "eval_dataset", None) is not None:
+                    dataloader = self.trainer.get_eval_dataloader()
+            except Exception:
+                dataloader = None
+            if dataloader is None:
+                dataloader = self.trainer.get_train_dataloader()
+
+            # Generate samples
+            diffusion_cfg = self.trainer.cfg.diffusion
+            samples = generate_samples(
+                model=self.trainer.model,
+                tokenizer=self.trainer.processing_class,
+                dataloader=dataloader,
+                num_generation_samples=diffusion_cfg.num_generation_samples,
+                max_length=diffusion_cfg.generation_max_length,
+                num_diffusion_steps=diffusion_cfg.generation_steps,
+                temperature=diffusion_cfg.generation_temperature,
+                mask_token_id=diffusion_cfg.mask_token_id,
+            )
+
+            # Log samples
+            self._log_samples(samples, state.global_step)
+
+    def _log_samples(self, samples: list, step: int):
+        """Log generated samples."""
+        if not samples:
+            return
+
+        logger.info("=" * 60)
+        logger.info("GENERATED SAMPLES")
+        logger.info("=" * 60)
+
+        for i, sample_data in enumerate(samples, 1):
+            original = sample_data["original"]
+            masked = sample_data["masked"]
+            generated = sample_data["generated"]
+            mask_ratio = sample_data["mask_ratio"]
+            masked_tokens = sample_data["masked_tokens"]
+            total_tokens = sample_data["total_tokens"]
+
+            logger.info(f"\nSample {i}:")
+            logger.info(f"\tOriginal ({total_tokens} tokens): {original}")
+            logger.info(
+                f"\tMasked ({masked_tokens}/{total_tokens} tokens, "
+                f"{mask_ratio:.1%}): {masked}"
+            )
+
+            try:
+                gen_ids = sample_data.get("generated_ids")
+                orig_ids = sample_data.get("orig_ids")
+                masked_positions = set(sample_data.get("masked_positions") or [])
+                if isinstance(gen_ids, list) and isinstance(orig_ids, list):
+                    styles: list[str] = []
+                    for i, tid in enumerate(gen_ids):
+                        if i in masked_positions:
+                            if i < len(orig_ids) and tid == orig_ids[i]:
+                                styles.append("green")
+                            elif i < len(orig_ids):
+                                styles.append("red")
+                            else:
+                                styles.append("normal")
+                        else:
+                            same = i < len(orig_ids) and tid == orig_ids[i]
+                            styles.append("dim" if same else "normal")
+
+                    spans: list[tuple[str, int, int]] = []
+                    if gen_ids:
+                        cur = styles[0]
+                        start = 0
+                        for i in range(1, len(gen_ids)):
+                            s = styles[i]
+                            if s != cur:
+                                spans.append((cur, start, i))
+                                cur, start = s, i
+                        spans.append((cur, start, len(gen_ids)))
+
+                    parts = []
+                    for style_name, a, b in spans:
+                        chunk_text = self.trainer.processing_class.decode(
+                            gen_ids[a:b], skip_special_tokens=False
+                        )
+                        if style_name == "green":
+                            parts.append(Fore.GREEN + chunk_text + Style.RESET_ALL)
+                        elif style_name == "red":
+                            parts.append(Fore.RED + chunk_text + Style.RESET_ALL)
+                        else:
+                            if style_name == "dim":
+                                parts.append(Style.DIM + chunk_text + Style.RESET_ALL)
+                            else:
+                                parts.append(chunk_text)
+                    logger.info("\tGenerated:\n%s", "".join(parts))
+                else:
+                    logger.info(f"\tGenerated: {generated}")
+            except Exception:
+                logger.info(f"\tGenerated: {generated}")
+
+        logger.info("=" * 60)
+
+        if self.trainer.cfg.use_wandb:
+            if wandb.run is not None:
+                wandb.log(
+                    {
+                        "generated_samples": wandb.Table(
+                            columns=[
+                                "step",
+                                "original",
+                                "masked",
+                                "generated",
+                                "mask_ratio",
+                                "masked_tokens",
+                                "total_tokens",
+                            ],
+                            data=[
+                                [
+                                    step,
+                                    sample["original"],
+                                    sample["masked"],
+                                    sample["generated"],
+                                    f"{sample['mask_ratio']:.1%}",
+                                    sample["masked_tokens"],
+                                    sample["total_tokens"],
+                                ]
+                                for sample in samples
+                            ],
+                        )
+                    },
+                    step=step,
+                )

