roundup_power2_divisions not needed with newer pytorch versions (#3540)

* roundup_power2_divisions not needed with newer pytorch versions

* remove typo

* update qwen3.5 moe 35b-a3b yaml for 5090

* more bug fixes

* fix tests to match updated trainer

* don't use fa2 for hooks test

* reset plugins on the instance

* retry download

* fix references to renamed axolotl_cfg property on trainer

* Fix ref to trainer cfg

.github/workflows/tests.yml

@@ -68,13 +68,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        python_version: ["3.12", "3.14"]
         pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
-#        exclude:
+        exclude:
-#          - python_version: "3.14"
+          - python_version: "3.14"
-#            pytorch_version: "2.8.0"
+            pytorch_version: "2.8.0"
-#          - python_version: "3.14"
+          - python_version: "3.14"
-#            pytorch_version: "2.9.1"
+            pytorch_version: "2.9.1"
     timeout-minutes: 20
 
     steps:
@@ -164,13 +164,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        python_version: ["3.12", "3.14"]
         pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
-#        exclude:
+        exclude:
-#          - python_version: "3.14"
+          - python_version: "3.14"
-#            pytorch_version: "2.8.0"
+            pytorch_version: "2.8.0"
-#          - python_version: "3.14"
+          - python_version: "3.14"
-#            pytorch_version: "2.9.1"
+            pytorch_version: "2.9.1"
     timeout-minutes: 30
 
     steps:

cicd/cicd.sh

@@ -4,7 +4,17 @@ set -e
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
 set -o pipefail
-curl --silent --show-error --fail --retry 3 --retry-delay 5 -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1
+for i in 1 2 3; do
+  if curl --silent --show-error --fail -L \
+    https://axolotl-ci.b-cdn.net/hf-cache.tar.zst \
+    | tar -xpf - -C "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1; then
+    echo "HF cache extracted successfully"
+    break
+  fi
+  echo "Attempt $i failed, cleaning up and retrying in 15s..."
+  rm -rf "${HF_HOME}/hub/"*
+  sleep 15
+done
 # hf download "NousResearch/Meta-Llama-3-8B"
 # hf download "NousResearch/Meta-Llama-3-8B-Instruct"
 # hf download "microsoft/Phi-4-reasoning"

examples/qwen3.5/35b-a3b-moe-qlora.yaml

@@ -1,8 +1,18 @@
-base_model: Qwen/Qwen3.5-35B-A3B
+base_model: Qwen/Qwen3.5-35B-A3B-Base
 
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-strict: false
+  - axolotl.integrations.kernels.KernelsPlugin
+  - axolotl.integrations.liger.LigerPlugin
+use_kernels: true
+use_scattermoe: true
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+
+torch_compile: false
 
 chat_template: qwen3_5
 datasets:
@@ -13,6 +23,7 @@ datasets:
     message_property_mappings:
       role: from
       content: value
+
 val_set_size: 0.0
 output_dir: ./outputs/out
 dataset_prepared_path: last_run_prepared
@@ -36,9 +47,13 @@ lora_target_modules:
 # lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
 
 # Target experts
-# lora_target_parameters:
+lora_target_parameters:
-#   - mlp.experts.gate_up_proj
+  - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
+  - mlp.experts.down_proj
+
+lora_qkv_kernel: true
+lora_o_kernel: true
+lora_mlp_kernel: false
 
 wandb_project:
 wandb_entity:
@@ -47,22 +62,17 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 2
-micro_batch_size: 1
+micro_batch_size: 4
 num_epochs: 1
-optimizer: adamw_torch_4bit
+optimizer: adamw_torch_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
 bf16: auto
 tf32: true
 
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
 gradient_checkpointing: true
-gradient_checkpointing_kwargs:
+activation_offloading: true
-  use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

setup.py

@@ -89,7 +89,7 @@ def parse_requirements(extras_require_map):
                 ]
                 if not install_xformers:
                     _install_requires.pop(_install_requires.index(xformers_version))
-                extras_require_map["vllm"] = ["vllm==0.17.1"]
+                extras_require_map["vllm"] = ["vllm>=0.17.1"]
             elif (major, minor) >= (2, 9):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = [

src/axolotl/cli/checks.py

@@ -4,6 +4,7 @@ import os
 from pathlib import Path
 
 import httpcore
+import httpx
 from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
@@ -48,7 +49,7 @@ def check_user_token() -> bool:
             "Error verifying HuggingFace token. Remember to log in using `hf auth login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
-    except (HTTPError, httpcore.ConnectError):
+    except (HTTPError, httpcore.ConnectError, httpx.ConnectError):
         LOG.warning(
             "Error accessing HuggingFace. This may be due to a network issue or rate limiting."
         )

src/axolotl/integrations/diffusion/callbacks.py

@@ -36,7 +36,9 @@ class DiffusionGenerationCallback(TrainerCallback):
         """Generate samples at specified intervals."""
         if (
             state.global_step > 0
-            and state.global_step % self.trainer.cfg.diffusion.generation_interval == 0
+            and state.global_step
+            % self.trainer.axolotl_cfg.diffusion.generation_interval
+            == 0
         ):
             if not self.trainer.state.is_world_process_zero:
                 return
@@ -52,7 +54,7 @@ class DiffusionGenerationCallback(TrainerCallback):
                 dataloader = self.trainer.get_train_dataloader()
 
             # Generate samples
-            diffusion_cfg = self.trainer.cfg.diffusion
+            diffusion_cfg = self.trainer.axolotl_cfg.diffusion
             samples = generate_samples(
                 model=self.trainer.model,
                 tokenizer=self.trainer.processing_class,
@@ -142,7 +144,7 @@ class DiffusionGenerationCallback(TrainerCallback):
 
         logger.info("=" * 60)
 
-        if self.trainer.cfg.use_wandb:
+        if self.trainer.axolotl_cfg.use_wandb:
             if wandb.run is not None:
                 wandb.log(
                     {

src/axolotl/integrations/diffusion/plugin.py

@@ -38,4 +38,6 @@ class DiffusionPlugin(BasePlugin):
 
     def post_trainer_create(self, cfg: DictDefault, trainer: DiffusionTrainer):
         """Configure trainer after creation."""
-        trainer.set_config(cfg)
+        if hasattr(trainer, "axolotl_cfg"):
+            trainer.axolotl_cfg = cfg
+        trainer.post_set_axolotl_cfg()

src/axolotl/integrations/diffusion/trainer.py

@@ -7,7 +7,6 @@ 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
@@ -21,19 +20,17 @@ class DiffusionTrainer(AxolotlTrainer):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.cfg = None
         self._special_token_ids = None
 
-    def set_config(self, config: DictDefault):
+    def post_set_axolotl_cfg(self):
         """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))
+        token_id = int(getattr(self.axolotl_cfg.diffusion, "mask_token_id", 0))
         LOG.info(f"Diffusion: using mask_token_id={token_id}")
 
-        if getattr(config.diffusion, "generate_samples", True):
+        if getattr(self.axolotl_cfg.diffusion, "generate_samples", True):
             generation_callback = DiffusionGenerationCallback(self)
             self.add_callback(generation_callback)
 
@@ -41,18 +38,20 @@ class DiffusionTrainer(AxolotlTrainer):
         """Ensure mask_token_id is valid for the current tokenizer."""
         from .utils import resolve_mask_token_id
 
+        assert self.axolotl_cfg is not None, "axolotl_cfg is not set yet"
+
         tokenizer = getattr(self, "processing_class", None)
         if tokenizer is None:
             return
 
         mid = resolve_mask_token_id(
             tokenizer,
-            self.cfg,
+            self.axolotl_cfg,
             allow_add=True,
             model=getattr(self, "model", None),
         )
         try:
-            self.cfg.diffusion.mask_token_id = int(mid)
+            self.axolotl_cfg.diffusion.mask_token_id = int(mid)
         except Exception:
             pass
 
@@ -150,7 +149,7 @@ class DiffusionTrainer(AxolotlTrainer):
             masked_indices = masked_indices & answer_mask
 