src/axolotl/integrations/diffusion/generation.py

@@ -0,0 +1,409 @@
+"""Sample generation utilities for diffusion training."""
+
+import re
+from typing import Any, List, Literal, Optional
+
+import torch
+
+from axolotl.utils.logging import get_logger
+
+from .utils import create_bidirectional_attention_mask
+
+LOG = get_logger(__name__)
+
+
+def generate_samples(
+    model: torch.nn.Module,
+    tokenizer: Any,
+    dataloader: Optional[Any] = None,
+    num_generation_samples: int = 3,
+    max_length: int = 100,
+    num_diffusion_steps: int = 128,
+    temperature: float = 0.0,
+    mask_token_id: int = 32000,
+    mode: Literal["random", "completion"] = "random",
+    completion_tokens: int = 0,
+    target_mask_ratio: Optional[float] = None,
+) -> List[dict]:
+    """
+    Generate text samples using the diffusion model by randomly masking sequences from
+    the given dataset and running the reverse diffusion process.
+
+    Args:
+        model: The wrapped or unwrapped model
+        tokenizer: Tokenizer for encoding/decoding
+        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
+        temperature: Temperature for sampling (0.0 = deterministic)
+        mask_token_id: Token ID used for masking
+
+    Returns:
+        List of dictionaries with original text, masked text, and generated text
+    """
+    if dataloader is None:
+        LOG.warning("No validation dataloader provided, cannot generate samples")
+        return []
+
+    unwrapped_model = model.module if hasattr(model, "module") else model
+    training = unwrapped_model.training
+    unwrapped_model.eval()
+
+    # Resolve device robustly (some modules don't expose `.device`)
+    device = getattr(unwrapped_model, "device", None)
+    if device is None:
+        try:
+            device = next(unwrapped_model.parameters()).device
+        except StopIteration:
+            device = torch.device("cpu")
+    generations = []
+
+    # Sample sequences from validation dataset
+    sampled_sequences = _sample_sequences_from_dataloader(
+        dataloader, num_generation_samples, max_length, device
+    )
+    LOG.info(f"Sampled {len(sampled_sequences)} sequences from validation dataset")
+
+    # Generate samples using reverse diffusion process
+    with torch.no_grad():
+        for sample in sampled_sequences:
+            if isinstance(sample, dict):
+                original_sequence = sample.get("input_ids")
+                labels_seq = sample.get("labels")
+                attn_seq = sample.get("attention_mask")
+            else:
+                original_sequence = sample
+                labels_seq = None
+                attn_seq = None
+            generation_result = generate(
+                unwrapped_model,
+                tokenizer,
+                original_sequence,
+                num_diffusion_steps,
+                temperature,
+                mask_token_id,
+                mode=mode,
+                completion_tokens=completion_tokens,
+                target_mask_ratio=target_mask_ratio,
+                labels=labels_seq,
+                attention_mask=attn_seq,
+            )
+            generations.append(generation_result)
+
+    # Restore prior training state
+    if training:
+        unwrapped_model.train()
+    else:
+        unwrapped_model.eval()
+
+    return generations
+
+
+def _sample_sequences_from_dataloader(
+    dataloader: Any, num_samples: int, max_length: int, device: torch.device
+) -> List[Any]:
+    """Sample sequences from validation dataloader."""
+    sampled_sequences: list[dict[str, torch.Tensor] | torch.Tensor] = []
+    sample_count = 0
+
+    # Skip a random number of batches (we could be more clever about this)
+    skip_batches = torch.randint(0, 10, (1,)).item()
+    batch_count = 0
+
+    for batch in dataloader:
+        # Skip some batches for variety
+        if batch_count < skip_batches:
+            batch_count += 1
+            continue
+
+        if sample_count >= num_samples:
+            break
+
+        batch_count += 1
+        input_ids = batch["input_ids"]
+        attention_mask = batch.get("attention_mask")
+        labels = batch.get("labels")
+
+        # Randomly sample from sequences in this batch
+        batch_indices = torch.randperm(input_ids.size(0)).tolist()
+
+        for i in batch_indices:
+            if sample_count >= num_samples:
+                break
+
+            # Get actual sequence length (non-padded)
+            if attention_mask is not None:
+                seq_len = attention_mask[i].sum().item()
+            else:
+                seq_len = input_ids.size(1)
+
+            if seq_len < 10:
+                continue
+
+            # Determine truncation length
+            max_total = min(seq_len, max_length)
+            if labels is not None:
+                labels_i = labels[i][:seq_len]
+                answer_mask = labels_i != -100
+                if not answer_mask.any():
+                    # No answer tokens; skip for SFT masking
+                    continue
+                first_ans_idx = int(
+                    torch.nonzero(answer_mask, as_tuple=False)[0].item()
+                )
+                prompt_len = first_ans_idx
+                if prompt_len >= max_total:
+                    # Prompt alone reaches cap; cannot include any answer
+                    continue
+                remaining_answer = int(answer_mask[prompt_len:].sum().item())
+                allowed_answer = max_total - prompt_len
+                take_answer = min(remaining_answer, allowed_answer)
+                if take_answer <= 0:
+                    continue
+                actual_length = prompt_len + take_answer
+            else:
+                actual_length = max_total
+
+            # Extract the (possibly truncated) sequence
+            sequence = input_ids[i][:actual_length].unsqueeze(0).to(device)
+            attn_seq = (
+                attention_mask[i][:actual_length].unsqueeze(0).to(device)
+                if attention_mask is not None
+                else None
+            )
+            if labels is not None:
+                labels_seq = labels[i][:actual_length].unsqueeze(0).to(device)
+                sampled_sequences.append(
+                    {
+                        "input_ids": sequence,
+                        "labels": labels_seq,
+                        "attention_mask": attn_seq,
+                    }
+                )
+            else:
+                if attn_seq is not None:
+                    sampled_sequences.append(
+                        {"input_ids": sequence, "attention_mask": attn_seq}
+                    )
+                else:
+                    sampled_sequences.append(sequence)
+            sample_count += 1
+
+    return sampled_sequences
+
+
+def generate(
+    model: torch.nn.Module,
+    tokenizer: Any,
+    original_sequence: torch.Tensor,
+    num_diffusion_steps: int,
+    temperature: float,
+    mask_token_id: int,
+    *,
+    mode: Literal["random", "completion"] = "random",
+    completion_tokens: int = 0,
+    target_mask_ratio: Optional[float] = None,
+    labels: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+) -> dict:
+    """Generate a single sample using reverse diffusion."""
+    # Get original text for comparison
+    original_text = tokenizer.decode(
+        original_sequence[0].cpu(), skip_special_tokens=True
+    )
+
+    # Build masked sequence
+    if (
+        labels is not None
+        and labels.numel() > 0
+        and (labels == -100).any()
+        and (labels != -100).any()
+    ):
+        # SFT case: completely mask all answer tokens (labels != -100)
+        total_tokens = original_sequence.size(1)
+        masked_indices = (labels != -100).to(dtype=torch.bool)
+        masked_sequence = original_sequence.clone()
+        masked_sequence[masked_indices] = mask_token_id
+        masked_tokens = int(masked_indices.sum().item())
+        mask_ratio = masked_tokens / max(int(total_tokens), 1)
+    elif mode == "completion" and completion_tokens > 0:
+        # Append mask tokens to the right for completion
+        total_tokens = original_sequence.size(1) + int(completion_tokens)
+        masked_indices = torch.zeros(
+            1, total_tokens, dtype=torch.bool, device=original_sequence.device
+        )
+        masked_indices[0, -int(completion_tokens) :] = True
+
+        append = torch.full(
+            (1, int(completion_tokens)), mask_token_id, device=original_sequence.device
+        )
+        masked_sequence = torch.cat([original_sequence, append], dim=1)
+        masked_tokens = int(completion_tokens)
+        mask_ratio = masked_tokens / total_tokens
+    else:
+        # Apply random masking with optional fixed ratio
+        total_tokens = original_sequence.size(1)
+        if target_mask_ratio is None:
+            min_ratio, max_ratio = 0.1, 0.7
+            target_mask_ratio = (
+                torch.rand(1).item() * (max_ratio - min_ratio) + min_ratio
+            )
+        target_masked_tokens = max(1, int(total_tokens * float(target_mask_ratio)))
+
+        # Create random mask indices
+        mask_positions = torch.randperm(total_tokens)[:target_masked_tokens]
+        masked_indices = torch.zeros(
+            1, total_tokens, dtype=torch.bool, device=original_sequence.device
+        )
+        masked_indices[0, mask_positions] = True
+
+        # Create masked sequence
+        masked_sequence = original_sequence.clone()
+        masked_sequence[masked_indices] = mask_token_id
+
+        # Calculate actual mask ratio
+        masked_tokens = masked_indices.sum().item()
+        mask_ratio = masked_tokens / total_tokens
+
+    # Get masked text for comparison
+    masked_text = tokenizer.decode(masked_sequence[0].cpu(), skip_special_tokens=False)
+    masked_text = _clean_masked_text(masked_text, tokenizer, mask_token_id)
+
+    # Run reverse diffusion process
+    sequence = masked_sequence.clone()
+    attention_mask = create_bidirectional_attention_mask(
+        sequence, attention_mask, sample_packing=attention_mask is not None
+    )
+    for step in range(num_diffusion_steps):
+        sequence = _diffusion_step(
+            model,
+            sequence,
+            step,
+            num_diffusion_steps,
+            temperature,
+            mask_token_id,
+            attention_mask,
+        )
+    generated_text = tokenizer.decode(sequence[0].cpu(), skip_special_tokens=True)
+
+    # Collect diagnostic info
+    final_ids = sequence[0].detach().cpu().tolist()
+    orig_ids_for_render = original_sequence[0].detach().cpu().tolist()
+    if masked_indices is not None:
+        masked_positions = (
+            torch.where(masked_indices[0])[0].detach().cpu().tolist()
+            if masked_indices.ndim == 2
+            else []
+        )
+    else:
+        masked_positions = []
+
+    result = {
+        "original": original_text,
+        "masked": masked_text,
+        "generated": generated_text,
+        "mask_ratio": mask_ratio,
+        "masked_tokens": masked_tokens,
+        "total_tokens": total_tokens,
+        "generated_ids": final_ids,
+        "masked_positions": masked_positions,
+        "orig_ids": orig_ids_for_render,
+        "formatted": (
+            f"Original: '{original_text}' → Masked: '{masked_text}' "
+            f"({mask_ratio:.1%}) → Generated: '{generated_text}'"
+        ),
+    }
+
+    return result
+
+
+def _clean_masked_text(masked_text: str, tokenizer: Any, mask_token_id: int) -> str:
+    """Clean up masked text for display."""
+    mask_token_repr = tokenizer.decode([mask_token_id], skip_special_tokens=False)
+    cleaned = masked_text.replace(mask_token_repr, "[MASK]")
+
+    # Remove literal special token strings
+    if hasattr(tokenizer, "special_tokens_map"):
+        for token_value in tokenizer.special_tokens_map.values():
+            if token_value and isinstance(token_value, str):
+                cleaned = cleaned.replace(token_value, "")
+
+    # Normalize whitespace but preserve newlines
+    cleaned = cleaned.replace("\r\n", "\n").replace("\r", "\n")
+    cleaned = re.sub(r"[ \t]+", " ", cleaned)
+    cleaned = "\n".join(line.rstrip() for line in cleaned.split("\n")).strip()
+    return cleaned
+
+
+def _diffusion_step(
+    model: torch.nn.Module,
+    sequence: torch.Tensor,
+    step: int,
+    num_diffusion_steps: int,
+    temperature: float,
+    mask_token_id: int,
+    attention_mask: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """Perform a single diffusion step with remasking."""
+    # Only process if there are masked tokens remaining
+    current_mask = sequence == mask_token_id
+    if not current_mask.any():
+        return sequence
+
+    # Create or use provided attention mask
+    if attention_mask is None:
+        batch_size, seq_len = sequence.shape
+        attention_mask = torch.ones(
+            batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=sequence.device
+        )
+
+    # Forward pass
+    outputs = model(input_ids=sequence, attention_mask=attention_mask)
+    logits = outputs.logits
+
+    # Only sample at currently masked positions
+    if current_mask.any():
+        masked_logits = logits[current_mask]
+
+        # Apply temperature scaling
+        if temperature > 0:
+            scaled_logits = masked_logits / temperature
+        else:
+            scaled_logits = masked_logits
+
+        # Suppress mask token in outputs
+        scaled_logits[:, mask_token_id] = -float("inf")
+
+        if temperature > 0:
+            # Add Gumbel noise for sampling
+            gumbel_noise = -torch.log(
+                -torch.log(torch.rand_like(scaled_logits, dtype=torch.float32))
+            )
+            gumbel_logits = scaled_logits + gumbel_noise
+            predicted_tokens = torch.argmax(gumbel_logits, dim=-1)
+        else:
+            predicted_tokens = torch.argmax(scaled_logits, dim=-1)
+
+        # Calculate probabilities for confidence scoring
+        probs = torch.softmax(scaled_logits, dim=-1)
+        predicted_token_probs = probs[range(len(predicted_tokens)), predicted_tokens]
+
+        # Determine how many tokens to unmask this step
+        remaining_masked = current_mask.sum().item()
+        if step == num_diffusion_steps - 1:
+            num_to_unmask = remaining_masked
+        else:
+            unmask_ratio = 1.0 / (num_diffusion_steps - step)
+            num_to_unmask = max(1, int(remaining_masked * unmask_ratio))
+
+        # Select highest confidence predictions to unmask
+        if num_to_unmask >= remaining_masked:
+            sequence[current_mask] = predicted_tokens
+        else:
+            _, top_indices = predicted_token_probs.topk(num_to_unmask)
+            mask_positions = torch.where(current_mask)[1]
+            positions_to_unmask = mask_positions[top_indices]
+            sequence[0, positions_to_unmask] = predicted_tokens[top_indices]
+
+    return sequence

src/axolotl/integrations/diffusion/plugin.py

@@ -0,0 +1,41 @@
+"""Diffusion LM training plugin for Axolotl."""
+
+from peft import PeftModel
+from transformers import PreTrainedModel
+
+from axolotl.integrations.base import BasePlugin
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
+
+from .trainer import DiffusionTrainer
+
+LOG = get_logger(__name__)
+
+
+class DiffusionPlugin(BasePlugin):
+    """
+    Plugin for diffusion language model training.
+
+    This plugin enables diffusion-based training using the LLaDA approach, which uses
+    random masking and bidirectional attention to train language models.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.cfg = None
+
+    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."""
+        self.cfg = cfg
+
+    def get_trainer_cls(self, cfg: DictDefault) -> type[DiffusionTrainer] | None:
+        """Return custom trainer class for diffusion training."""
+        return DiffusionTrainer
+
+    def post_trainer_create(self, cfg: DictDefault, trainer: DiffusionTrainer):
+        """Configure trainer after creation."""
+        trainer.set_config(cfg)

src/axolotl/integrations/diffusion/trainer.py

@@ -0,0 +1,301 @@
+"""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
+from .utils import create_bidirectional_attention_mask
+
+LOG = get_logger(__name__)
+
+
+class DiffusionTrainer(AxolotlTrainer):
+    """Custom trainer for diffusion LM training that overrides loss computation."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.cfg = None
+        self._special_token_ids = None
+
+    def set_config(self, config: DictDefault):
+        """Set config for diffusion training."""
+        self.cfg = config
+        self._cache_special_token_ids()
+        self._resolve_mask_token_id()
+
+        token_id = int(getattr(self.cfg.diffusion, "mask_token_id", 0))
+        LOG.info(f"Diffusion: using mask_token_id={token_id}")
+
+        if getattr(config.diffusion, "generate_samples", True):
+            generation_callback = DiffusionGenerationCallback(self)
+            self.add_callback(generation_callback)
+
+    def _resolve_mask_token_id(self) -> None:
+        """Ensure mask_token_id is valid for the current tokenizer."""
+        from .utils import resolve_mask_token_id
+
+        tokenizer = getattr(self, "processing_class", None)
+        if tokenizer is None:
+            return
+
+        mid = resolve_mask_token_id(
+            tokenizer,
+            self.cfg,
+            allow_add=True,
+            model=getattr(self, "model", None),
+        )
+        try:
+            self.cfg.diffusion.mask_token_id = int(mid)
+        except Exception:
+            pass
+
+    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
+
+    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)
+        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 = int(self.cfg.diffusion.mask_token_id)
+        mask_value = torch.full_like(input_ids, mask_token_id)
+        noisy_batch = torch.where(masked_indices, mask_value, input_ids)
+
+        return noisy_batch, masked_indices, p_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.
+        """
+        # Short-circuit empty sequences
+        if input_ids is None or input_ids.numel() == 0 or input_ids.shape[1] == 0:
+            zero = torch.tensor(
+                0.0,
+                device=(input_ids.device if input_ids is not None else None),
+                requires_grad=True,
+            )
+            return zero, {}
+
+        # If an attention_mask is provided and all positions are padding for every
+        # sample in this batch, skip the step.
+        if attention_mask is not None:
+            if attention_mask.dim() == 2 and (attention_mask.sum(dim=1) == 0).all():
+                zero = torch.tensor(0.0, device=input_ids.device, requires_grad=True)
+                return zero, {}
+
+        # Apply forward process
+        noisy_batch, masked_indices, p_mask = self._forward_process(
+            input_ids, attention_mask, labels, self.cfg.diffusion.eps
+        )
+
+        # Create bidirectional attention mask
+        bidirectional_mask = create_bidirectional_attention_mask(
+            input_ids, attention_mask, sample_packing=self.cfg.sample_packing
+        )
+
+        # Forward pass
+        outputs = model(
+            input_ids=noisy_batch.long(),
+            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.cfg.diffusion.importance_weighting:
+                masked_p_mask = masked_p_mask.float()
+                weighted_loss = token_loss / masked_p_mask
+            else:
+                weighted_loss = token_loss
+
+            if labels is not None:
+                # For SFT data: normalize by answer token count 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
+                batch_size = input_ids.shape[0]
+                loss_per_sample = torch.zeros(batch_size, device=input_ids.device)
+                for i in range(batch_size):
+                    sample_mask = masked_batch_indices == i
+                    if sample_mask.sum() > 0:
+                        sample_loss = weighted_loss[sample_mask].sum()
+                        denom = answer_lengths[i].clamp(min=1.0)
+                        loss_per_sample[i] = sample_loss / denom
+
+                loss = loss_per_sample.mean()
+            else:
+                # Non-SFT: when importance weighting is enabled, use unbiased estimator
+                # (sum(loss/p) / total_tokens). Otherwise, average over masked tokens
+                # for stable scaling across varying mask ratios.
+                if self.cfg.diffusion.importance_weighting:
+                    loss = weighted_loss.sum() / (
+                        input_ids.shape[0] * input_ids.shape[1]
+                    )
+                else:
+                    loss = weighted_loss.mean()
+
+            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)
+
+        avg_p_mask = (
+            p_mask[masked_indices].mean().item() if masked_indices.any() else 0.0
+        )
+        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": avg_p_mask,
+            "ce_loss": ce_loss.item(),
+        }
+
+        # If doing SFT training, log answer-specific metrics
+        if self.cfg.datasets is not None:
+            with torch.no_grad():
+                answer_mask = labels != -100
+                answer_lengths = answer_mask.sum(dim=1).float()  # type: ignore
+                total_answer_tokens = answer_mask.sum().item()  # type: ignore
+                total_tokens = labels.numel()  # type: ignore
+                metrics["answer_ratio"] = total_answer_tokens / max(total_tokens, 1)
+                metrics["avg_answer_length"] = answer_lengths.mean().item()
+
+        if self.cfg.diffusion.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 +1,159 @@
+"""Shared utilities for diffusion integration."""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+import torch
+
+from axolotl.utils.dict import DictDefault
+
+
+def resolve_mask_token_id(
+    tokenizer: Any,
+    cfg: DictDefault,
+    *,
+    allow_add: bool,
+    model: Any | None = None,
+    default_token: str = "<|diffusion_mask|>",
+) -> int:
+    """Resolve mask token id. Training may add a new special token; inference won't."""
+    # Determine vocab size if available
+    vocab_size = None
+    if tokenizer is not None:
+        if hasattr(tokenizer, "vocab_size") and tokenizer.vocab_size is not None:
+            try:
+                vocab_size = int(tokenizer.vocab_size)  # type: ignore[arg-type]
+            except Exception:
+                vocab_size = None
+        elif hasattr(tokenizer, "__len__"):
+            try:
+                vocab_size = int(len(tokenizer))
+            except Exception:
+                vocab_size = None
+
+    # Use explicit id from config if provided
+    diffusion_cfg = getattr(cfg, "diffusion", None)
+    # Fallback to top-level attr names only if nested missing (shouldn't happen)
+    cfg_id = (
+        getattr(diffusion_cfg, "mask_token_id", None)
+        if diffusion_cfg is not None
+        else getattr(cfg, "diffusion_mask_token_id", None)
+    )
+    if isinstance(cfg_id, int) and cfg_id >= 0:
+        if vocab_size is None or cfg_id < vocab_size:
+            return int(cfg_id)
+
+    def _existing_special_token_id(token_str: str | None) -> int | None:
+        """Attempt to resolve an existing special token string to a real ID."""
+        if not token_str or not hasattr(tokenizer, "convert_tokens_to_ids"):
+            return None
+        try:
+            token_id = tokenizer.convert_tokens_to_ids(token_str)
+        except Exception:
+            return None
+
+        if not isinstance(token_id, int) or token_id < 0:
+            return None
+
+        # Ensure it's registered as special and not UNK, and within vocab
+        unk_id = getattr(tokenizer, "unk_token_id", None)
+        specials = set(getattr(tokenizer, "all_special_tokens", []) or [])
+        addl = set(getattr(tokenizer, "additional_special_tokens", []) or [])
+        is_special = token_str in specials or token_str in addl
+        in_vocab = vocab_size is None or token_id < vocab_size
+        if (
+            (unk_id is not None and token_id == unk_id)
+            or not is_special
+            or not in_vocab
+        ):
+            return None
+        return token_id
+
+    # Try mask token string if provided
+    token_str = (
+        getattr(diffusion_cfg, "mask_token_str", None)
+        if diffusion_cfg is not None
+        else getattr(cfg, "diffusion_mask_token_str", None)
+    )
+    for candidate in (token_str, default_token):
+        token_id = _existing_special_token_id(candidate)
+        if isinstance(token_id, int):
+            try:
+                if diffusion_cfg is None:
+                    cfg.diffusion_mask_token_id = int(token_id)  # legacy fallback
+                else:
+                    diffusion_cfg.mask_token_id = int(token_id)
+            except Exception:
+                pass
+            return int(token_id)
+
+    # Optionally add and return a dedicated special token during training
+    if allow_add and hasattr(tokenizer, "add_special_tokens"):
+        token_to_add = token_str or default_token
+        try:
+            tokenizer.add_special_tokens({"additional_special_tokens": [token_to_add]})
+
+            # Resize embeddings if possible
+            if (
+                model is not None
+                and hasattr(tokenizer, "__len__")
+                and hasattr(model, "resize_token_embeddings")
+            ):
+                try:
+                    model.resize_token_embeddings(len(tokenizer))
+                except Exception:
+                    pass
+            new_id = tokenizer.convert_tokens_to_ids(token_to_add)
+            if isinstance(new_id, int) and new_id >= 0:
+                try:
+                    if diffusion_cfg is None:
+                        cfg.diffusion_mask_token_id = int(new_id)  # legacy fallback
+                    else:
+                        diffusion_cfg.mask_token_id = int(new_id)
+                except Exception:
+                    pass
+                return int(new_id)
+        except Exception:
+            pass
+
+    # Fallback to unk or 0 (do not update cfg)
+    fallback = getattr(tokenizer, "unk_token_id", 0) or 0
+    return int(fallback)
+
+
+def create_bidirectional_attention_mask(
+    input_ids: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    sample_packing: bool = False,
+) -> 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]
+        sample_packing: Whether sample packing is enabled
+
+    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 sample_packing:
+        return torch.ones(
+            batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=device
+        )
+
+    # Handle sample packing: tokens can only attend within their sample
+    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]
+    return bidirectional_mask.unsqueeze(1)

src/axolotl/loaders/adapter.py

@@ -14,6 +14,7 @@ from peft import (
     PeftConfig,
     PeftMixedModel,
     PeftModel,
+    TaskType,
     get_peft_model,
 )
 from transformers import PreTrainedModel
@@ -101,6 +102,15 @@ def load_lora(
     if cfg.peft_trainable_token_indices:
         lora_config_kwargs["trainable_token_indices"] = cfg.peft_trainable_token_indices
 
+    # Determine the correct PEFT task type
+    model_cls = type(model).__name__
+    if "SequenceClassification" in model_cls:
+        task_type = TaskType.SEQ_CLS
+    elif "TokenClassification" in model_cls:
+        task_type = TaskType.TOKEN_CLS
+    else:
+        task_type = TaskType.CAUSAL_LM
+
     lora_config = LoraConfig(
         r=cfg.lora_r,
         lora_alpha=cfg.lora_alpha,
@@ -112,7 +122,7 @@ def load_lora(
         fan_in_fan_out=cfg.lora_fan_in_fan_out,
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
         bias="none",
-        task_type="CAUSAL_LM",
+        task_type=task_type,
         **lora_config_kwargs,
     )
 

src/axolotl/loaders/model.py

@@ -673,6 +673,33 @@ class ModelLoader:
 
         return hf_ds_cfg
 
+    def _load_model_from_config(self, model_loader_class=None) -> PreTrainedModel:
+        """
+        Load model with random initialization using from_config.
+
+        Uses the selected loader when provided; otherwise falls back to the auto loader.
+        """
+        loader = model_loader_class or self.auto_model_loader
+        if loader in [AutoModelForCausalLM, AutoModelForVision2Seq]:
+            model = loader.from_config(
+                config=self.model_config,
+                trust_remote_code=self.cfg.trust_remote_code or False,
+            )
+        else:
+            model = loader(config=self.model_config)
+
+        return model
+
+    def _load_model_from_pretrained(self, model_loader_class=None) -> PreTrainedModel:
+        """Load model from pretrained weights."""
+        loader = model_loader_class or self.auto_model_loader
+        kwargs = {
+            "config": self.model_config,
+            "trust_remote_code": self.cfg.trust_remote_code or False,
+            **self.model_kwargs,
+        }
+        return loader.from_pretrained(self.base_model, **kwargs)
+
     def _build_model(self) -> bool:
         """Load model, with load strategy depending on config."""
         skip_move_to_device = False
@@ -687,7 +714,8 @@ class ModelLoader:
         if self.is_fsdp_enabled:
             if self.cfg.fsdp_config.cpu_ram_efficient_loading:
                 skip_move_to_device = True
-                # Don't delete device_map for QLoRA + FSDP - it was set correctly in _set_device_map
+                # Don't delete device_map for QLoRA + FSDP - it was set correctly in
+                # _set_device_map
                 if (
                     "device_map" in self.model_kwargs
                     and not self.is_qlora_and_fsdp_enabled
@@ -716,6 +744,11 @@ class ModelLoader:
                 or self.cfg.qlora_sharded_model_loading
             )
         ):
+            if self.cfg.reinit_weights:
+                LOG.warning(
+                    "reinit_weights is not supported with sharded quantized loading. "
+                    "Loading from pretrained weights instead."
+                )
             quant_storage = self.cfg.torch_dtype
             quantization_config = getattr(
                 self.model_config, "quantization_config", None
@@ -731,33 +764,12 @@ class ModelLoader:
                 quantization_config=quantization_config,
             )
             skip_move_to_device = True
-        elif (
-            self.model_config.model_type in ["llama", "llama4"]
-            and not self.cfg.trust_remote_code
-            and not self.cfg.gptq
-        ):
-            # Please don't remove underscore binding without reading the fn docstring.
-            _ = self._configure_zero3_memory_efficient_loading()
-
-            # Load model with random initialization if specified
-            if self.cfg.random_init_weights:
-                # AutoModel classes support the from_config method
-                if self.auto_model_loader in [
-                    AutoModelForCausalLM,
-                    AutoModelForVision2Seq,
-                ]:
-                    self.model = self.auto_model_loader.from_config(
-                        config=self.model_config,
-                    )
-                else:
-                    self.model = self.auto_model_loader(config=self.model_config)
-            else:
-                self.model = self.auto_model_loader.from_pretrained(
-                    self.base_model,
-                    config=self.model_config,
-                    **self.model_kwargs,
-                )
         elif self.model_type == "MambaLMHeadModel":
+            if self.cfg.reinit_weights:
+                LOG.warning(
+                    "reinit_weights is not supported with MambaLMHeadModel. "
+                    "Loading from pretrained weights instead."
+                )
             # FIXME this is janky at best and hacked together to make it work
             MambaLMHeadModel = fix_mamba_attn_for_loss()
 
@@ -770,41 +782,27 @@ class ModelLoader:
                 self.base_model,
                 **self.model_kwargs,
             )
-        elif (
-            self.model_type
-            and self.model_type != "AutoModelForCausalLM"
-            and not self.cfg.trust_remote_code
-        ):
-            if self.cfg.gptq:
-                self.model = self.auto_model_loader.from_pretrained(
-                    self.base_model,
-                    config=self.model_config,
-                    trust_remote_code=self.cfg.trust_remote_code or False,
-                    **self.model_kwargs,
-                )
-            else:
-                self.model = getattr(transformers, self.model_type).from_pretrained(
-                    self.base_model,
-                    config=self.model_config,
-                    trust_remote_code=self.cfg.trust_remote_code or False,
-                    **self.model_kwargs,
-                )
-        elif self.cfg.gptq:
-            self.model = self.auto_model_loader.from_pretrained(
-                self.base_model,
-                config=self.model_config,
-                trust_remote_code=self.cfg.trust_remote_code or False,
-                **self.model_kwargs,
-            )
         else:
-            # Please don't remove underscore binding without reading the fn docstring.
+            # Please don't remove underscore binding without reading the fn docstring
             _ = self._configure_zero3_memory_efficient_loading()
-            self.model = self.auto_model_loader.from_pretrained(
-                self.base_model,
-                config=self.model_config,
-                trust_remote_code=self.cfg.trust_remote_code or False,
-                **self.model_kwargs,
-            )
+
+            if (
+                self.model_type
+                and self.model_type != "AutoModelForCausalLM"
+                and not self.cfg.trust_remote_code
+                and not self.cfg.gptq
+            ):
+                # Use model type from transformers
+                model_loader_class = getattr(transformers, self.model_type)
+            else:
+                # Use auto model loader (handles gptq and default cases)
+                model_loader_class = self.auto_model_loader
+
+            if self.cfg.reinit_weights:
+                self.model = self._load_model_from_config(model_loader_class)
+            else:
+                self.model = self._load_model_from_pretrained(model_loader_class)
+
         if is_deepspeed_zero3_enabled():
             skip_move_to_device = True
 

src/axolotl/loaders/patch_manager.py

@@ -3,8 +3,8 @@
 Applies pre- and post-model load patches for various fixes and optimizations.
 """
 
-import os
 import importlib.util
+import os
 from functools import cached_property
 
 import addict
@@ -468,9 +468,10 @@ class PatchManager:
 
     def _apply_patch_deepspeed_zero3(self):
         try:
-            from axolotl.monkeypatch.deepspeed_utils import apply_deepspeed_patches
             from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
+            from axolotl.monkeypatch.deepspeed_utils import apply_deepspeed_patches
+
             if self.cfg.activation_offloading is True and (
                 is_deepspeed_zero3_enabled()
                 or os.getenv("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3"

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -160,9 +160,11 @@ def get_state_dict(self, model, unwrap=True):
                 state_dict[param_name] = param.cpu()
             torch.distributed.barrier()
     elif self.distributed_type == DistributedType.FSDP:
-        from torch.distributed.fsdp import FullStateDictConfig
-        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-        from torch.distributed.fsdp import StateDictType
+        from torch.distributed.fsdp import (
+            FullStateDictConfig,
+            FullyShardedDataParallel as FSDP,
+            StateDictType,
+        )
 
         full_state_dict_config = FullStateDictConfig(
             offload_to_cpu=True, rank0_only=True

src/axolotl/monkeypatch/attention/flex_attn.py

@@ -1,11 +1,12 @@
 """Flex attention monkey patch"""
 
 import sys
-from packaging import version
 
 import torch
 import transformers
+from packaging import version
 from transformers.utils.import_utils import _torch_version, is_torch_less_or_equal
+
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)

src/axolotl/monkeypatch/deepspeed_utils.py

@@ -1,5 +1,6 @@
 import importlib
 import importlib.util
+
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)

src/axolotl/utils/config/__init__.py

@@ -17,8 +17,8 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+    AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
 from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
 
 LOG = get_logger(__name__)

src/axolotl/utils/data/__init__.py

@@ -1,14 +1,14 @@
 """Init for `axolotl.utils.data` module."""
 
-from axolotl.utils.data.streaming import (
-    encode_streaming,
-    wrap_streaming_dataset,
-)
 from axolotl.utils.data.rl import prepare_preference_datasets
 from axolotl.utils.data.sft import (
     get_dataset_wrapper,
     prepare_datasets,
 )
+from axolotl.utils.data.streaming import (
+    encode_streaming,
+    wrap_streaming_dataset,
+)
 from axolotl.utils.data.utils import md5
 
 __all__ = [

src/axolotl/utils/data/sft.py

@@ -16,7 +16,6 @@ from transformers import PreTrainedTokenizer, ProcessorMixin
 
 from axolotl.prompters import Prompter
 from axolotl.utils.data.lock import FileLockLoader
-from axolotl.utils.data.streaming import wrap_streaming_dataset
 from axolotl.utils.data.shared import (
     create_train_validation_split,
     datasets_with_name_generator,
@@ -27,6 +26,7 @@ from axolotl.utils.data.shared import (
     save_preprocessed_dataset,
     try_load_from_hub,
 )
+from axolotl.utils.data.streaming import wrap_streaming_dataset
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
     handle_long_seq_in_dataset,

src/axolotl/utils/environment.py

@@ -6,8 +6,6 @@ from importlib.metadata import version
 
 from accelerate.utils.environment import (
     check_cuda_p2p_ib_support as accelerate_check_cuda_p2p_ib_support,
-)
-from accelerate.utils.environment import (
     get_gpu_info,
 )
 from packaging.version import Version, parse

src/axolotl/utils/schemas/config.py

@@ -106,6 +106,12 @@ class AxolotlInputConfig(
             "description": "Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs"
         },
     )
+    reinit_weights: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Reinitialize model weights randomly instead of loading pretrained weights"
+        },
+    )
 
     trainer_cls: str | None = Field(
         default=None,

src/axolotl/utils/schemas/validation.py

@@ -14,7 +14,6 @@ from transformers.utils.import_utils import is_torch_npu_available
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import ChatTemplate, RingAttnFunc, RLType
 
-
 LOG = get_logger(__name__)
 
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}

tests/e2e/test_diffusion.py

@@ -0,0 +1,139 @@
+"""E2E smoke test for diffusion training plugin."""
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_model_output_exists
+
+
+class TestDiffusion:
+    """Test case for diffusion training plugin."""
+
+    def test_diffusion_smoke_test(self, temp_dir):
+        """
+        Smoke test for diffusion training to ensure the plugin loads and trains without
+        error.
+        """
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
+                "trust_remote_code": True,
+                "sequence_len": 256,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 3,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "bf16": True,
+                "save_safetensors": True,
+                "save_first_step": False,
+                "logging_steps": 1,
+                "eval_steps": 3,
+                # Diffusion-specific config
+                "plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
+                "diffusion": {
+                    # sample generation
+                    "generate_samples": True,
+                    "generation_interval": 1,
+                    "num_generation_samples": 1,
+                    "generation_steps": 2,
+                    "generation_max_length": 32,
+                    "generation_temperature": 0.0,
+                    # training-specific
+                    "mask_token_id": 16,
+                    "eps": 1e-3,
+                    "importance_weighting": False,
+                },
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+    def test_diffusion_sft_labels(self, temp_dir):
+        """Test that diffusion training properly handles SFT data with labels."""
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
+                "trust_remote_code": True,
+                "sequence_len": 256,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 3,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "bf16": True,
+                "save_safetensors": True,
+                "save_first_step": False,
+                "logging_steps": 1,
+                "eval_steps": 2,
+                # Diffusion-specific config
+                "plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
+                "diffusion": {
+                    # sample generation
+                    "generate_samples": True,
+                    "generation_interval": 1,
+                    "num_generation_samples": 1,
+                    "generation_steps": 2,
+                    "generation_max_length": 32,
+                    "generation_temperature": 0.0,
+                    # training-specific
+                    "mask_token_id": 16,
+                    "eps": 1e-3,
+                    "importance_weighting": True,
+                },
+                # Ensure we have proper SFT labels
+                "train_on_inputs": False,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        # Verify that the dataset has labels
+        sample = dataset_meta.train_dataset[0]
+        assert "labels" in sample, "SFT dataset should have labels"
+
+        # Check that some labels are -100 (prompt tokens)
+        labels = sample["labels"]
+        if hasattr(labels, "tolist"):
+            labels = labels.tolist()
+        assert -100 in labels, "SFT dataset should have -100 labels for prompt tokens"
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)

tests/integrations/test_diffusion.py

@@ -0,0 +1,274 @@
+"""Tests for diffusion trainer integration."""
+
+# pylint: disable=redefined-outer-name,protected-access
+
+from unittest.mock import Mock
+
+import pytest
+import torch
+
+from axolotl.integrations.diffusion import DiffusionTrainer
+from axolotl.integrations.diffusion.utils import create_bidirectional_attention_mask
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture
+def mock_tokenizer():
+    """Create a mock tokenizer."""
+    tokenizer = Mock()
+    tokenizer.bos_token_id = 1
+    tokenizer.eos_token_id = 2
+    tokenizer.pad_token_id = 0
+    return tokenizer
+
+
+@pytest.fixture
+def diffusion_config():
+    """Create a diffusion config."""
+    return DictDefault(
+        {
+            "diffusion": {
+                "mask_token_id": 32000,
+                "eps": 1e-3,
+                "importance_weighting": False,
+            },
+            "sample_packing": False,
+        }
+    )
+
+
+@pytest.fixture
+def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
+    """Create a diffusion trainer instance for testing methods directly."""
+    # Create a minimal trainer instance just for testing methods
+    trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
+    trainer.cfg = diffusion_config
+    trainer._special_token_ids = {0, 1, 2}  # pad, bos, eos
+    trainer.processing_class = mock_tokenizer
+    trainer.store_metrics = Mock()  # Mock metrics storage
+    return trainer
+
+
+class TestDiffusionTrainer:
+    """Test the DiffusionTrainer class."""
+
+    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_trainer_instance._forward_process(input_ids, 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 special tokens are not masked
+        special_token_positions = (input_ids == 1) | (input_ids == 2) | (input_ids == 0)
+        assert not masked_indices[special_token_positions].any()
+
+        # Check that mask token is applied
+        mask_token_id = diffusion_trainer_instance.cfg.diffusion.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)
+
+        noisy_batch, masked_indices, p_mask = (
+            diffusion_trainer_instance._forward_process(
+                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),
+        # 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)
+
+        _, masked_indices, p_mask = diffusion_trainer_instance._forward_process(
+            input_ids, attention_mask=attention_mask, eps=0.1
+        )
+
+        # Check that padding tokens are not masked
+        padding_positions = attention_mask == 0
+        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):
+        """Test bidirectional attention mask without sample packing."""
+        input_ids = torch.tensor([[1, 10, 20, 2]], dtype=torch.long)
+
+        mask = create_bidirectional_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(
+        self, diffusion_trainer_instance
+    ):
+        """Test bidirectional attention mask with sample packing."""
+        diffusion_trainer_instance.cfg.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 = create_bidirectional_attention_mask(
+            input_ids, attention_mask, sample_packing=True
+        )
+
+        # Check that tokens within same sample can attend to each other
+        # but not across samples
+        assert mask[0, 0, 0, 1].item()  # First sample tokens can attend to each other
+        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):
+        """Test basic loss computation."""
+        # Mock model that returns logits
+        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)
+
+        loss, outputs = diffusion_trainer_instance._compute_diffusion_loss(
+            mock_model, input_ids
+        )
+
+        # Check that loss is computed
+        assert isinstance(loss, torch.Tensor)
+        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_sft(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
+        diffusion_trainer_instance.cfg.datasets = Mock()
+
+        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
+        labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
+
+        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
+            mock_model, input_ids, labels=labels
+        )
+
+        # Check that loss is computed
+        assert isinstance(loss, torch.Tensor)
+        assert loss.requires_grad
+
+        # 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."""
+        # Mock model
+        mock_model = Mock()
+        mock_outputs = 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)
+        input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
+
+        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
+            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, mock_tokenizer):
+        """Test caching of special token IDs."""
+        trainer = object.__new__(DiffusionTrainer)
+        trainer.processing_class = mock_tokenizer
+        trainer._cache_special_token_ids()
+        assert trainer._special_token_ids == {0, 1, 2}
+
+    def test_cache_special_token_ids_no_tokenizer(self):
+        """Test caching when no tokenizer is available."""
+        trainer = object.__new__(DiffusionTrainer)
+        trainer.processing_class = None
+        trainer._cache_special_token_ids()
+
+        assert trainer._special_token_ids == set()
+
+    def test_main_compute_loss_interface(self, diffusion_trainer_instance):
+        """Test the main compute_loss interface."""
+        # Mock model
+        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 = {
+            "input_ids": torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.long),
+            "labels": torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long),
+        }
+
+        # Test without return_outputs
+        loss = diffusion_trainer_instance.compute_loss(mock_model, inputs)
+        assert isinstance(loss, torch.Tensor)
+
+        # 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)

tests/integrations/test_diffusion_callback.py

@@ -0,0 +1,92 @@
+"""Tests for diffusion generation callback dataloader selection and triggering."""
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+
+from axolotl.integrations.diffusion import DiffusionGenerationCallback
+
+
+class DummyTrainer:
+    """Minimal trainer double with required attributes/methods for the callback."""
+
+    def __init__(self, use_eval: bool):
+        # Config used by callback
+        self.cfg = SimpleNamespace(
+            diffusion=SimpleNamespace(
+                generation_interval=1,
+                num_generation_samples=1,
+                generation_max_length=32,
+                generation_steps=4,
+                generation_temperature=0.0,
+                mask_token_id=16,
+            ),
+            use_wandb=False,
+        )
+
+        # Model/tokenizer are passed through to generate_samples; not used here
+        self.model = Mock()
+        self.processing_class = Mock()
+
+        # Datasets and loaders
+        self.eval_dataset = object() if use_eval else None
+        self._train_loader = object()
+        self._eval_loader = object()
+
+        # State for world process check
+        self.state = SimpleNamespace(is_world_process_zero=True)
+
+        # Track which loader was requested
+        self.requested: list[str] = []
+
+    def get_train_dataloader(self):
+        self.requested.append("train")
+        return self._train_loader
+
+    def get_eval_dataloader(self):
+        self.requested.append("eval")
+        return self._eval_loader
+
+
+@pytest.mark.parametrize("use_eval", [False, True])
+def test_callback_uses_correct_dataloader(monkeypatch, use_eval):
+    trainer = DummyTrainer(use_eval=use_eval)
+    callback = DiffusionGenerationCallback(trainer)
+
+    captured = {}
+
+    # Patch generate_samples in the callback module's namespace
+    def fake_generate_samples(**kwargs):
+        captured["dataloader"] = kwargs.get("dataloader")
+        # Return one dummy sample to exercise logging path
+        return [
+            {
+                "original": "o",
+                "masked": "m",
+                "generated": "g",
+                "mask_ratio": 0.5,
+                "masked_tokens": 1,
+                "total_tokens": 2,
+            }
+        ]
+
+    monkeypatch.setattr(
+        "axolotl.integrations.diffusion.callbacks.generate_samples",
+        fake_generate_samples,
+    )
+
+    # Trigger at step 1 (interval=1)
+    args = SimpleNamespace()
+    state = SimpleNamespace(global_step=1)
+    control = SimpleNamespace()
+
+    callback.on_step_end(args=args, state=state, control=control)
+
+    # Assert the expected dataloader path was used
+    if use_eval:
+        assert trainer.requested[0] == "eval"
+        assert captured["dataloader"] is trainer._eval_loader
+    else:
+        assert trainer.requested[0] == "train"
+        assert captured["dataloader"] is trainer._train_loader

tests/test_streaming.py

@@ -5,12 +5,12 @@ from unittest.mock import Mock, patch
 
 from datasets import IterableDataset
 
-from axolotl.utils.dict import DictDefault
+from axolotl.utils.config import validate_config
 from axolotl.utils.data.sft import (
     _prepare_streaming_dataset,
     prepare_datasets,
 )
-from axolotl.utils.config import validate_config
+from axolotl.utils.dict import DictDefault
 
 
 class TestStreamingConfig(unittest.TestCase):