         # Create masked input
-        mask_token_id = int(self.cfg.diffusion.mask_token_id)
+        mask_token_id = int(self.axolotl_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)
 
@@ -194,12 +193,12 @@ class DiffusionTrainer(AxolotlTrainer):
 
         # Apply forward process
         noisy_batch, masked_indices, p_mask = self._forward_process(
-            input_ids, attention_mask, labels, self.cfg.diffusion.eps
+            input_ids, attention_mask, labels, self.axolotl_cfg.diffusion.eps
         )
 
         # Create bidirectional attention mask
         bidirectional_mask = create_bidirectional_attention_mask(
-            input_ids, attention_mask, sample_packing=self.cfg.sample_packing
+            input_ids, attention_mask, sample_packing=self.axolotl_cfg.sample_packing
         )
 
         # Forward pass
@@ -222,7 +221,7 @@ class DiffusionTrainer(AxolotlTrainer):
                 masked_logits.float(), masked_targets, reduction="none"
             )
 
-            if self.cfg.diffusion.importance_weighting:
+            if self.axolotl_cfg.diffusion.importance_weighting:
                 masked_p_mask = masked_p_mask.float()
                 weighted_loss = token_loss / masked_p_mask
             else:
@@ -251,7 +250,7 @@ class DiffusionTrainer(AxolotlTrainer):
                 # 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:
+                if self.axolotl_cfg.diffusion.importance_weighting:
                     loss = weighted_loss.sum() / (
                         input_ids.shape[0] * input_ids.shape[1]
                     )
@@ -283,7 +282,7 @@ class DiffusionTrainer(AxolotlTrainer):
         }
 
         # If doing SFT training, log answer-specific metrics
-        if self.cfg.datasets is not None:
+        if self.axolotl_cfg.datasets is not None:
             with torch.no_grad():
                 answer_mask = labels != -100
                 answer_lengths = answer_mask.sum(dim=1).float()  # type: ignore
@@ -292,7 +291,7 @@ class DiffusionTrainer(AxolotlTrainer):
                 metrics["answer_ratio"] = total_answer_tokens / max(total_tokens, 1)
                 metrics["avg_answer_length"] = answer_lengths.mean().item()
 
-        if self.cfg.diffusion.importance_weighting:
+        if self.axolotl_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"

src/axolotl/utils/__init__.py

@@ -48,7 +48,8 @@ def set_pytorch_cuda_alloc_conf():
     """Set up CUDA allocation config"""
     torch_version = torch.__version__.split(".")
     torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
-    config_value = "expandable_segments:True,roundup_power2_divisions:16"
+    config_value = "expandable_segments:True"
+    config_older_suffix = ",roundup_power2_divisions:16"
     if (
         torch_major == 2
         and torch_minor >= 9
@@ -60,7 +61,7 @@ def set_pytorch_cuda_alloc_conf():
         and torch_minor >= 2
         and os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None
     ):
-        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = config_value
+        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = config_value + config_older_suffix
 
 
 def set_misc_env():

tests/conftest.py

@@ -1,5 +1,6 @@
 """Shared pytest fixtures"""
 
+import collections
 import functools
 import importlib
 import logging
@@ -473,6 +474,18 @@ def temp_dir() -> Generator[str, None, None]:
     shutil.rmtree(_temp_dir)
 
 
+@pytest.fixture(scope="function", autouse=True)
+def reset_plugin_manager():
+    from axolotl.integrations.base import PluginManager
+
+    yield
+    PluginManager._cfg = None
+    # Don't reset _instance to None — module-level PLUGIN_MANAGER references
+    # in train.py, model.py, etc. would become stale
+    if PluginManager._instance is not None:
+        PluginManager._instance.plugins = collections.OrderedDict()
+
+
 @pytest.fixture(scope="function", autouse=True)
 def torch_manual_seed():
     torch.manual_seed(42)

tests/e2e/test_diffusion.py

@@ -2,7 +2,7 @@
 
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
 from tests.e2e.utils import check_model_output_exists
@@ -62,6 +62,7 @@ class TestDiffusion:
             }
         )
 
+        prepare_plugins(cfg)
         cfg = validate_config(cfg)
         normalize_config(cfg)
         dataset_meta = load_datasets(cfg=cfg)
@@ -119,6 +120,7 @@ class TestDiffusion:
             }
         )
 
+        prepare_plugins(cfg)
         cfg = validate_config(cfg)
         normalize_config(cfg)
         dataset_meta = load_datasets(cfg=cfg)

tests/integrations/test_diffusion.py

@@ -42,7 +42,7 @@ 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.axolotl_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
@@ -70,7 +70,7 @@ class TestDiffusionTrainer:
         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
+        mask_token_id = diffusion_trainer_instance.axolotl_cfg.diffusion.mask_token_id
         masked_positions = masked_indices
         if masked_positions.any():
             assert (noisy_batch[masked_positions] == mask_token_id).all()
@@ -132,7 +132,7 @@ class TestDiffusionTrainer:
         self, diffusion_trainer_instance
     ):
         """Test bidirectional attention mask with sample packing."""
-        diffusion_trainer_instance.cfg.sample_packing = True
+        diffusion_trainer_instance.axolotl_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)
@@ -184,7 +184,7 @@ class TestDiffusionTrainer:
         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()
+        diffusion_trainer_instance.axolotl_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)

tests/integrations/test_diffusion_callback.py

@@ -13,7 +13,7 @@ class DummyTrainer:
 
     def __init__(self, use_eval: bool):
         # Config used by callback
-        self.cfg = SimpleNamespace(
+        self.axolotl_cfg = SimpleNamespace(
             diffusion=SimpleNamespace(
                 generation_interval=1,
                 num_generation_samples=1,

tests/integrations/test_swanlab.py

@@ -1176,7 +1176,7 @@ class TestSwanLabProfiling:
 
         # Mock trainer with SwanLab enabled
         mock_trainer = MagicMock()
-        mock_trainer.cfg = MagicMock(use_swanlab=True)
+        mock_trainer.axolotl_cfg = MagicMock(use_swanlab=True)
         mock_trainer.__class__.__name__ = "TestTrainer"
 
         with patch("swanlab.get_run") as mock_get_run, patch("swanlab.log") as mock_log:
@@ -1199,7 +1199,7 @@ class TestSwanLabProfiling:
         from axolotl.integrations.swanlab.profiling import swanlab_profiling_context
 
         mock_trainer = MagicMock()
-        mock_trainer.cfg = MagicMock(use_swanlab=False)  # Disabled
+        mock_trainer.axolotl_cfg = MagicMock(use_swanlab=False)  # Disabled
 
         with patch("swanlab.log") as mock_log:
             with swanlab_profiling_context(mock_trainer, "test_function"):
@@ -1213,7 +1213,7 @@ class TestSwanLabProfiling:
         from axolotl.integrations.swanlab.profiling import swanlab_profiling_context
 
         mock_trainer = MagicMock()
-        mock_trainer.cfg = MagicMock(use_swanlab=True)
+        mock_trainer.axolotl_cfg = MagicMock(use_swanlab=True)
 
         with (
             patch("swanlab.get_run", return_value=None),
@@ -1294,7 +1294,7 @@ class TestSwanLabProfiling:
         )
 
         mock_trainer = MagicMock()
-        mock_trainer.cfg = MagicMock(use_swanlab=True)
+        mock_trainer.axolotl_cfg = MagicMock(use_swanlab=True)
         mock_trainer.__class__.__name__ = "TestTrainer"
 
         # Config that filters out very fast operations
@@ -1320,7 +1320,7 @@ class TestSwanLabProfiling:
         from axolotl.integrations.swanlab.profiling import swanlab_profiling_context
 
         mock_trainer = MagicMock()
-        mock_trainer.cfg = MagicMock(use_swanlab=True)
+        mock_trainer.axolotl_cfg = MagicMock(use_swanlab=True)
         mock_trainer.__class__.__name__ = "TestTrainer"
 
         with patch("swanlab.get_run") as mock_get_run, patch("swanlab.log") as mock_log: